Cognitive Modeling Greatest Hits
This is a list of cognitive modeling papers solicited from a wide range
of cognitive modelers, by asking them the following: "I wonder if you would do me the honor of
sending me a list of your top 2-5 favorite cognitive modeling papers. I
would expect that 1-3 of these would be your papers, and 1-3 would be
someone else's. I am looking for papers where someone really nailed the
phenomenon, whatever it is. I would lean towards more recent papers,
but oldies but goodies are ok too."
At the bottom of this list are some of the comments received with the
papers, organized by the name of the respondent. Please let me know if
any of the links are broken: gary@ucsd.edu
Copyright notice
Abstracts, papers, chapters, and other documents are posted on this
site as an efficient way to distribute reprints. The respective authors
and publishers of these works retain all of the copyrights to this
material. Anyone copying, downloading, bookmarking, or printing any of
these materials agrees to comply with all of the copyright terms. Other
than having an electronic or printed copy for fair personal use, none
of these works may be reposted, reprinted, or redistributed without the
explicit permission of the relevant copyright holders.
- Allopenna, P.D., Magnuson, J.S., & Tanenhaus, M.K. (1998).
Tracking
the time course of spoken word recognition using eye movements:
Evidence for continuous mapping models. Journal of Memory and
Language, 38(4), 419–439. pdf
- Anderson, J.R. (1991). Is human cognition adaptive? Behavioral
and
Brain Sciences, 14, 471-484. pdf
- Anderson, J.R. (1991). The Adaptive Nature of Human
Categorization. Psychological
Review, 98(3), 409-429. pdf
- Anderson, J.R., & Milson, R. (1989). Human Memory: An
Adaptive
Perspective. Psychological Review, 96(4), 703-719. pdf
- Ashby, F.G., & Alfonso-Reese, L. (1995). Categorization as
probability density estimation. Journal of Mathematical Psychology,
39, 216-233.
pdf
- Baayen, R.H. (2011). Corpus linguistics and naive discriminative
learning. Submitted to Brazilian Journal of Applied Linguistics.
pdf
- Baayen, R.H., & Hendrix, P. (2011). Sidestepping the
combinatorial
explosion: Towards a processing model based on discriminative learning.
Abstract for the LSA workshop: Empirically examining parsimony and
redundancy in usage-based models, January 2011. pdf
- Barrington, L., Marks, T.K., Hsiao, J.H.-W., & Cottrell, G.W.
(2008). NIMBLE: A kernel density model of saccade-based visual memory. Journal
of Vision, 8(14):17, 1-14. pdf
- Beck, J., Ma, W.J., Kiani, R., Hanks, T., Churchland, A.K.,
Roitman,
J., Shadlen, M.N, Latham, P.E., & Pouget, A. (2008). Probabilistic
population codes for Bayesian decision making. Neuron, 60,
1142-1152. pdf
- Botvinick, M., & Plaut, D.C. (2004). Doing Without Schema
Hierarchies: A Recurrent Connectionist Approach to Normal and Impaired
Routine Sequential Action. Psychological Review, 111(2),
395-429. pdf
- Brown, S.D., & Heathcote, A. (2008). The simplest complete
model of
choice reaction time: Linear ballistic accumulation. Cognitive
Psychology, 57, 153-178. pdf
- Brown, G.D.A., Neath, I., & Chater, N. (2007). A Temporal
Ratio
Model of Memory. Psychological Review, 114(3), 539-576. pdf
- Brown, S.D., & Steyvers, M. (2009). Detecting and Predicting
Changes. Cognitive Psychology, 58, 49-67. pdf
- Cadieu, C., Kouh, M., Pasupathy, A., Conner, C., Riesenhuber, M.,
&
Poggio, T.A. (2007). A
Model of V4 Shape Selectivity and Invariance. J Neurophysiol, 98,
1733-1750. pdf
- Chang, F., Dell, G.S., & Bock, K. (2006). Becoming Syntactic.
Psychological
Review, 113(2), 234-272. pdf
- Christiansen, M.H., Allen, J. & Seidenberg, M.S.
(1998). Learning to segment speech using multiple cues: A connectionist
model. Language and Cognitive Processes, 13, 221-268. pdf
- Christiansen, M.H., & Chater, N. (2001). Connectionist
Psycholinguistics: Capturing the Empirical Data. Trends in
Cognitive Sciences, 5(2), 82-88. pdf
- Christiansen, M.H. & Chater, N. (1999). Toward a
connectionist model of recursion in human linguistic performance.
Cognitive Science, 23, 157-205. pdf
- Clark, H.H. (1973). The Language-as-Fixed-Effect Fallacy:
A Critique of
Language statistics in Psychological Research. Journal of Verbal
Learning and Verbal Behavior, 12, 335-359. pdf
- Cleeremans, A., & McClelland, J.L. (1991). Learning the
structure
of event sequences. Journal of Experimental Psychology: General, 120,
235-253. pdf
- Cottrell, G.W., Branson, K., and Calder, A. J. (2002) Do
expression and identity need separate representations? In Proceedings of the 24th Annual Cognitive
Science Society Conference, Fairfax, Va. pdf
- Cottrell, G.W., & Plunkett, K. (1994). Acquiring the mapping
from meanings to sounds.Connection Science, 6(4), 379-412. pdf
- Cowell, R.A., Bussey, T.J., & Saksida, L.M. (2006). Why does
brain
damage impair memory? A connectionist model of object recognition
memory in perirhinal cortex. Journal of Neuroscience, 26(47),
12186-12197. pdf
- Criss, A.H., & McClelland, J.L. (2006). Differentiating the
differentiation models: A comparison of the retrieving effectively from
memory model (REM) and the subjective likelihood model (SLiM). Journal
of Memory and Language, 55, 447-460. pdf
- Daw, N.D., O'Doherty, J.P., Dayan, P., Seymour, B., & Dolan,
R.J.
(2006). Cortical substrates for exploratory decisions in humans. Nature,
44, 876-879. pdf
- Daw, N.D., Niv, Y., & Dayan, P. (2005). Uncertainty-based
competition between prefrontal and dorsolateral striatal systems for
behavioral control. Nature Neuroscience, 8(12), 1704-1711. pdf (paper) pdf (supplement)
- Dawson, M.R.W. (1991). The How and Why of What Went Where in
Apparent
Motion: Modeling Solutions ot the Motion Correspondence Problem. Psychological
Review, 98(4), 569-603. pdf
- Dell, G.S., Burger, L.K., & Svec, W.R. (1997). Language
Production
and Serial Order: A Functional Analysis and a Model. Psychological
Review, 104(1), 123-147. pdf
- Dell, G.S., Schwartz, M.F., Martin, N., Saffran, E.M., &
Gagnon,
D.A. (1997) Lexical Access in Aphasic and Nonaphasic Speakers. Psychological
Review, 104(4), 801-838. pdf
- Dennis, S., & Humphreys, M.S. (2001). A context noise model
of
episodic word recognition.Psychological Review, 108(2), 452-478.
pdf
- Elman, J.L. (1990). Finding Structure in Time. Cognitive
Science, 14,
179-211. pdf
- Elman, J.L. (1991). Distributed Representations, Simple Recurrent
Networks and
Grammatical structure. Machine Learning, 7, 195-225. pdf
- Elman, J.L. (1993). Learning and development in neural networks:
The
importance of starting small. Cognition, 48(1), 71-99. pdf
- Fific, M., Little, D.R., & Nosofsky, R.M. (2010).
Logical-Rule
Models of Classification Response Times: A Synthesis of
Mental-Architecture, Random-Walk, and Decision-Bound Approaches. Psychological
Review, 117,(2), 309-348. pdf
- Frank, T.D., van der Kamp, J., & Savelsbergh, G.J.P. (2010).
On a
multistable dynamic model of behavioral and perceptual infant
development. Developmental Psychobiology, 52, 352–371. pdf
- French, R.M., Mareschal, D., Mermillod, M., & Quinn, P.C.
(2004).
The Role of Bottom-Up Processing in Perceptual Categorization by 3- to
4-Month-Old Infants: Simulations and Data. Journal of Experimental
Psychology: General, 133(3), 382-397. pdf
- Gao, J., Tortell, R., & McClelland, J.L. (2011). Dynamic
Integration of Reward and Stimulus Information in Perceptual
Decision-Making. PloS One, 6(3), 1-21. pdf
- Goldstein, D.G., & Gigerenzer, G. (2002). Models of
ecological
rationality: The Recognition Heuristic. Psychological Review, 109(1),
75-90. pdf
- Grant, D.A. (1962). Testing The Null Hypothesis and the Strategy
and
Tactics of Investigating Theoretical Models. Psychological Review,
69(1), 54-61. pdf
- Griffiths, T.L., Steyvers, M., & Firl, A. (2007). Google and
the
Mind: Predicting Fluency With PageRank. Psychological Science, 18(12),
1069-1076. pdf
- Griffiths, T.L., Steyvers, M., & Tenenbaum, J.B. (2007).
Topics in
Semantic Representation. Psychological Review, 114(2), 211-244.
pdf
- Griffiths, T.L., & Tenenbaum, J.B. (2006). Optimal
predictions in
everyday cognition. Psychological Science, 17(9), 767–773. pdf
- Gupta, P. (2008). The Role of Computational Models in
Investigating
Typical and Pathological Behaviors. Seminars in Speech and
Language, 29(3), 211-225. pdf
- Gureckis, T.M., & Love, B.C. (2010) Direct Associations or
Internal
Transformations? Exploring the Mechnisms Underlying Sequential Learning
Behavior. Cognitive Science, 34, 10-50. pdf
- Hahn, U., & Nakisa, R.C. (2000). German Inflection: Single
Route or
Dual Route?. Cognitive Psychology, 41, 313-360. pdf
- Henson, R.N.A. (1998). Short-term memory for serial order: The
start-end model. Cognitive Psychology, 36, 73-137. pdf
- Hinton, G.E. (1986). Learning distributed representations of
concepts.
In Proceedings of the Eighth Annual Conference of the Cognitive
Science Society, Amherst, MA.
- Hinton, G.E., & Nowlan, S.J. (1987). How Learning Can Guide
Evolution. Complex Systems, 1, 495-502. pdf
- Hintzman, D.L. (1986). "Schema abstraction" in a Multiple-Trace
Memory
Model. Psychological Review, 93, 411-428. pdf
- Hopfield, J.J. (1982).Neural networks and physical systems with
emergent collective computational abilities. Proc. Natl. Acad. Sci.
USA, 79, 2554-2558.
pdf
- Hsiao, J.H.-W., Shahbazi, R. & Cottrell, G.W. (2008).
Hemispheric
Asymmetry in Visual Perception Arises from Differential Encoding beyond
the Sensory Level. In Proceedings of the 30th Annual Meeting of the
Cognitive Science Society. pdf
- Huber, D.E., Shiffrin, R.M., Lyle, K.B., & Ruys, K.I. (2001).
Perception and preference in short-term word priming. Psychological
Review, 108, 149-182. pdf
- Jiang, X., Rosen, E., Zeffiro, T., VanMeter, J., Blanz, V., &
Riesenhuber, M. (2006). Evaluation of a Shape-Based Model of Human Face
Discrimination Using fMRI and Behavioral Techniques. Neuron, 50,
159-172. pdf
- Johns, B.T., & Jones, M.N. (2010). Evaluating the random
representation assumption of lexical semantics in cognitive models. Psychonomic
Bulletin & Review, 17, 662-672. pdf
- Jones, M., & Love, B. (2010). Bayesian Fundamentalism or
Enlightenment? On the Explanatory Status and Theoretical Contributions
of Bayesian Models of Cognition (Unpublished Draft). Behavioral and
Brain Sciences. pdf
- Jones, M.N., & Mewhort, D.J.K. (2007). Representing word
meaning
and order information in a composite holographic lexicon. Psychological
Review, 114, 1-37. pdf
- Jordan, M.I. (1986). Serial Order: A parallel distributed
processing
approach. UCSD Cognitive Science Technical Report 8604. pdf OCRed pdf (pages straightened!)
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Supervised
learning with a distal teacher. Cognitive Science, 16, 307-354.
pdf
- Kanan, C.M., Tong, M.H., Zhang, L., & Cottrell, G.W. (2009).
SUN:
Top-down saliency using natural statistics. Visual Cognition, 17(6-7),
979-1003. pdf
- Kanerva, P. (1985) Parallel Structures in Human and Computer
Memory. Cognitiva
85, Paris, France. pdf
- Kelso, J.A.S. (2008). Haken-Kelso-Bunz model. Scholarpedia,
3(10):1612. html
- Kemp, C., & Tenenbaum, J.B. (2008). The discovery of
structural
form. Proc. Natl. Acad. Sci. U.S.A., 105, 10687–10692. pdf
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overhypotheses with hierarchical Bayesian methods. Developmental
Science: Bayesian Special Section, 10(3), 307-321. pdf
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correct
feature maps. Biological Cybernetics, 43, 59-69. pdf
- Kording, K.P., Tenenbaum, J.B., & Shadmehr, R. (2007). The
dynamics
of memory as a consequence of optimal adaptation to a changing body. Nature
Neuroscience, 10, 779–786. pdf
- Kruschke, J.K. (1992). ALCOVE: An Exemplar-Based Connectionist
Model of
Category Learning. Psychological Review, 99(1), 22-44 pdf
- Kruschke, J.K. (2006). Locally Bayesian Learning with
Applications to
Retrospective Revaluation and Highlighting. Psychological Review,
113(4), 677-699. pdf
- Landauer, T.K., & Dumais, S.T. (1997). A solution to Plato's
problem: The Latent Semantic
Analysis theory of the acquisition, induction, and representation of
knowledge. Psychological Review, 104, 211-240. pdf
- Larkey, L.B., & Love, B.C. (2003). CAB: Connectionist analogy
builder. Cognitive Science, 27, 781-794. pdf
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and
asymmetry in visual search. Proc. Natl. Acad. Sci. USA, 96,
10530-10535. pdf
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development in a self-organizing neural network. Neural Networks, 17,
1345–1362. pdf
- Ma, W.J., Beck, J.M., Latham, P.E., & Pouget, A. (2006).
Bayesian
inference with probabilistic population codes. Nature Neuroscience,
9(11), 1432-1438. pdf (paper) pdf (supplement) pdf (review)
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working memory: A comment on Just & Carpenter (1992) and Waters
& Caplan (1996). Psychological Review, 109, 35-54. pdf
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- McCleery, J.P., Zhang, L. Ge, L. Wang, Z., Christiansen,
E.M., Lee, K., and Cottrell, G.W. (2008) The roles of visual expertise
and visual input in the face inversion effect: Behavioral and
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- McClelland, J.L. (2009). The place of modeling in cognitive
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Speech
Perception. Cognitive Psychology, 18, 1-86. pdf
- McClelland, J.L., McNaughton, B.L., & O'Reilly, R.C. (1995).
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There Are Complementary Learning Systems in the Hippocampus and
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Activation Model of Context Effects in Letter Perception: Part 1. An
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distributed
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Modeling the
influence of thematic fit (and other constraints) in on-line sentence
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pdf
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control in
a composite holographic associative recall model: Implications for
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Impairments in
a Self-Organizing Feature Map Model of the Lexicon. Brain and
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"Computation
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V.L., Mason, R.A., & Just, M.A. (2008). Predicting Human Brain
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arbitrariness of the sign: Learning advantages from the structure of
the vocabulary. Journal of Experimental Psychology: General. pdf
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Remarks by Researchers in Cognitive Science
Morten Christiansen
- Dell et al (1997): "A brilliant example of how
models can be used to
make predictions for new data collection (for specific individuals)!"
- St. Clair et al (2010): "A recent paper of mine
exploring limitations
of a past model (Mintz) and proposing a new model of how kids might
learn about lexical categories (combining corpus analyses of
child-directed speech and connectionist models)."
- MacDonald, M.C. & Christiansen, M.H. (2002).
Reassessing working
memory: A comment on Just & Carpenter (1992) and Waters &
Caplan (1996). Psychological Review, 109, 35-54. "showed how working
memory capacity effects can be explained by experience with language"
- Christiansen, M.H., Allen, J. & Seidenberg,
M.S. (1998). Learning to segment speech using multiple cues: A
connectionist model. Language and Cognitive Processes, 13, 221-268.
"first comprehensive multiple-cue integration model in the context of
word segmentation"
- Christiansen, M.H. & Chater, N. (1999). Toward
a connectionist model of recursion in human linguistic performance.
Cognitive Science, 23, 157-205.
"A model of complex recursion before Hauser, Chomsky & Fitch made
the topic popular again"
Axel Cleeremans
- Elman (1990), Cleeremans & McClelland (1991),
and Munakata et al
(1997): "All of these have to do with the SRN and in each case, I feel
that something has been nailed indeed: the general idea and power of
limited recurrence in the first paper; the application to sequence
learning in the second; and to cognitive development in the third."
- Hinton (1986): "This is the first connectionist
paper I read. This is
truly seminal I think in showing key concepts from distributed
representations to functional similarity as well as from analyzing
hidden units activity to watching abstract concepts emerge out of mere
processing."
- Plaut & Shallice (2003): "This is a truly
insightful paper about
how you can get double, graded dissociations out of a single system. I
always use it in class as an illustration of the pitfalls of standard
neuropsychological thinking."
Gary Dell
- My current favorites are Elman (1991, grammatical structure SRN
paper); Plaut & Shallice (1993, Cog Neuroscience), and McClelland,
McNaughton, & O'Reilly (1995). For my papers, I
like Chang, Dell & Bock, 2006) and Dell, Burger & Svec (1997).
- As for the paper that you've requested (Dell, Schwartz et al.,
1997), the pdf is already on your list, as are these other papers. So,
I'm afraid that I can't really expand on your list. I'm still an old
connectionist fogey.
Simon Dennis
- Dennis & Humphreys (2001): "This is the paper
that nailed how they
ought to be modified ;-)."
- Elman (1991): "It was just such a different way of
looking at language
structure and really emphasised the power of statistics."
- Henson (1998): "Conclusively shows that simple
chaining models cannot
be an accurate
portrayal of serial recall and proposes the Start End model."
- Landauer & Dumais (1997): "This one also was
just very surprising.
It taught me that toy examples are not good enough. Sometimes what we
think is a complicated process is really just big data. And you can't
see it if your corpus is restricted to 'man eats. woman eats ...'."
- Ratcliff et al (1990) and Shiffrin et al (1990):
"It isn't often that
one really has to concede defeat and move on. There is normally some
kind of wiggle room. The List Strength Effect just didn't give any. The
Global Matching Models were all demonstrably wrong and had to be
fundamentally modified."
Robert French
- French et al (2004): "This paper has had a fair
amount of success and
clearly illustrates, I think, the importance of modeling as a tool for
understanding human behavior."
- Kanerva (1985): "This is a simple, absolutely clear
presentation of
sparse distributed memory. In fact it is the clearest description of
SDM that I know of. His book, Sparse Distributed Memory, "mathematized"
everything in any attempt to put it all on a formal footing and, in so
doing, lost a lot of potential fans. This paper is simple, clear, and
lends itself perfectly to implementation. It's impossible to find on
the Web, however."
David Huber
- Nosofsky (1986): "I think there's no better example
of a model that
nailed the phenomenon of interest."
- Nosofsky (1984): "His GCM model gave rise to
everything that has
followed in the study of categorization and it's still a valid
contender to this day. But, more importantly, it changed the way that
people model by forcing them to consider both representation (as
revealed by MDS in this case) and the process (feature attention)
simultaneously. Subsequently, John Kruschke demonstrated that GCM is
mathematically identical to his ALCOVE neural network model."
Bradley Love
- Roberts & Pashler (2000): "This paper is a good
conceptual overview
of why fit is not enough and motivates model selection statistics
(proper model testing)."
- Sanborn et al (2010): "This paper is a really nice
linkage of process
and rational models with intuitive explanations of Gibbs sampling and
particle filters"
- Daw et al (2006): "This is a good introduction to
Reinforcement Leaning
(RL) models and using cognitive models to interpret fMRI data."
- Mitchell et al (2008): "This is a fun paper with
cool twist on 'mind
reading'."
- Shiffrin et al (2008): "This is a nice and easy to
understand overview
of Bayesian methods (model selection)."
Michael Mozer
- McClelland & Rumelhart (1981): "It's a classic,
and probably nobody
covers it anymore, and it probably wins an award as the model that was
recycled more times than any other-with its units relabeled-to explain
other phenomena."
- Najemnik & Geisler (2005): "It takes some
decoding to figure out
but it seems like a really pretty and believable Bayesian account that
takes into account limitations of the visual system (falloff of acuity
with retinal eccentricity)."
Richard Shiffrin
- Huber et al (2001): "The model kept predicting
correctly in study after
study even though our intuitions kept leading us to expect other
results."
Michael Spivey
- Allopenna et al (1998): "They used McClelland and
Elman's (1986) TRACE
interactive-activation model to make remarkable time-course predictions
of eye-movement patterns during spoken word recognition in a visual
context."
- McRae et al (1998): "We built a normalized version
of an
interactive-activation network to simulate the nonlinearities inherent
in "the garden-path effect", where syntactically ambiguous sentences
cause slowed reading times, due to a constraint-satisfaction process
(rather than a stage-based modular syntax process)."
Angela Yu
Wanted to add
Dayan, P, Hinton, GE, Neal, RM & Zemel, RS
(1995).
The Helmholtz machine. Neural
Computation, 7,
889-904. pdf
but I did not
include it because
it is not modeling any particular data. Angela's response: "Personally
I feel like it is an important theoretical constructs worth
teaching/learning. After all, any model can fit data, whether
good or bad, bit only a few (good ones) can generalize across
experimental settings and levels of analysis."