Meeting time
Wednesday 2-3:50pm, Meyer 465

Instructors
Lila Davachi
lila.davachi@nyu.edu
website: here, lab
office: 871A
office hours: by appt.

Todd M. Gureckis
todd.gureckis@nyu.edu
website: here, lab
office: 859
office hours: by appt.

PSYCH-GA 3405: Core Course

Learning and Memory

Description

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.

Format of the Course
The course will be organized into a series of lectures and hands-on computer lab sessions. Students will be encouraged to interact with one another to solve problems and to develop a solid understanding of the course material. Occasional short homework assignments will be completed partly in class and partly at home.

Books
Learning and Memory by Howard Echenbaum [website]
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.

Grading
Active class participation (15%) and homeworks and assignments based on readings (15%), two exams (each worth 35%).



Tenative Schedule

Date Description Slides
Jan. 27 1. Introduction/Overview - introduction to class for people contemplating registering. Overview of syllabus, instructors, requirements, grading, etc... Introduction of students.
Please fill out the following pre-course survey before class next time.
[slides]
Feb. 3 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

Textbook reading: Eichenbaum, Ch. 1 - The nature of learning and memory

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]

Pinker, S. (2004) “Why nature & nurture won’t go away” Daedalus, 133(4), 5-17. [PDF]

Phattanasri, P., Chiel, H.J., and Beer, R.D. (2007) “The Dynamics of Associative Learning in Evolved Model Circuits” Adaptive Behavior, 15(4), 377-396.[PDF]

[slides]
Feb. 10 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...)

Since this is primarily a introduction/review for students who have no prior exposure to neuroscience or psychology, we will default primarily to the book for neuroscience background, then turn to the paper readings for the evolution of a view of the organization of learning and memory in the brain. If you already know all this, great, but it's always important to look back and realize exactly WHY you know this.

Textbook reading: Eichenbaum, Ch. 2 - The neural bases of learning and memory

Lashley, K.S. (1950) “In search of the Engram” Society of Experimental Biology Symposium, 4, 454-482. [PDF]

Squire, L.R. (1992) “Declarative and Nondeclarative Memory: Multiple Brain Systems Supporting Learning and Memory” Journal of Cognitive Neuroscience, 4 (3), 232-243.[PDF]

Optional Readings (discussed in lecture):

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]

Posner, M.I., Peteresen, S.E., Fox, P.T., Raichle, M.E. (1988) “Localization of Cognitive Operations in the Human Brain” Science, 240, 1627-1631.[PDF]

Bliss, T.VP. and Collingridge, G.L. (1993) A synaptic model of memory:long-term potentiation in the hippocampus. Nature, 361, 31-29. [PDF]

[slides]
Feb. 17 Lecture 2 continued/Lecture 3 begin
Feb. 24 4. Non-associative/perceptual forms of learning - This lecture will cover basic, non-associative forms of learning including perceptual learning, habituation/sensitization (incl. habituation as a empirical technique for studying learning in non-linguistic animals), latent learning, feature learning, imprinting, priming, repetition suppression, etc....

Textbook reading: Eichenbaum, Ch. 3&4 - Simple Forms of Learning and Memory/Perceptual Learning and Memory

Foundational Work:
Tolman, E.C. (1948) “Cognitive Maps in Rats and Men” Psychological Review, 55(4), 189-208. [PDF]

Contemporary Work:
Goldstone, R.L. (1998) "Perceptual Learning" Annual Review of Psychology, 49, 585-612. [PDF]

Some theory:
Barlow, H.B. (1989) “Unsupervised Learning” Neural Computation, 1, 295-311.[PDF]

Optional Readings (discussed in lecture):

Grill-Spector, K., Henson, R. and Martin, A. (2006) "Repetition and the brain: neural models of stimulus-specific effects" Trends in Cognitive Sciences, 10(1), 14-23. [PDF]

[slides]
Mar. 2 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)

Textbook reading: Eichenbaum, Ch. 5 - Procedural Learning I: Classical Conditioning

Foundational Work:
Rescorla, R.A. (1998) “Pavlovian Conditioning: It's not what you think it is” American Psychologist, 43(4), 151-160. [PDF]

A theory:
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]

Example empirical result:
Clark, R.E. and Squire, L.R. (1998) “Classical Conditioning and Brain System: The Role of Awareness” Science, 280, 77-81. [PDF]

More theories:
Dayan, P., Kakade, S. and Montague, P.R. (2000) “Learning and selective attention” Nature Neuroscience, 3, 1218-1223. [PDF]

Optional Theory Paper:
Pearce, J.M. and Hall, G. (1980) “A Model for Pavlovian Learning: Variations in the Effectiveness of Conditioned by Not of Unconditioned Stimuli” Psychological Review, 87, 532-552. [PDF]

[slides]
Mar. 9 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

Learning and prediction errors:
Niv, Y. and Schoenbaum, G. (2008) “Dialogues on prediction errors” Trends in Cognitive Science, 12(7), 265-72. [PDF]

Schultz, W., Dayan, P. & Montague, P.R. (1997) “A neural substrate of prediction and reward” Science, 275, 1593. [PDF]

"Latent Cause" models of classical conditioning:
Courville, A.C., Daw, N.D., Gordon, G.J., and Touretzky, D.S. (2003) “Model Uncertainty in Classical Conditioning” Neural Information Processing Systems, 16, 977-984. [PDF]

Context dependence of conditioning:
Gershman, S.J., and Blei, D. and Niv, Y. (2009) “Context, learning, and extinction” Psychological Review, 117(1), 197-209. [PDF]

[slides]
Mar. 23 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)

Textbook reading: Eichenbaum, Ch. 6 - Procedural Learning II: Habits and Instrumental Conditioning

Dickinson, A. (1994) “Instrumental Conditioning” Animal Learning and Cognition, Chapter 3, pg 45-79. [PDF]

Herrnstein, R.J. (1970) “On the law of effect” Journal of the Experimental Analysis of Behavior, 13, 243-266. [PDF]

Skinner, B.F. (1948) “Superstition in the Pigeon” Journal of Experimental Psychology, 38, 168-172. [PDF]

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]

Optional Instrumental conditioning papers mentioned in lecture:

Sugrue, L.P. (2004) “Matching behavior and the representation of value in the parietal cortex” Science, 304, 1782. [PDF]

Herrnstein, R.J. and Prelec, D. (1991) “Melioration: A Theory of Distributed Choice" The Journal of Economic Perspectives, 5(3), pg 137-156. [PDF]


Mar. 30 8. instrumental conditioning (continued) [slides]
[midterm]
Apr. 6 9. Cognitive Forms of Learning - introduction, episodic memory, hippocampus and space, flexibility, interactions with striatum and cortex


Textbook reading: Eichenbaum, Ch. 8 - Cognitive Memory (versus other forms of memory)

Packard, M.G. and McGaugh, J.L. (1996). Inactivation of hippocampus or caudate nucleus with lidocaine differentially affects expression of place and response learning. Neurobiol Learn Mem. 1996 Jan;65(1):65-72. [PDF]

Cohen, N.J. and Square, L.R. (1980) “Preserved learning and retention of pattern-analyzing skill in amnesia: dissociation of knowing how and knowing that.” Science. 1980 Oct 10;210(4466):207-10. [PDF]

Olton DS, Wible CG, and Shapiro ML. (1986) "Mnemonic theories of hippocampal function", Behav Neurosci. 1986 Dec;100(6):852-5. Review. [PDF]

[slides]
Apr. 13 10. Generalization and Discrimination - Pearce (configural) vs. R-W (elemental), stimulus generalization, attention learning, context dependent learning
And, on to generalization (no corresponding textbook reading this week):

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]

Shepard, R.N. (1987) “Toward a universal law of generalization for psychological science” Science, 237(4820), 1317-1323. [PDF]

Tenenbaum, J.B. and Griffiths, T.L. (2001) "Generalization, similarity, and Bayesian inference.", Behavioral and Brain Sciences, 24, 629-641. [PDF]

[slides]
Apr. 20 11. Recollection and Familiarity - two distinct forms of memory

Textbook reading: Eichenbaum, Ch. 9 - Episodic Memory

Norman KA, O'Reilly RC. (2003) Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. Psychological Reviewi>. 2003 Oct;110(4):611-46. [pdf]

Brown MW, Aggleton JP. (2001) Recognition memory: what are the roles of the perirhinal cortex and hippocampus?. Nat Rev Neurosci. Jan;2(1):51-61. [pdf]

Leutgeb JK, Leutgeb S, Moser MB, Moser EI. (2007) Pattern separation in the dentate gyrus and CA3 of the hippocampus. Science. 2007 Feb 16;315(5814):961-6. [pdf]

[slides]
Apr. 27 12. Cognitive Forms of Learning - category and concept learning, hypothesis testing behavior, learning with rules or associations


Textbook reading: Eichenbaum, Ch. 10 - Semantic Memory

Maddox, W.T. and Ashby, F.G. (2004). Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioral Processes, 66, 309-332. [PDF]

Erickson, M.A. and Kruschke, J.K. (1998) "Rules and Exemplars in Category Learning", Journal of Experimental Psychology: General, 127 (2), 107-140. [PDF]

[slides]
May. 4 13. Cognitive Forms of Learning (continued) - relational processing, causal learning, learning by analogy, prior knowledge and basic learning processes,


[slides]
[final]