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ELL457/HSL622: Computation and Cognition

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The subject matter of this course is, in a sense, highly ambitious: we seek to understand the history of the enterprise of computationally conceptualising and emulating the human mind. This is essentially a cognitive science course, with a pronounced computational bent. Cognitive Science is a relatively new discipline which sits at the interface of psychology, neuroscience, linguistics, philosophy, computer science, and electrical engineering. It seeks to understand the processes underlying human cognition by means of formal (especially computational) models. This course can be also seen as trying to merge perspectives from two distinct, but complementary, academic streams: one with an engineering objective, the other with a scientific one. As engineers, we have sought to design intelligent systems that can emulate human performance on perception and cognition tasks. As scientists, we have sought to turn this around and use such designed systems to better understand, or 'reverse engineer', the human mind itself.

Instructor: Sumeet Agarwal
TAs: Oshin Dutta, Avantika Dev, Aadarsh Gupta
ELL457: 3 credits (3-0-0); HSL622: 4 credits (3-0-2)
II Semester 2022–23
M Th 15:30–16:50, IIA 101 (Bharti School building)

Evaluation components (ELL457)

Evaluation components (HSL622)

References

  1. Jay Friedenberg and Gordon Silverman. Cognitive Science: An Introduction to the Study of Mind. SAGE Publications, 2006.
  2. José Luis Bermúdez. Cognitive Science: An Introduction to the Science of the Mind. Cambridge, 2nd Edition, 2014.
  3. Jay L. Garfield (ed.). Foundations of Cognitive Science: The Essential Readings. Paragon House, 1990.
  4. Zenon W. Pylyshyn. Computation and Cognition: Toward a Foundation for Cognitive Science. MIT, 1984.
  5. Stan Franklin. Artificial Minds. MIT, 1995.
  6. Turing, Alan M. Computing Machinery and Intelligence. Mind LIX(236): 433–460 (1950) [doi:10.1093/mind/LIX.236.433] [pdf] [sqapo].
  7. Searle, John R. Minds, Brains, and Programs. Behavioral and Brain Sciences 3: 417–424 (1980) [doi:10.1017/S0140525X00005756] [pdf].
  8. Pinker, Steven. How the Mind Works. Penguin, 1999.
  9. Fodor, Jerry. The Mind Doesn't Work That Way: The Scope and Limits of Computational Psychology. MIT, 2000.
  10. Thomas L. Griffiths, Charles Kemp, and Joshua B. Tenenbaum. Bayesian models of cognition. In Ron Sun (ed.), The Cambridge handbook of computational cognitive modeling (2008) [pdf].

Planned outline

Serial no. Lecture nos. Topics Slides and other resources
1 1–8 Introduction; Philosophical, Psychological, and Cognitive approaches to modeling the mind Introduction: Philosophical and Psychological Perspectives; The Libet Experiment; Mary's Room; The Chinese Room
2 8–9 The relevance of computation; types of computation The empty brain
3 10–12 Symbolic representations and models of cognition
4 13–14; 16–18 Debates about thinking, AI, and representation
5 15 Student presentations on term paper proposals
6 19–22 Connectionist models; Higher order perception like object and pattern recognition Convolutional neural nets: demos; sparse autoencoder notes; unsupervised visual learning; comparing to brain representations [slides, paper]
7 23–24 Cognitive models and Bayesian inferencing Bayesian models of cognition
8 25–26 Cognitive architectures How are cognitive systems organised?; ACT–R paper
9 27–28 Student presentations on final term papers

[Image credits: directactioneverywhere.com; mcgill.ca; edx.org; wikipedia.org; nu.ac.th; binghamton.edu.]

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