# CSCI 7782 / CSCI 4830 / PSYC 7215 Spring 2008

## Instructors

Department of Psychology
Muenzinger E229
Phone TBA
Office Hours:  W 13:30-15:30 or by appointment

Department of Computer Science
Engineering Center Office Tower 7-41
(303) 492-4103
Office Hours:  Tu 12:30-13:30, W 13:00-14:00, or by appointment

## Course Overview

Cognitive modeling involves the design of computer simulation and mathematical models of human cognition and perception.  The goals of cognitive modeling include:
• understanding mechanisms of information processing in the human brain
• interpreting behavioral, neuropsychological, and neuroscientific data
• suggesting techniques for remediation of cognitive deficits due to brain injury and developmental disorders
• suggesting techniques for facilitating learning in normal cognition
• constructing computer architectures to mimic human-like intelligence.
The range of  modeling tools in cogntiive science are vast, and include production systems (sequential rule fiiring), neural networks, Bayesian probabilistic models, and pure mathematical theories.  All of these tools share the following virtues:
• Models force you to be explicit about your hypotheses and assumptions.
• Models provide a framework for integrating knowledge from various fields.
• Models allow you to bserve complex interactions among hypotheses.
• Models provide the ultimate in controlled experimentation.
• Models lead to empirical predictions.
• Models provide the sort of mechanistic framework that will ultimately be required in a theory of cortical computation.
Models can be built at many levels of the reductionist hierarchy.  Single cell models characterize the details of neural function:  ion flow, membrane depolarization, neurotransmitter release, action potentials, neuromodulatory interactions.  Network models focus on neurophysiology and neuroanatomy of cortical regions, cell firing patterns, inhibitory interactions, and neural mechanisms of learning.. Functional models characterize the operation and interaction of components of the cognitive architecture and emphasize the transformation of representations.  Finally, models at the computational level focus on the input-output behavior of the system and provide a mathematical characterization of cognition and learning.  In this seminar, we'll emphasize the functional and computational level models.  Randy O'Reilly and Yuko Munakata in psychology teach an outstanding course that focuses on the single-cell and network levels.

We will read state-of-the-art research in the field of cognitive modeling, critique the work, and discuss its contributions to the field. Students will have the opportunity to develop their own models as well.  The course participants are likely to be a diverse group of students and faculty, some with primarily an engineering/CS focus and others primarily interested in cognitive science and cognitive neuroscience.

In 2008, we plan to focus on sequential dependencies in human cognition, i.e., how one experience influences subsequent perceptions, decisions, and judgements.  As a trivial example, if I ask the following question: "On a 1 to 10 scale, how bad is it to steal from a homeless person?", your response will depend on the preceding question I've asked.  Stealing will be given a lower rating if the previous question is, "How bad is it to shoot at someone who annoys you?" than if the previous question is, "How bad is it to not leave a 10% tip?

The instructors believe that sequential dependencies offer deep insight into mechanisms and principles of learning in the brain. Sequential dependencies occur across domains of cognition -- perception, attention, categorization, decision making, language, choice -- and at multiple time scales.  We will examine both experimental papers and models that have been built to explain sequential dependencies.  And throughout the course, we will seek common underlying mechanisms and normative principles to explain sequential dependencies.

## Prerequisites

The course is open to any students who have some background in cognitive science or artificial intelligence.  Some background in proababilty and statistics will be helpful, but iis not essential as long as you are willing to learn.

## Course Requirements

In the style of graduate seminars, your primary responsibility for the course will be to read the series of papers before class and be prepared to come into class to discuss the paper (asking clarification questions, working through the math in the paper, relating the paper to other readings, critiquing the paper, presenting original ideas related to the paper).

### Written Commentaries

For some of the readings, we'll ask you to write a one-page commentary on the paper, The commentary consists of approximately one page of comments, questions, or critiques of the assigned reading(s) for that class. This page will be due the day of class, and can include one or more of the following:
• a summary of what you think the main or most interesting ideas are behind the reading(s).
• questions about the material for further discussion, either clarification questions or points of disagreement with the authors (I don't see how such and such will work as the author claims...'').
• comments on how the assigned reading relates to other course readings, or, if you feel ambitious and want to track down some related work in the field, how the assigned reading compares to this other work.
• a critique of the work. What are the flaws in the ideas presented? What are the limitations? Do the authors place their work in the appropriate theoretical perspective? Do the authors overstate their results? In what direction might the work be extended?
These commentaries are intended to promote careful thought about a paper before the session in which it is discussed. The point is not to give you more busy work, but rather to encourage you to jot down notes and questions as you read the papers. They will not be accepted after the class in which the paper is discussed.

### Presentation

You are required to present  a share of the papers during the course of the semester.  The presentation is meant to be a summary of the paper and its main ideas.  Ideally, two class members will collaborate to do each presentation, allowing you to work through the papers together.  We expect grad students to do twice the presentations that undergrads do.  We guess that grad students will do 2 or 3 presentations and undergrads will do 1.

Presentations may be of the main article for the class (which everyone is required to read), or a supplementary article that we recommend.  By assigning students to present the supplementary articles, we can cover a lot more material without asking everyone to read every paper.

Grades will be based roughly on the following:  oral presentations 20%, class discussions 20%, written commentary on papers 60%.

## Class-By-Class Plan and Course Readings

All papers for the course can be found here or click on the individual readings.

## Other Papers

#### Overview

Mozer, Kinoshita, & Shettel (2007). Sequential dependencies offer insight into cognitive control

#### Stimulus and Response sequences -- alternation, priming of repetition, response priming

Jentzsch & Leuthold (2006?).  Response conflict determines sequential effects in short response-stimulus-interval serial response time tasks.  JEP:HPP
repetition suppression paper?

Soetens, Deboek, & Hueting (1984). Automatic Aftereffects in Two-Choice Reaction Time: A Mathematical Representation of Some Concepts

Dobbins, I.G., Schnyer, D.M., Verfaellie, M. & Schacter, D.L. (2004). Cortical activity reductions during repetition priming can result from rapid response learning. Nature, 428, 316-319.

Pashler & Baylis (1991). Procedural learning 2: Intertrial repetition effrects in speeded-choice tasks.  This paper suggests that the locus of sequential effects is primarily in the S-R mapping.

Marios G. Philiastides, Roger Ratcliff, and Paul Sajda1. Neural representation of task difficulty and decision making during perceptual categorization:  A timing diagram.

#### Attention

Neo & Chua (2006). Capturing focused attention -- probably not worth doing a class on, but Vecera suggested reintrepreting these results in terms of sequential effects
Geng, J. J. and Behrmann, M. (2006). Spatial probability as an attentional bias in visual search, Perception and Psychophysics, 67, 7, 1252-1568.

#### Categorization

Busemeyer & Myung (1988) A new method for investigating prototype learning

#### Memory

Anderson, J. (1997). A production system theory of serial memory.  Psychological Review, 104, 728-748.

#### Judgement

Huettel, S. A.,  & Lockwood, G. R. (1999). Range effects of an irrelevant dimension on classification. Perception & Psychophysics, 61, 1624-45.
Lockhead (2004) Absolute judgements are relative: A reinterpretation of some psychophysical ideas
Brown, Marley, & Lacouture (2007) Is absolute identification always relative?
Stewart, N. (2007). Absolute identification is relative: A reply to Brown, Marley, and Lacouture. Psychological Review, 114, 533-538.
Mozer, Jones, & Shettel (2007). Context effects in category learning: An investigation of four probabilistic models
Jesteadt, Luce, & Green (1977) Sequential effects in judgments of loudness
Ward & Lockhead (1970) Sequential effects and memory in category judgments
Lockhead & King (1983) A memory model for sequential effects in scaling tasks
Parducci paper?

Change Detection

Causal Learning

Probability & Reinforcement Learning

Flood, MM (1954). Environmental non-stationarity in a sequential decision-making experiment. (Hardcopy)

Conditioning

#### Language

Bock 2002

Game theory

Vlaev & Chater (2006) Game relativity: How context influences strategic decision making
Jones & Zhang (2004) Rationality and bounded information in repeated games, with application to the iterated Prisoner’s Dilemma
Colman (1998). Rationality assumptions of game theory and the backward induction paradox. In: Rational models of cognition, ed. M. Oaksford & N. Chater. Oxford University Press. (BF311.R34 1998)

Long-term effects

Gilden & Wilson (1995) On the nature of streaks in signal detection
Gilden, Thornton, & Mallon (1995) 1/f noise in human cognition
Gilden (1997) Fluctuations in the time required for elementary decisions
Thornton & Gilden (2005) Provenance of correlations in psychological data
Wagenmakers, Farrell, & Ratcliff (2005) Human cognition and a pile of sand: A discussion on serial correlations and self-organized criticality
Farrell, Wagenmakers, & Ratcliff (2006) 1/f noise in human cognition: Is it ubiquitous and what does it mean?
van Orden, Holden, & Turvey (2005) Human cognition and 1/f scaling
van Orden, Holden, & Turvey (2003) Self-organization of cognitive performance
Gilden & Hancock (2007) Response variability in attention deficit disorders
Wagenmakers, Farrell, & Ratcliff (2004) Estimation and interpretation of 1/f^\alpha noise in human cognition

#### Decision Making

Vlaev, I., Chater, N., & Stewart, N. (2007b). Relativistic financial decisions: Context effects on retirement saving and investment risk preferences. Judgment and Decision Making, 2, 292-311.
Vlaev, I., Chater, N., & Stewart, N. (2007a). Financial prospect relativity: Context effects in financial decision-making under risk. Journal of Behavioral Decision Making, 20, 273-304

## Legal Disclaimers

If you qualify for accommodations because of a disability, please submit a letter from Disability Services in a timely manner so that your needs may be addressed.  Disability Services determines accommodations based on documented disabilities.  Contact: 303-492-8671, Willard 322, and http://www.Colorado.EDU/disabilityservices.

Campus policy regarding religious observances requires that faculty make every effort to reasonably and fairly deal with all students who, because of religious obligations, have conflicts with scheduled exams, assignments or required attendance.  Please see me if you have concerns with our syllabus.  See full details of campus policy at http://www.colorado.edu/policies/fac_relig.html.

Students and faculty each have responsibility for maintaining an appropriate learning environment. Students who fail to adhere to such behavioral standards may be subject to discipline. Faculty have the professional responsibility to treat all students with understanding, dignity and respect, to guide classroom discussion and to set reasonable limits on the manner in which they and their students express opinions.  Professional courtesy and sensitivity are especially important with respect to individuals and topics dealing with differences of race, culture, religion, politics, sexual orientation, gender variance, and nationalities.  Class rosters are provided to the instructor with the student's legal name. I will gladly honor your request to address you by an alternate name or gender pronoun. Please advise me of this preference early in the semester so that I may make appropriate changes to my records.  See polices at http://www.colorado.edu/policies/classbehavior.html and at http://www.colorado.edu/studentaffairs/judicialaffairs/code.html#student_code.