Spring 2023 // PSYCH-GA 3405: CORE COURSE
Learning and Memory
Tuesday 10:30-12:20pm, Meyer 465
Learning is a critical component of adaptive behavior in animals and humans. This course will expose students to key concepts, theories, and experimental paradigms for studying human learning. The goal is to provide an integrative view of the area that crosses both classic approaches (e.g., classical conditioning, instrumental learning) as well as modern issues (e.g., cognitive neuroscience of learning, language learning, social learning, computational approaches). Special attention will be given to exploring what is known about the neural substrates of learning and memory, as well as computational and mathematical theories. Students will leave the course as sophisticated consumers of learning research and be able to apply learning concepts directly to their own research. This course fulfills part of the introductory "core" cognition requirements for the NYU psychology program. As such there will be a series of take-home exams throughout the semester that assess mastery of the key concepts.
Todd M. Gureckis
website: here, lab
office: Meyer 590
office hours: by appt.
Catherine Hartley
website: here, lab
office: Meyer 871A
office hours: by appt.
Format of the Course
The course will be organized into a series of lectures on core topics in the field of learning and memory. Each class students will submit a reaction piece (approximate 2 paragraphs) with questions or thoughts that came up during the reading. These are due by 9am Tuesday before class. Each class will include time for discussion between students and the instructor about the core ideas.
Learning and Memory by Howard Eichenbaum [website, amazon] Readings from the book will be supplemented with additional research articles distributed from this website. Note: At various point there are a lot of readings for this course. It is a core course and so is reading-intensive. If you aren't reading 20-30 hours a week you aren't reading enough in grad school. This course should help increase your average. Given the number of papers it is important to try to read 0.5-1 papers per day for the course so we recommend setting aside 30 mins every day for reading.
Active class participation (15%) and weekly reading responses (15%), two exams (each worth 35%). Assignments, including weekly reading reactions, will be submitted using [Gradescope](https://www.gradescope.com/). You should have received an invite to the course but if you didn't contact the instructors.
There is a slack channel for the course. This is where we can send announcements to you and students can share interesting papers related to the course, ask questions, start discussions. Contact Todd if you haven't been added.
Schedule and Readings
(Final reading list subject to change, please check back each week.)
Date Agenda Assignments/Slides
Tue Jan 24
#1. Introduction/Overview
Introduction to class for people contemplating registering. Overview of syllabus, instructors, requirements, grading, etc... Introduction of students.
Tue Jan 31
#2. What is learning exactly?
Historical ideas and the birth of the modern science of learning. Additional topics include learning/performance, innate behaviors versus adaptation (nature/nurture), critical periods, models and mechanisms, and levels of analysis.
  1. Textbook reading: Eichenbaum, Chapter 1 - The nature of learning and memory
  2. Meltzoff, A.N., Kuhl, P.K., Movellan, J. and Sejnowski, T.J. (2009) "Foundations for a New Science of Learning" Science, 325, 284-288. [PDF]
  3. Zador, A. (2019) A critique of pure learning and what artificial neural networks can learn from animal brains. Nature Communications, 10, 3770. [PDF]
Lecture 1 slides
Tue Feb 07
#3. Basic concepts in the neuroscience of learning and memory
In the following weeks we will explore a number of basic phenomena of learning. However, it is helpful to begin by casting these ideas against the backdrop of contemporary neuroscience. Today's lecture will be a basic whirl-wind tour of the neural processes thought to underly learning and memory. We'll talk about the function of neurons, the specialization of function in the brain, basic learning mechanisms (hebbian learning, LTP), and modern techniques for studying learning and memory (fMRI, EEG, etc...).
  1. Textbook reading: Eichenbaum, Chapter 2 - The neural bases of learning and memory
  2. Scoville, W.B. and Milner, B. (1957) "Loss of Recent Memory After Bilateral Hippocampal Lesions" Journal of Neurology, Neurosurgery and Psychiatry, 20, 11-21. [PDF]
  3. Squire, L.R. (1992) "Declarative and Nondeclarative Memory: Multiple Brain Systems Supporting Learning and Memory" Journal of Cognitive Neuroscience, 4 (3), 232-243. [PDF]
  4. Josselyn, S.A. and Tonegawa, S. (2020). Memory engrams: Recalling the past and imagining the future. Science, 367(6473), eaaw4325. [PDF]
Lecture 2 slides
Tue Feb 14
#4. Unsupervised and perceptual learning
This lecture will cover non-associative forms of learning (habituation/sensitation), unsupervised learning, perceptual learning, latent learning, feature learning, stimulus-stimulus learning, statistical learning, imprinting, priming, repetition suppression.
  1. Textbook reading: Eichenbaum, Ch. 3&4 - Simple Forms of Learning and Memory/Perceptual Learning and Memory
  2. Aslin, R.N. and Newport, E.L. (2012) Statistical learning: From acquiring specific items to forming general rules. Current Directions in Psychological Science, 21 (3), 170–176. [PDF]
  3. Goldstone, R.L. (1998) "Perceptual Learning" Annual Review of Psychology, 49, 585-612. [PDF]
  4. Barlow, H.B. (1989) "Unsupervised Learning" Neural Computation, 1, 295-311. [PDF]
Lecture 3 slides
Tue Feb 21
#5. Classical conditioning I
Pavlov, basic procedure, phenomena and terms (CS/US, etc...), basic findings, blocking and overshadowing, etc..., Resorla-Wagner model, Pearce-Hall model and the role of attention/associability in classical conditioning, basic neural substrates of classical conditioning, interactions with other learning systems (e.g., role of hippocampus in trace conditioning).
  1. Textbook reading: Eichenbaum, Ch. 5 - Procedural Learning I: Classical Conditioning
  2. Rescorla, R.A. (1998) "Pavlovian Conditioning: It's not what you think it is" American Psychologist, 43(4), 151-160. [PDF]
  3. Rescorla, R.A. and Wagner, A.R.(1971) "A Theory of Pavlovian Conditioning: Variations in the Effectiveness of Reinforcement and Non-reinforcement" in Black, A.H. & Prokasy, W.F. (eds.), Classical conditioning II: Current research and theory (pp. 64-99). New York: Appleton-Century-Crofts. [PDF]
  4. Courville, A., Daw, N.D., Touretsky, D.S. (2006) Bayesian theories of conditioning in a changing world. Trends in Cognitive Sciences, 10(7). 294-300. [PDF]
Lecture 4 slides
Tue Feb 28
#6. Classical conditioning II
modern theories including causal interpretations of classical conditioning, context-dependent learning, second-order condition (temporal-difference model and relationship to Rescorla-Wagner), neural basis of prediction errors
  1. Niv, Y. and Schoenbaum, G. (2008) "Dialogues on prediction errors" Trends in Cognitive Science, 12(7), 265-72. [PDF]
  2. Schultz, W., Dayan, P. & Montague, P.R. (1997) "A neural substrate of prediction and reward" Science, 275, 1593. [PDF]
  3. Gershman, S.J., and Blei, D. and Niv, Y. (2009) "Context, learning, and extinction" Psychological Review, 117(1), 197-209. [PDF]
Lecture 5 slides
Tue Mar 07
#7. Instrumental conditioning I
law of effect, role of reinforcement, stimulus control, choice behavior, matching law, melioration, concurrent schedules, self control/impulsivity, habits and planning, superstitious responding (special thanks to nathaniel daw for sharing slides and thoughts on the instrumental section)
  1. Textbook reading: Eichenbaum, Ch. 6 - Procedural Learning II: Habits and Instrumental Conditioning
  2. Herrnstein, R.J. (1970) "On the law of effect" Journal of the Experimental Analysis of Behavior, 13, 243-266. [PDF]
  3. Skinner, B.F. (1948) "Superstition in the Pigeon" Journal of Experimental Psychology, 38, 168-172. [PDF]
  4. Dickinson, A. (1985) "Actions and Habits: The Development of Behavioral Autonomy" Philosophical Transactions of the Royal Society of London. Series B, Biological, 38, 168-172. [PDF]
Lecture 6 slides
Tue Mar 14 No class, Spring Break Midterm Assigned
Tue Mar 21
#8. Instrumental conditioning II
computational reinforcement learning, model-based and model-free learning algorithms
  1. Silver, D., Sigh, S., Precup, D. and Sutton, R.S. (20210) Reward is enough. Artificial Intelligence, 299, 103535. [PDF]
  2. Tolman, E.C. (1948) "Cognitive Maps in Rats and Men" Psychological Review, 55(4), 189-208. [PDF]
  3. Daw, N., Gershman, S.J, Seymour, B., Dayan, P. and Dolan, R.J. (2011) Model-based influences on humans' choices and striatal prediction errors. Neuron, 69, 6, 1204-1215. [PDF]
Lecture 7 slides
Tue Mar 28
#9. Generalization and discrimination
Pearce (configural) vs. R-W (elemental), stimulus generalization, attention learning, context dependent learning
  1. Mitchell, T.M. (1980). The need for biases in learning generalizations (Report CBM- TR-5-110). New Brunswick, NJ: Rutgers University, Department of Computer Science. [PDF]
  2. Shepard, R.N. (1987) "Toward a universal law of generalization for psychological science" Science, 237(4820), 1317-1323. [PDF]
  3. Dunsmoor, JE and Murphy, GL (2015) Categories, concepts, and conditioning: how humans generalize fear Trends in cognitive sciences 19 (2), 73-77. [PDF]
Midterm Due
Lecture 8 slides
Tue Apr 04
#10. Episodic memory
introduction, episodic memory, hippocampus and space, flexibility, interactions with striatum and cortex
  1. Textbook reading: Eichenbaum, Ch. 8 - Cognitive memory
  2. Davachi, L., Mitchell, J.P., and Wager, A.D. (2003) Multiple routes to memory: Distinct medial temporal lobe processes build item and source memories. Proceedings of the National Academy of Science, 100 (4) 2157-2162 [PDF]
  3. Lisman, J., Buzsáki, G., Eichenbaum, H., Nadel, L., Ranganath, C., & Redish, A.D. (2017) Viewpoints: how the hippocampus contributes to memory, navigation and cognition. Nature Neuroscience 20, 1434–1447. [PDF]
  4. Stachenfeld, K.L., Botvinick, M.M., and Gershman, S.J. (2017) The hippocampus as a predictive map. Nature Neuroscience, 20(11), 1643-1653. [PDF]
  5. Shiffrin, R.M. and Steyvers, M. (1997) A model for recognition memory: REM -- retrieving effectively from memory. Psychonomic Bulletin & Review, 4, 145-166. [PDF]
Lecture 9 slides
Tue Apr 11
#11. Memory consolidation and complementary learning systems
consolidation, complementary learning systems hypothesis, catastrophic interference, specificity and abstraction, recognition versus recall
  1. Textbook reading: Eichenbaum, Ch. 11 - Memory consolidation
  2. McClelland, J.L. and McNaughton, B.L., and O'Reilly RC. (1995) Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory Psychological Review, 102(3), 419-457 [PDF]
  3. Tse D., Langston R.F., Kakeyama M., Bethus I., Spooner P.A., Wood E.R., Witter M.P. and Morris R.G.M. (2007) "Schemas and memory consolidation" Science, 316 (5821), 76 [PDF]
  4. Leutgeb JK, Leutgeb S, Moser MB, Moser EI. (2007) Pattern separation in the dentate gyrus and CA3 of the hippocampus. Science, 315(5814):961-6. [PDF]
Lecture 10 slides
Tue Apr 18
#12. Context and memory
events, temporal context models, decay versus interference
  1. Kurby, C.A. and Zacks, J.M., (2008) Segmentation in the perception and memory of events. Trends in Cognitive Science, 12(2), 72-79.
  2. Howard, M.W. and Kahana, M.J. (2001) A distributed representation of temporal context. Journal of Mathematical Psychology. 46(3), 269-299.
Lecture 11 slides
Tue Apr 25
#13. Semantic memory
semantic memory, models of semantic memory, episodic-semantic memory interactions
  1. Textbook reading: Eichenbaum, Ch. 10 - Semantic Memory
  2. Tenenbaum, J.B., Kemp, C., Griffiths, T.L., and Goodman, N.D. (2011) How to grow a mind: Statistics, structure and abstraction. Science, 331(6022), 1279-1285
  3. Jones, M. N., Willits, J. A., & Dennis, S. (2015). Models of semantic memory. In J. R. Busemeyer & J. T. Townsend (Eds.) Oxford Handbook of Mathematical and Computational Psychology.
  4. Bhatia, S. and Richie, R. (2022) Transformer networks of human conceptual knowledge. Psychological Review
Lecture 12 slides
Tue May 02
#14. The science of being a better learner
how to use science of learning and memory to be a better student?
  1. Schacter, D. L. (1999). The seven sins of memory: Insights from psychology and cognitive neuroscience.American Psychologist, 54(3), 182–203. [PDF]
  2. Dunlosky, J., Rawson, K.A., Marsh, E.J., Nathan, M.J., and Willingham, D.T. (2013) Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology Psychological Science in the Public Interest, 14(1), 4-58. [PDF]
  3. Karpicke, J.D. (2012) Retrieval-Based Learning: Active Retrieval Promotes Meaningful Learning Current Directions in Psychological Science 21(2),157-163. [PDF]
Final (Due May 19th)
Lecture 13 slides