Categories and Concepts – Fall 2023

General information

Instructor: Brenden Lake
Assistant Professor of Psychology and Data Science
New York University
brenden@nyu.edu

Meeting time and location:
Monday 4:00-5:50 PM
Meyer Room 465 (6 Washington Place)

Course numbers:
PSYCH-GA 2207 (Psychology)

Office hours:
Thursdays 4:30-5:30pm. 6 Washington Place, Room 589. I can also accommodate you on zoom if you let me know in advance.

Summary

This course introduces the major topics in the psychology of concepts, focusing on issues of concept representation and use. The first part of the course discusses the main theories of concepts, including the classic view, prototype models, exemplar models, and the knowledge view. We will also spend several weeks discussing computational models that implement these theories, focusing on the neural network and probabilistic traditions. The second part of the course will cover other key topics including taxonomic categories, category-based induction, conceptual development, categorical perception, and conceptual combination. The readings will be drawn from the textbook and classic papers in the field. The course will be in a lecture-discussion format.

I am grateful to Greg Murphy for developing this first version of this class and, of course, writing the “The Big Book of Concepts” that we use as our textbook.

Textbook

We will be using Greg Murphy’s “The Big Book of Concepts” as the textbook for this class. We will also draw original research articles. You can get the book from Amazon. You can also download the individual chapters here while on the NYU network.

Pre-requisites

• This course is for graduate students in cognitive science and related fields. All students are expected to have previous coursework in psychology.
• Computational modeling has been central to the study of concepts and categories. This course will cover some of the key modeling proposals in the literature, with a stronger focus on modeling than previous versions of the course. I want this to be a positive for everyone interested in the course, and I will not assume you have a lot of experience with computational modeling. I will try to make the material as understandable as possible, even though we will not have time to cover the basics of cognitive modeling. If you have taken “Computational cognitive modeling,” you’re in a great position; if you have had linear algebra and statistics as an undergraduate, you will also be in the a good position to understand the modeling details. If you don’t have either, don’t fret! This course does not require programming or implementing computational models, and I hope you find this aspects of the class interesting regardless of your background.
• Again, computer programming will not be used in this course.

Grading

Grading will be based on the response papers (35%) and a final paper (65%). Class participation will be used to decide grades in borderline cases.

Responses to the reading

Each week you will write a mini-paper (about 4 paragraphs or about 600 words) in which you will critique the week’s readings, discuss an issue raised by it, or propose a new experiment based on it. The main purpose of these responses is to get you to: 1) do the reading on time so we can talk about it, and 2) think about the reading. In the responses, please focus on what issues are most important or interesting and to think about, and what questions are unresolved in the field. Do not give a list of minor questions or flaws. You may skip one weekly response, but any other missed ones will need to be made up. Post your response to the class EdStem page before class (the night before would be preferred). Your responses will be graded on a check-plus, check, or check-minus basis, with most responses receiving a check.

Final paper

The final paper will be due Wed. December 13. Submit via email with the file name lastname-cc-final.pdf

The final paper should address one of the topics covered in the class in more detail. Alternatively, it could investigate a key topic related to concepts/categories that was not covered in class. To make sure your paper is headed for success, you should write a proposal for your final paper due on Monday 11/13 (one half page written). Submit via email with the file name lastname-cc-proposal.pdf (brenden@nyu.edu). You can also discuss your topic with me during office hours. The paper should include a critical review of the literature, along with theoretical conclusions or suggestions for future research. I would expect papers to be about 12 pages long (double spaced), though the exact length is not as important as the quality of thought the paper reveals. In your paper, you should also be sure to connect with and demonstrate your knowledge of the topics covered in class.

EdStem and course discussion

We will be using the class Edstem here to post the weekly responses to the readings, and for class discussion in general. If you are registered for the class, you should automatically be added to the class on Edstem. If not, you can join the class EdStem through this link.

Course policies

Overview of topics and schedule

• 9/4 Labor day (no class)
• 9/11 Introduction; the classical view
• 9/18 Prototype and exemplar theories
• 9/25 Concepts as theories and the knowledge view
• 10/2 Computational models of category learning (part 1)
• 10/10 (Note special Tuesday time due to Fall recess) Computational models of category learning (part 2)
• 10/16 Computational models of category learning (part 3)
• 10/23 Computational models of category learning (part 4)
• 10/30 Taxonomic organization and the basic level
• 11/6 Category-based induction
• 11/13 Concepts in infancy
• 11/20 Conceptual development
• 11/27 How categories influence perception
• 12/4 Conceptual combination and exemplar generation
• 12/11 TBD
• Final paper proposal due (Monday 11/13)
• Final paper due (Wed 12/13)

Detailed schedule and readings

Please see below for the assigned readings for each class (to be read before class). Papers are available for download on Brightspace.

9/11 Introduction; the classical view (slides)

• Big Book; Chapter 1

9/18 Prototype and exemplar theories (slides)

• Big Book; Chapter 2 and Chapter 3
• Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7(4), 573-605.
• Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207-238.

9/25 Concepts as theories and the knowledge view (slides)

• Big Book; Chapter 4 (pgs. 94-114) and Chapter 6
• Murphy, G. L., & Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289-316.
• Barsalou, L. W. (1983). Ad hoc categories. Memory & cognition, 11(3), 211-227.

10/2 Computational models of category learning (part 1) (slides)

• Kruschke, J. L. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22-44.

10/10 (Note special Tuesday time due to Fall recess) Computational models of category learning (part 2)
(slides)

• Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98(3), 409.
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).
• (Optional reference on probability theory) Russel, S. J., and Norvig, P. Artificial Intelligence: A Modern Approach. Chapter 13, Uncertainty.

10/16 Computational models of category learning (part 3) (slides)

• Xu, F., & Tenenbaum, J. B. (2007). Word learning as Bayesian inference. Psychological Review, 114(2), 245.
• Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. (2008). A rational analysis of rule‐based concept learning. Cognitive Science, 32(1), 108-154.

10/23 Computational models of category learning (part 4) (slides)

• Heit, E., & Bott, L. (2000). Knowledge selection in category learning. In Psychology of learning and motivation (Vol. 39, pp. 163-199). Academic Press.
• Rehder, B. (2007). Essentialism as a generative theory of classification. In A. Gopnik, & L. Schultz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 190-207). Oxford, England: Oxford University Press.

10/30 Taxonomic organization and the basic level (slides)

• Big Book; Chapter 7
• Rosch, E., Mervis, C. B., Gray, W. Johnson, D., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382-439.
• Tanaka, J. W., & Taylor, M. (1991). Object categories and expertise: Is the basic level in the eye of the beholder?. Cognitive Psychology, 23(3), 457-482.

11/6 Category-based induction (slides)

• Big Book; Chapter 8
• Osherson, D. N., Smith, E. E., Wilkie, O., Lopez, A., & Shafir, E. (1990). Category-based induction. Psychological Review, 97, 185-200.
• Kemp, C., & Tenenbaum, J. B. (2009). Structured statistical models of inductive reasoning. Psychological Review, 116(1), 20.

11/13 Concepts in infancy (slides)

• Big Book; Chapter 9
• Mandler, J. M., & McDonough, L. (1993). Concept formation in infancy. Cognitive Development, 8, 291-318.
• Quinn, P. C. (2004). Development of subordinate-level categorization in 3- to 7-month-old infants. Child Development, 75, 886-899.

11/20 Conceptual development (slides)

• Big Book; Chapter 10
• Markman, E. M. (1989). Categorization and naming in children: Problems of induction. Cambridge, MA: MIT Press. (excerpts only)
• Gelman, S. A. (2003). The essential child. Oxford: Oxford University Press. (excerpts only)

11/27 How categories influence perception (slides)

• Goldstone, R. L., & Hendrickson, A. T. (2010). Categorical perception. Wiley Interdisciplinary Reviews: Cognitive Science, 1(1), 69-78.
• Goldstone, R. L. (1994). Influences of categorization on perceptual discrimination. Journal of Experimental Psychology: General, 123(2), 178.
• Schyns, P. G., & Rodet, L. (1997). Categorization creates functional features. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(3), 681.

12/4 Conceptual combination and exemplar generation

• Big Book; Chapters 12 and 13
• Murphy, G. L. (1988). Comprehending complex concepts. Cognitive Science, 12(4), 529-562.
• Ward, T. B. (1994). Structured imagination: The role of category structure in exemplar generation. Cognitive Psychology, 27(1), 1-40