version 2019-11-078

NOTE: THIS SYLLABUS IS A LIVING DOCUMENT. THE READING LIST WILL BE ADJUSTED AS WE GO.

Image of brain and neuron HPS 2634 Topics in Philosophy of Cognitive Science: Cognitive Models

Fall 2019 Schedule and Syllabus
Meeting time: M 9:30-12; CL 1008C

Instructor

(Prof.) Colin Allen <colin.allen@pitt.edu>
Office: CL 1109H
Office hours: T 1:45-2:45 and W 2-3 (and by appointment)

Course Description

This seminar will address the diversity of modeling approaches used in the cognitive sciences: mathematical, computational, Bayesian, dynamical, network models, etc. We will cover issues such as the relationship of cognitive models to neuroscience, and questions about the nature and significance of distinctions among models such as cognitive vs. associative, dynamical vs. computational, optimal vs. heuristic. These topics will lead us to more general issues concerning the epistemic and explanatory status of cognitive models, pragmatism in modeling choices, and methods for evaluating models.

There will be maths! (But we will work through it together.) (And see Additional Resources below the schedule.)

Consider four ways in which our reading could be organized:
  1. Technical (cf., chapter headings in the text book: qualitative model comparisons, parameter estimation, quantitive model comparisons, hierarchical modeling)
  2. Historical (origins in psychophysics and learning theory, main contributors, societies, etc.)
  3. Typological (serial, parallel, rational, connectionist, dynamical, causal, bayesian, etc.)
  4. Applications (concept learning, decision making, etc.)
In this course, we will be trying to build solid foundations in the first that recognize the context provided by the second, and help us to understand the debates surrounding the third. My goal is not to weigh in on specific issues in the last category, but those will inevitably come up as we address the others. We are not aiming for a comprehensive view of any application domain, but aiming instead to give you the tools to understand what's at stake in those debates and how modelers have attempted to address shortcomings in less formal approaches to them.

Course Objectives

Aside from acquiring the necessary background in cognitive modeling, the main goal of this seminar is for you to develop a research paper on any philosophical issue raised during the course. You will write a proposal, draft, engage peer reviews, and the final paper. You will also be asked to give at least one presentation based on the reading list, and you will also give a presentation based on your developing research ideas during the last month of the course.

Texts

  1. Book: Busemeyer and Diederich 2010 Cognitive Modeling Sage Publishing.
  2. Additional readings provided electronically from the pool at Blackboard Candidate Readings. Once assigned they will be moved to the folder at Blackboard Assigned Readings

Both Blackboard folders mentioned just above are also accessible from the "Course Documents" menu item on the left. New items will be added to the hopper as we go.

Schedule of Readings and Presentations

We are going to adjust this as we go along.

NOTE: SOMETIMES LINKS BREAK. IF SOMETHING CAN'T BE REACHED, PLEASE LET ME KNOW IMMEDIATELY.

DateTopicReading Assignments
Mon 08/26 Overview •Busemeyer et al. 2015 "Review of Basic Mathematical Concepts Used in Computational and Mathematical Psychology" •Sternberg 1966 "High-Speed Scanning in Human Memory"
Mon 09/07 Getting started: Modeling basics, some history, and a skeptical take from comparative animal cognition •Busemeyer & Diederich Chapters 1 and 2
•Link (2015) "Psychophysical Theory and Laws, History of"
Mon 09/16 Model comparison • Busemeyer & Diederich Chapter 2
•Smith et al. (2016) "Formal models in animal-metacognition research: the problem of interpreting animals’ behavior"
Mon 09/23 Parameter estimation • Nosofsky & Zaki (2002) "Exemplar and Prototype Models Revisited"
•Luce (1995) "Four Tensions Concerning Mathematical Modeling in Psychology"
• Busemeyer & Diederich Chapter 3
Mon 09/30 More (complex) models • Estes (2002) "Traps in the route to models of memory and decision"
• Busemeyer & Diederich Chapter 4
• Townsend et al (2011) "Experimental Discrimination of the World’s Simplest and Most Antipodal Models: The Parallel-Serial Issue"
Mon 10/07 Stay on last week's readings
Mon 10/14 Model selection + hierarchical/Bayes • Busemeyer & Diederich Chapters 5 and 6
• [Brendan] Evans (2019) "Assessing the practical differences between model selection methods in inferences about choice response time tasks"
Mon 10/21 Categories and Decisions • [Clara] Smith et al. (2004) "Category learning in rhesus monkeys
• [CA] Vigo and Allen (unpublished) "Form over Mind: How Structure Determines Cognition"
• [optional] Vigo 2009 "Categorical invariance and structural complexity in human concept learning"
• [Mahi] Bruza, Wang, and Busemeyer 2015 "Quantum Cognition"
Mon 10/28 Dynamical models / Anti-representationalism • [optional] Weinberger and Allen (in prep.) "Unpacking equation (1) of the governor model"
• [Justin] van Gelder 1995 "What might cognition be if not computation?"
Mon 11/04 Dynamical • [Osman] Beer and Williams 2014 "Information Processing and Dynamics in Minimally Cognitive Agents"
Mon 11/11 presentations • [Osman (continued!)] Beer and Williams 2014 "Information Processing and Dynamics in Minimally Cognitive Agents"
• [Dzintra] Shenoy et al. "Cortical control of arm movements"
Mon 11/18 presentations • [Mara] Daile et al. "EMPATH: A Neural Network that Categorizes Facial Expressions"
• Weinberger & Allen (in prep) "What do dynamical models tell us about their target systems?"
Mon 12/02 Papers workshop
Fri 12/13 Final papers due

Additional Resources

If you want some exceptionally clear, wonderfully animated, and often humorous introductions/refreshers for some of the mathematics we'll be faced with, I highly recommend the videos put out by Grant Sanderson, aka 3blue1brown. The following are particularly relevant as we get further into the course: And although it's got nothing to do with the course, this one is a wonderful illustration of the style: This problem seems hard...

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