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ELL457/HSL622: Computation and Cognition

If you are doing the course, please join the Piazza forum (the access code has been announced in class and over e-mail).

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
TA: Akhil Abburu
ELL457: 3 credits (3-0-0); HSL622: 4 credits (3-0-2)
II Semester 2023–24
M Th 15:30–16:50, LH 517

Evaluation components (ELL457)

Evaluation components (HSL622)

Audit criteria

Grade B- or better, plus 75% attendance

Textbooks

  1. Jay Friedenberg and Gordon Silverman. Cognitive Science: An Introduction to the Study of Mind (3rd ed.). SAGE, 2015.
  2. Cameron J. Buckner. From Deep Learning to Rational Machines: What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence. Oxford, 2023.
  3. Stan Franklin. Artificial Minds. MIT, 1995.
  4. John Haugeland, Carl F. Craver, and Colin Klein. Mind Design III: Philosophy, Psychology, and Artificial Intellligence. MIT, 2023.

Other references

  1. José Luis Bermúdez. Cognitive Science: An Introduction to the Science of the Mind. Cambridge, 2nd Edition, 2014.
  2. Jay L. Garfield (ed.). Foundations of Cognitive Science: The Essential Readings. Paragon House, 1990.
  3. Zenon W. Pylyshyn. Computation and Cognition: Toward a Foundation for Cognitive Science. MIT, 1984.
  4. Daniel C. Dennett. The Intentional Stance. MIT, 1989.
  5. Turing, Alan M. Computing Machinery and Intelligence. Mind LIX(236): 433–460 (1950) [doi:10.1093/mind/LIX.236.433] [pdf] [sqapo].
  6. Searle, John R. Minds, Brains, and Programs. Behavioral and Brain Sciences 3: 417–424 (1980) [doi:10.1017/S0140525X00005756] [pdf].
  7. Pinker, Steven. How the Mind Works. Penguin, 1999.
  8. Fodor, Jerry. The Mind Doesn't Work That Way: The Scope and Limits of Computational Psychology. MIT, 2000.
  9. 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 1–7 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 Nature and components of cognition/intelligence; epistemological debates Epistemology of AI
3 10 The relevance of computation; types of computation The empty brain
4 10–13 Symbolic representations and models of cognition
5 14 Student presentations on term paper proposals
6 15–18 The First AI Debate: Can Machines Think?
7 19–25 Connectionist models; Higher order perception like object and pattern recognition; The Second AI Debate: Symbolism or Connectionism? Convolutional neural nets: demos; sparse autoencoder notes; unsupervised visual learning; comparing to brain representations [slides, paper]
8 25–26 Cognitive models and Bayesian inferencing Bayesian models of cognition
11 26 Summary and conclusion Modelling frameworks in Cognitive Science
12 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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