(Sequential Dependencies in Human Cognition)
CSCI 7782 / CSCI 4830 / PSYC 7215
Tu, Th 14:00-15:15
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
characterize the operation and interaction of components of the
cognitive architecture and emphasize the transformation of
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
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,
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
underlying mechanisms and normative principles to explain sequential
The course is open to any
have some background in cognitive science or artificial intelligence.
Some background in proababilty and statistics will be
but iis not essential as long as you are willing to learn.
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).
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,
critiques of the assigned reading(s) for that class. This page will be
day of class, and can include one or more of the following:
- a summary of what you think the main or most interesting
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
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
- 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
promote careful thought about a
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
class in which the paper is discussed.
You are required to
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.
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.
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
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.
||Vision and eye movements
Fecteau & Munoz (2003) Exploring the consequences of the previous trial
Sharma et al. (2003) V1 neurons signal acquisition of an internal representation of stimulus location
||Maloney, L. T., Dal Martello, M. F., Sahm, C., & Spillmann, L. (2005). Past trials influence perception of ambiguous motion quartets through pattern completion
||Kristjansson (2006) Rapid learning in attention shifts: A review
||Kristjansson et al. (2006). Neural basis for priming of pop-out during visual search revealed with fMRI.
||Mozer, Shettel, & Vecera (2006). Control of visual attention: A rational account
COMMENTARY IS NOT REQUIRED
|Mozer & Baldwin (2008). Experience-guided search: A theory of attentional control
||Distinct mechanisms underlying sequential effects
|Cho, Nystrom, Brown, Jones, Braver, Holmes, & Cohen (2002). Mechanisms underlying dependencies of performance on stimulus history in a two alternative forced choice task
||Jentzsch & Sommer (2002) Functional localization and mechanisms of sequential effects in serial reaction time
COMMENTARY IS REQUIRED
Notebaert & Soetens (2003) . The influence of irrelevant stimulus changes on stimulus and response repetition effects.
||Motor control and response intiation
Mozer, Kinoshita, & Davis (2004) Control of response initiation: Mechanisms of adaptation to recent experience
COMMENTARY IS NOT REQUIRED
|Song & Nakayama (2007). Automatic adjustment of visuomotor readiness
Jones (Song & Nakayama)
||Dixon & Glover (2004). Action and memory.
||Gilovich, T., Vallone, R., & Tversky, A. (1985). The hot hand in basketball: On the misperception of random sequences
Gilden & Wilson (1995) Streaks in skilled performance
Bar-Eli, Avugos, & Raab
(2006). Twenty years of “hot
hand” research: Review and critique
|Matt Wilder, Dan Knights
Holger Dick (Gilden)
Steyvers & Brown (2006). Optimal change detection
Johnson J, Tellis GJ (2005). Blowing bubbles: Heuristics and biases in the run-up of stock prices
||Adam Bates (Johnson & Tellis); Mozer (Steyvers & Brown)
||Perceptual judgement and estimation
||DeCarlo & Cross (1990) Sequential effects in magnitude estimation: Models and theory
|Jesteadt, Luce, & Green (1977) Sequential
effects in judgments of loudness
||Treisman & Williams (1984) A theory of criterion setting with an application to sequential dependencies
|Lockhead & King (1983) A
memory model for sequential effects in scaling tasks
||Mozer & Adam Bates
||Petrov & Anderson (2005) The dynamics of scaling
COMMENTARY IS NOT REQUIRED, BUT TRY TO HAVE A LOOK AT THE PAPER (We decided that two heavy papers in one week was too much.)
Petrov, Dosher, & Lu (2005). The dynamics of
perceptual learning: An incremental reweighting model
Petrov, Dosher, & Lu (2006). Perceptual learning without feedback in non-stationary
contexts: Data and model
||Stewart, Brown, & Chater (2005). Absolute identification by relative judgement
||Brown, Marley, & Lacouture (2007) Is
absolute identification always relative?
Stewart, N. (2007). Absolute
identification is relative: A reply to Brown, Marley, and Lacouture
|Laura Rassbach (Stewart, Brown & Chater, 2005)
||Myers, JL (1976). Probability learning.
In W. K. Estes (Ed.), Handbook of learning and cognitive processes:
Vol. 3. Approaches to human learning and motivation (pp. 171-205).
Hillsdale, NJ: Erlbaum.
Sutton & Barto (1998) Reinforcement learning, Section 2.6
Estes (1957) Theory of learning with constant, variable, or contingent probabilities of reinforcement
Anderson (1960) Effects of first-order conditional probability in a two-choice learning situation
|Matt Wilder, Dan Knights
||Mozer & Jones
||Gallistel et al.(2001) The rat approximates an ideal detector of changes in rates of reward
||Sugrue, Corrado, & Newsome (2004). Matching behavior and the representation of value in the parietal cortex. Science, 304, 1782-1787.
||Spacing of Practice
|Landauer, T K (1986) How Much Do People Remember? Some Estimates of the Quantity of Learned Information in Long-term Memory, Cognitive Science 10, 477-493.
Landauer, T. K. (1975) Memory Without Organization: Properties of a
Model with Random Storage and Undirected Retrieval, Cognitive
Psychology, 7, 495-531.
||Behrens et al. (2007) Learning the value of information in an uncertain world
||Supplementary material for article
||Cuthill, Kacelnik, Krebs, Haccou, & Iwasa (1990) Starlings exploiting patches: The effect of recent experience on foraging decisions
Real, L. A. (1991). Animal choice behavior and the evolution of cognitive architecture
Dukas & Real (1993) Effects of nectar variance on learning by bumble bees
|Tres Spicher (Cuthill et al., 1990); Holger Dick (Real, 1991); Ron Le Bel (Dukas & Real, 1993)
Chapman (1991) Trial order affects cue interaction in coningency judgment
||Lopez, Shanks, Almaraz, & Fernandez (1998) Effects
of trial order on contingency judgments: a comparison of associative and
probabilistic contrast accounts
||Kelsey Anderson (Lopez et al.)
||Matute, Vegas, & Marez (2002) Flexible use of recent information in causal and predictive judgments
|4/15 (Mozer away)
||Jones & Sieck (2003). Learning myopia: An adaptive recency effect in category learning
||Stewart, Brown, & Chater (2002) Sequence effects in categorization of simple perceptual stimul
|4/17 (Mozer away)
||Jones, Love, & Maddox (2006) Recency effects as a window to generalization
||Jones, Maddox, & Love (2006) Stimulus generalization in category learning
||Jones (main reading); Hadjar Homaei (supplementary paper)
||Sakamoto, Jones, & Love (2008) Putting the psychology back into psychological models: Mechanistic vs. rational approaches
||Nosofsky, Kruschke, &
McKinley (1992) Combining
exemplar-based category representations and connectionist learning rules
Hadjar Homaei (Nosofsky et al.)
||Anderson, J. R. & Schooler, L. (1991) Reflections of the environment in memory, Psychological Science, 2, 396-408.
||Anderson, Tweney, Rivardo, & Duncan (1997) Need probability affects retention: A direct demonstration
||Tres Spicher (Anderson & Schooler, 1991); Braden Wright (Anderson et al., 1997)
Brown, Steyvers, & Hemmer (2007). Modeling experimentally induced strategy shifts
Kruschke, J. (2006). Locally Bayesian learning.
Brown S, Steyvers M. (2005). The dynamics of experimentally induced criterion shifts
|Ron Le Bel (Brown et al., 2007); Hadjar Homaei (Kruschke, 2006)
||Gilden (2001) Cognitive emissions of 1/f noise
||Sanborn & Griffiths. (2008). MCMC with people.
Kello, Beltz, Holden, & van Orden (2007) The
emergent coordination of cognitive function
|Mark Lewis-Pranzen (Gilden, 2001); Adam Bates (Sanborn & Griffiths)
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
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.
Huettel, S. A., & Lockwood, G. R. (1999). Range effects of an irrelevant dimension on classification
. Perception & Psychophysics
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,
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
Probability & Reinforcement Learning
Flood, MM (1954). Environmental non-stationarity in a sequential decision-making experiment. (Hardcopy)
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)
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
Vlaev, I., Chater, N., & Stewart, N. (2007b). Relativistic financial
decisions: Context effects on retirement saving and investment risk
preferences. Judgment and Decision Making, 2,
Vlaev, I., Chater, N., & Stewart, N. (2007a). Financial prospect
relativity: Context effects in financial decision-making under risk
Journal of Behavioral Decision Making, 20,
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