CS6251 - Computational Models of Cognition

Course Data :


The course is designed to provide an in-depth appreciation of the central challenges in realizing aspects of human cognition (with specific focus on language and memory ) on machine. It surveys significant breakthroughs in our understanding till date, and identifies avenues for exploration in future.

Course Contents:

  • Introduction: The journey from machines that compute to machines that "think": the central challenges, philosophical conundrums, schools of thought, the big debates.
  • Modeling Paradigms: Declarative/ logic-based computational cognitive modeling, connectionist models of cognition, Bayesian models of cognition, a dynamical systems approach to cognition.
  • Cognitive Models of Memory and Language Computational models of episodic and semantic memory, modeling psycholinguistics (with emphasis on lexical semantics), towards deep understanding: modeling the interaction of language, memory and learning.
  • Modeling Select Aspects of Cognition Classical models of rationality, symbolic reasoning and decision making; Formal models of inductive generalization, causality, categorization and similarity; the role of analogy in problem solving,
  • Cognitive Development Child concept acquisition, child language learning, acquisition of arithmetic skills.
  • Cognition and Artificial Intelligence The import of modeling aspects of human cognition on Artificial Intelligence; cognitive architectures such as as ACT-R, SOAR, OpenCog, CopyCat, Memory Networks; great ideas and open problems:revisiting the gap.
  • Physics of Cognition Exploring the parallels between the science of matter and the science of information, Quantum Models of Cognition, Models of Emergence inspired by Statistical Physics, Non-linear Dynamics and Cognition.

Text Books:

No single textbook, collection of papers / references to be used.

Reference Books:

  • The Cambridge Handbook of Computational Psychology, Ron Sun (ed.), Cambridge University Press (2008)
  • The Oxford Handbook of Computational and Mathematical Psychology, Jerome R. Busemeyer, Zheng Wang, James T.
  • Townsend, Ami Eidels (ed.), Oxford University Press (2015)
  • Formal Approaches in Categorization, Emmanuel M. Pothos, Andy J. Wills, Cambridge University Press (2011)
  • Quantum Models of Cognition and Decision, Jerome R. Busemeyer, Peter D. Bruza, Cambridge University Press (2014)
  • The Quest for Artificial Intelligence, Nils J. Nilsson, Cambridge University Press (2009)
  • Cognition, Brain and Consciousness: Introduction to Cognitive Neuroscience (2010), Bernard J. Bears, Nicole M. Gage, Academic Press (2010)



Credits Type Date of Introduction
4-0-0-0-8-12 Elective Feb 2017

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