Contents

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

LAB IN COGNITION AND PERCEPTION — Lab in C&P (Fall2022)
Contents

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

Contents

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

Contents

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

Contents

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

Contents

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

Contents

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

Contents

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

Contents

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

Contents

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.

LAB IN COGNITION AND PERCEPTION — Lab in C&P (Fall2022)
Contents

LAB IN COGNITION AND PERCEPTION

LAB IN COGNITION AND PERCEPTION#

PSYCH-UA.46 - Fall 2022#

Instructors#

Professor: Todd Gureckis, PhD
Email: todd.gureckis@nyu.edu
Office Hours: Wednesday 4-5pm (or by appointment)
Office: the Interweb

Teaching Assistant: Richard He
Email: qh2193@nyu.edu
Office Hours: Thursday 4-5 pm (or by appointment)

Place and Time#

Silver room 509, will try to record via zoom for those that miss
M&W: 11-12:15

Course Overview#

This course provides hands-on experience with the tools and practices used in psychological research. Students learn how to use a computer to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.

Class Website#

http://teaching.gureckislab.org/fall22/labincp

Credits and Degree Progress#

This class is worth four credit hours and applies to the lab requirement for the psychology major.

Textbook#

You are looking at it! You will refer to this website for reading, lecture slides, videos, and other exercises. Sometimes links will be provided to other websites and videos.

Prerequisites#

PSYCH-UA 10 (Statistical reasoning for the behavioral sciences), PSYCH-UA 11 (Adavance psychological statistics) and one of the following: PSYCH-UA 22 (Perception), PSYCH-UA 25 (Cognitive Neuroscience), or PSYCH-UA 29 (COGNITION). Permission of the instructor may be obtained in certain cases.

Computing Environment#

The course will rely on a online computing environment called Jupyter. Each student will have their own JupyterHub instance and can connect to this from a variety of devices over the internet. The address of the class JupyterHub is https://psychua-46-fall.rcnyu.org. You will log in with your normal NYUHome credentials.

Communication#

We will use EdStem to facilitate discussion outside of class. You can post questions about any of the course content and the TA or instructor will try their best to respond quickly. By asking your question on the public forum you can learn from the questions and answers of your fellow students.

Schedule#

Each class will be divided into several parts. Sometimes there will be a short lecture, but often we will try to provide videos in advance to watch (a “flipped class”). In class will focus on discussion, student presentations, group work, and practical exercises. A link to the current schedule can be found here, but it may be updated as we go.

Assignments#

The grade in the course will be determined by the following categories:

Class Participation (10% of grade)#

Attendance and participation in lectures and labs is essential given the hands on nature of the course. Each class period we will record attendance based on being logged into Zoom and participation and attendance will be counted. Two “free”/”no excuse” absences are automatically granted. You do not need to tell me the reason but connect with other students or the TA to find out what you missed. Otherwise, please get in touch in advance if you expect to miss class.

Quizzes (10% of grade)#

Short quizzes over the assigned reading and videos will be assigned in class at the start of each lecture.

Presentation (10% of grade)#

Students will be assigned to working group through the entire semester. Each group will present a summary of the reading/chapter assigned for that class. Presentations involve a few slides describing the overall content of the class, and a list of questions that your group had about the read that we can discuss as a group.

Homework (15% of grade)#

We will have semi-regular homeworks (at least once a week or once every other week). These should not be too long or onerous and should let you develop your skill and practice material covered in class. Often you will complete these assignments by filling out a webform or by completing some exercises on the class Jupyterhub instance and submitting your completed notebooks. Some are written assignments which give you practice summarizing research results in paper form, writing the various parts of an academic paper, and communicating the results of statistical tests.

Labs (45% of grade)#

The main part of the course will consist of three multi-part labs. These labs will expose key concepts in the psychology of cognition and perception and will consist of multiple exercises and reports. Most of this work will take place in the Jupyterhub.

Final Project (10% of grade)#

The course builds to a final project which will be completed in groups. The final project will involve designing and experiment, collecting the data, and analyzing it. Students may work in small groups (~2-4 people) and will collectively submit a final paper. The final written paper should be in conference submission format with an abstract, intro, methods, results, discussion, and references. The lenght will be 6-8 pages in the Cognitive Science Society Latex template (will discuss in class). Bibliography and references will not included in the page limit. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the day of the final exam.

  • Final Paper: 5%

  • Final Presentation: 5%

Classroom Policies#

Attendance and tardiness#

Attendance at all classes is required. The course is very cumulative and so that the information you learn on one day will be important for the following day’s learning. Thus, it is not something you can catch up easily with notes from a friend. If you have to be away for a class please let the instructor or TA know in advance. Also, students should aim to arrive at class on time as much as possible. Frequent tardiness will be relfected in the participation grade due to missed quizes.

Honor code and plagarism#

All work that students turn in must be their own work. For group assignments, all work must have been done by the students on the team and must include an acknowledgements section detailing the contribution of each team member. Any outside sources (articles, books, people) must be appropriately cited in written assignments. Turning in someone else’s work as your own is unacceptable and will result in a failing grade. Written assignments will be run through a website that checks for plagarism and we have code that compares the submitted responses across class members and to previous semesters (it is easy in python!). Most importantly, such behavior is academically dishonest and lazy. Submit only your own ideas and words.

Research Ethics and Misconduct#

Although the experiments performed in this class are for educational purposes, and therefore not covered by the usual informed consent regulations, we will try to treat the confidentiality of the data as if it were. Falsification of any data or analysis will result in a failing grade for the course. (Note that grades are not based in any way on getting statistical significance or any particular result!)

Statistical software#

Note that part of the class will be learning to use Python software packages for data analysis. We will be teaching these skills in the class. However, if you find that you need extra assistance, the Bobst library provide statistical consultants who are familiar with these packages. According to their webpage: Consultation information will be available on the 6th floor in rooms 620 and 621* via e- mail (data.service@nyu.edu), telephone 212-998-3434, by appointment or on a walk-in basis. Staff and student consultants will offer free tutorials and workshops on a variety of statistical packages. Sign up for fall software tutorials on the library’s classes page: http://www.library.nyu.edu/forms/research/classes.html

Accomodation#

Students requesting academic accommodations are advised to reach out to the Moses Center for Students with Disabilities as early as possible in the semester for assistance.

NYU’s Henry and Lucy Moses Center for Students with Disabilities. 726 Broadway, 2nd Floor New York, NY 10003-6675 Telephone: 212-998-4980 Web site: http://www.nyu.edu/csd Email: mosescsd@nyu.edu For students who would benefit from assistive technology but don’t wish to register with Moses CSD, please use: Info and links on CSD website.

Late Assignments#

All papers and presentations are due at the date and time specified. Scores for late papers will be reduced by 10% for every 24-hour period a paper is late. No extensions will be granted due to computer failure, roommate difficulties, printing problems, etc.

Technology#

Due to the online nature of the course, we will use a variety of technical tools including Zoom, EdStem, Dischord, Jupyter, etc… to facilitate our work together. You will need to have a strong internet connection during class time and so I would recommend you try to work that out early on in the semester. If you have internet connection issues let me know and we can discuss it and work together to find a solution because being online and in class is important.

Disabilities#

Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me by the end of the second week of the term.

Religious observances#

Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance which conflicts with your participation in the course, please meet with me by the end of the second week of the term to discuss appropriate accommodations.

Pandemic#

We are all going through difficult times during the pandemic. These include changes in our normal ways of doing things, living arrangements, family responsibilities, concerns about health and wellbeing, etc… None of us is really OK all the time. If you tell me you’re having trouble, I will not judge you or think less of you but will work with you to come up with a pathway forward. I hope you can extend me and the TA the same grace. You never owe me personal information about your health. You are always welcome to talk to me about things that you are going through. If I can’t help you I might be able to direct you to someone who can. If you need extra help or need to miss a class or need more time please just ask. I will try to work with you.