Cognitive Modeling Greatest Hits

"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."

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

(4), 419–439. pdf

Anderson, J.R. (1991). Is human cognition adaptive? Behavioral and Brain Sciences, 14 , 471-484. pdf

, 471-484. pdf

Anderson, J.R. (1991). The Adaptive Nature of Human Categorization. Psychological Review, 98 (3), 409-429. pdf

(3), 409-429. pdf

Anderson, J.R., & Milson, R. (1989). Human Memory: An Adaptive Perspective. Psychological Review, 96 (4), 703-719. pdf

(4), 703-719. pdf

Ashby, F.G., & Alfonso-Reese, L. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39 , 216-233. pdf

, 216-233. pdf

Baayen, R.H. (2011). Corpus linguistics and naive discriminative learning. Submitted to Brazilian Journal of Applied Linguistics . pdf

. 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

, 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

(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

, 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

(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

, 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

(3), 539-576. pdf

Brown, S.D., & Steyvers, M. (2009). Detecting and Predicting Changes. Cognitive Psychology, 58 , 49-67. pdf

, 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

, 1733-1750. pdf

Chang, F., Dell, G.S., & Bock, K. (2006). Becoming Syntactic. Psychological Review, 113 (2), 234-272. pdf

(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

(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

(47), 12186-12197.

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

, 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

, 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)

(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

(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

(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

(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

(2), 452-478. pdf

Elman, J.L. (1990). Finding Structure in Time. Cognitive Science, 14 , 179-211. pdf

, 179-211. pdf

Elman, J.L. (1991). Distributed Representations, Simple Recurrent Networks and Grammatical structure. Machine Learning, 7 , 195-225. pdf

, 195-225. pdf

Elman, J.L. (1993). Learning and development in neural networks: The importance of starting small. Cognition, 48 (1), 71-99. pdf

(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

,(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

, 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

(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

(3), 1-21. pdf

Goldstein, D.G., & Gigerenzer, G. (2002). Models of ecological rationality: The Recognition Heuristic. Psychological Review, 109 (1), 75-90. pdf

(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

(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

(12), 1069-1076. pdf

Griffiths, T.L., Steyvers, M., & Tenenbaum, J.B. (2007). Topics in Semantic Representation. Psychological Review, 114 (2), 211-244. pdf

(2), 211-244. pdf

Griffiths, T.L., & Tenenbaum, J.B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17 (9), 767–773. pdf

(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

(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

, 10-50. pdf

Hahn, U., & Nakisa, R.C. (2000). German Inflection: Single Route or Dual Route?. Cognitive Psychology, 41 , 313-360. pdf

, 313-360. pdf

Henson, R.N.A. (1998). Short-term memory for serial order: The start-end model. Cognitive Psychology, 36 , 73-137. pdf

, 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.

, Amherst, MA.

Hinton, G.E., & Nowlan, S.J. (1987). How Learning Can Guide Evolution. Complex Systems, 1 , 495-502. pdf

, 495-502. pdf

Hintzman, D.L. (1986). "Schema abstraction" in a Multiple-Trace Memory Model. Psychological Review, 93 , 411-428. pdf

, 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

, 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

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

, 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

, 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

, 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

. 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

, 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!)



Jordan, M.I., & Rumelhart, D.E. (1992). Forward models: Supervised learning with a distal teacher. Cognitive Science, 16 , 307-354. pdf

, 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

(6-7), 979-1003. pdf

Kanerva, P. (1985) Parallel Structures in Human and Computer Memory. Cognitiva 85 , Paris, France. pdf

, Paris, France. pdf

Kelso, J.A.S. (2008). Haken-Kelso-Bunz model. Scholarpedia , 3(10):1612. html

, 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

, 10687–10692. pdf

Kemp, C., Perfors, A., & Tenenbaum, J.B. (2007). Learning overhypotheses with hierarchical Bayesian methods. Developmental Science: Bayesian Special Section, 10 (3), 307-321. pdf

(3), 307-321. pdf

Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43 , 59-69. pdf

, 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

, 779–786. pdf

Kruschke, J.K. (1992). ALCOVE: An Exemplar-Based Connectionist Model of Category Learning. Psychological Review, 99 (1), 22-44 pdf

(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

(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

, 211-240. pdf

Larkey, L.B., & Love, B.C. (2003). CAB: Connectionist analogy builder. Cognitive Science, 27 , 781-794. pdf

, 781-794. pdf

Li, Z. (1999). Contextual influences in V1 as a basis for pop out and asymmetry in visual search. Proc. Natl. Acad. Sci. USA, 96 , 10530-10535. pdf

, 10530-10535. pdf

Li, P., Farkas, I., & MacWhinney, B. (2004). Early lexical development in a self-organizing neural network. Neural Networks, 17 , 1345–1362. pdf

, 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)

(11), 1432-1438. pdf (paper) pdf (supplement) pdf (review)

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. pdf



MacWhinney, B., & Li, P (2008). Neurolinguistic Computational Models. In B. Stemmer & H. Whitaker (Eds.), Handbook of the neuroscience of language (pp. 229-236). London: Academic Press pdf



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 neurocomputational evidence Vision Research 48 :703-715. pdf



:703-715. pdf

McClelland, J.L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1 (1), 11-38. pdf

(1), 11-38. pdf

McClelland, J.L., McNaughton, B.L., & O'Reilly, R.C. (1995). Why There Are Complementary Learning Systems in the Hippocampus and Neocortex: Insights From the Successes and Failures of Connectionist Models of Learning and Memory. Psychological Review, 102 (3), 419-457. pdf

(3), 419-457. pdf

McClelland, J.L., & Elman, J.L. (1986). The TRACE Model of Speech Perception. Cognitive Psychology, 18 , 1-86. pdf

, 1-86. pdf

McClelland, J.L., McNaughton, B.L., & O'Reilly, R.C. (1995). Why There Are Complementary Learning Systems in the Hippocampus and Neocortex: Insights From the Successes and Failures of Connectionist Models of Learning and Memory. Psychological Review, 102 (3), 419-457. pdf

(3), 419-457. pdf

McClelland, J.L., & Rumelhart, D.E. (1981). An Interactive Activation Model of Context Effects in Letter Perception: Part 1. An Account of Basic Findings. Psychological Review, 88 (5), 375-407. pdf

(5), 375-407. pdf

McClelland, J.L., & Rogers, T.T. (2003). The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience, 4 , 310-322. pdf

, 310-322. pdf

McRae, K., de Sa, V.R., & Seidenberg, M.S. (1993). Modeling Property Intercorrelations in Conceptual Memory. In Proceedings of the 15th Annual Meeting of the Cognitive Science Society , 729-734. pdf

, 729-734. pdf

McRae, K., de Sa, V.R., & Seidenberg, M.S. (1997). On the Nature and Scope of Featural Representations of Word Meaning. Journal of Experimental Psychology: General, 126 (2), 99-130. pdf

(2), 99-130. pdf

McRae, K., Spivey-Knowlton, M.J., Tanenhaus, M.K. (1997). Modeling the influence of thematic fit (and other constraints) in on-line sentence comprehension. Journal of Memory and Language , 38 , 283-312. pdf

, , 283-312. pdf

Metcalfe, J. (1993). Novelty monitoring, metacognition, and control in a composite holographic associative recall model: Implications for Korsakoff amnesia. Psychological Review, 100 (1), 3-22. pdf

(1), 3-22. pdf

Miikkulainen, R. (1997). Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon. Brain and Language, 59 , 334–366. pdf

, 334–366. pdf

Mitchell, M. (1998). Complex-Systems Perspective on the "Computation vs. Dynamics" debate in Cognitive Science. In M.A. Gernsbacher, & S.J. Derry (Eds.), Proceedings of the Twentieth Annual Conference of the Cognitive Science Society . Mahwah, NJ: Lawrence Erlbaum Associates. pdf

. Mahwah, NJ: Lawrence Erlbaum Associates. pdf

Mitchell, T.M., Shinkareva, S.V., Carlson, A., Chang, K.-M., Malave, V.L., Mason, R.A., & Just, M.A. (2008). Predicting Human Brain Activity Associated with the Meanings of Nouns. Science, 320 , 1191-1195. pdf (paper) pdf (supplement) website (supplement)

, 1191-1195. pdf (paper) pdf (supplement) website (supplement)

Monaghan, P., Christiansen, M.H., & Fitneva, S.A. (2011). The arbitrariness of the sign: Learning advantages from the structure of the vocabulary. Journal of Experimental Psychology: General . pdf

. pdf

Montague, PR, Dayan, P & Sejnowski, TK (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience, 16, 1936-1947. pdf



Mozer, M.C. (2002). Frames of Reference in Unilateral Neglect and Visual Perception: A Computational Perspective. Psychological Review, 109 (1), 156-185. pdf

(1), 156-185. pdf

Munakata, Y., McClelland, J.L., Johnson, M.H., & Siegler, R.S. (1997). Rethinking Infant Knowledge: Toward an Adaptive Process Account of Successes and Failures in Object Permanence Tasks. Psychological Review, 104 (4), 686-713. pdf

(4), 686-713. pdf

Najemnik, J., & Geisler, W.S. (2005). Optimal eye movement strategies in visual search. Nature, 434 , 387-391. pdf

, 387-391. pdf

Nolfi, S., Elman, J.L., & Parisi, D. (1994). Learning and evolution in neural networks. Adaptive Behavior, 3 , 5-28. pdf

, 5-28. pdf

Norman, K.A. & O'Reilly, R.C. (2003). Modeling Hippocampal and Neocortical Contributions to Recognition Memory: A Complementary Learning Systems Approach. Psychological Review, 110 , 611-646. pdf

, 611-646. pdf

Nosofsky, R.M. (1984). Choice, Similarity, and the Context Theory of Classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10 (1), 104-114. pdf

(1), 104-114. pdf

Nosofsky, R.M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115 (1), 39-57. pdf

(1), 39-57. pdf

Nosofsky, R.M., & Palmeri, T.J. (1997). An Exemplar-Based Random Walk Model of Speeded Classification. Psychological Review, 104 (2), 266-300. pdf

(2), 266-300. pdf

Nosofsky, R.M., & Palmeri, T.J. (1998). A rule-plus-exception model for classifying objects in continuous-dimension spaces. Psychonomic Bulletin & Review, 5 (3), 345-369. pdf

(3), 345-369. pdf

Oaksford, M. & Chater N. (1994). A Rational Analysis of the Selection Task as Optimal Data Selection. Psychological Review, 101 (4), 608-631. pdf



(4), 608-631. pdf

Oaksford, M., & Chater, N. (2009). Précis of bayesian rationality: The probabilistic approach to human reasoning. Behavioral and Brain Sciences, 32 (1), 69-120. pdf



(1), 69-120. pdf

O'Doherty, J.P., Hampton, A., & Kim, H. (2007) Model-Based fMRI and Its Application to Reward Learning and Decision Making. Ann. N. Y. Acad. Sci., 1104 , 35–53. pdf

, 35–53. pdf

O'Reilly, R.C. & Frank, M.J. (2006). Making Working Memory Work: A Computational Model of Learning in the Frontal Cortex and Basal Ganglia. Neural Computation, 18 , 283-328. pdf

, 283-328. pdf

O’Toole, A.J., Deffenbacher, K.A., Valentin, D., & Abdi, H. (1994). Structural aspects of face recognition and the other-race effect. Memory and Cognition, 22 (2), 208-224. pdf

(2), 208-224. pdf

Otto, A.R., & Love, B.C. (2010). You don't want to know what you're missing: When information about forgone rewards impedes dynamic decision making. Judgment and Decision Making, 5 (1), 1-10. pdf

(1), 1-10. pdf

Palmeri, T.J. (1999). Learning Categories at Different Hierarchical Levels: A Comparison of Category Learning Models. Psychonomic Bulletin & Review . pdf

. pdf

Palmeri, T.J., & Gauthier, I. (2004). Visual object understanding. Nature Reviews Neuroscience, 5 , 291-303. pdf

, 291-303. pdf

Plaut, D.C., McClelland, J.L., Seidenberg, M.S., & Patterson, K. (1996). Understanding Normal and Impaired Word Reading: Computational Principles in Quasi-Regular Domains. Psychological Reviews, 103 , 56-105. pdf

, 56-105. pdf

Plaut, D.C., & Shallice, T. (1993). Deep dyslexia: A case study of connectionist neuropsychology. Cognitive Neuropsychology, 10 (5), 377-500. pdf

(5), 377-500. pdf

Pouget, A, Dayan, P & Zemel, RS (2003) Inference and computation with population codes. Annual Review of Neuroscience 26 , 381-410. pdf



Purcell, B.A., Heitz, R.P., Cohen, J.Y., Schall, J.D., Logan, G.D., & Palmeri, T.J. (2010). Neurally Constrained Modeling of Perceptual Decision Making. Psychological Review, 117 (4), 1113-1143. pdf

(4), 1113-1143. pdf

Raaijmakers, J.G.W., & Shiffrin, R.M. (1980). SAM: A theory of probabilistic search of associative memory. In G.H. Bower (Ed.), The Psychology of Learning and Motivation, 14 , 207-262. New York: Academic Press. pdf

, 207-262. New York: Academic Press. pdf

Raaijmakers, J.G.W., & Shiffrin, R.M. (1981). Search of associative memory. Psychological Review, 88 , 93-134. pdf

, 93-134. pdf

Ramscar, M., Dye, M., Popick, H.M. & O’Donnell-McCarthy, F. (2010) How children learn to value numbers: Information structure and the acquisition of numerical understanding. pdf



Ramscar, M., Suh, E., & Dye, M. (2010). For the price of a song: How pitch category learning comes at a cost to absolute frequency information. pdf



Ramscar, M., Yarlett, D., Dye, M., Denny, K., & Thorpe, K. (2010) The Effects of Feature-Label-Order and their implications for symbolic learning. Cognitive Science, 34 (6), 909-957. pdf

(6), 909-957. pdf

Ratcliff, R., Clark, S.E., & Shiffrin, R.M. (1990). List-Strength Effect .1. Data and Discussion. Journal of Experimental Psychology-Learning Memory and Cognition, 16 (2), 163-178. html (abstract)

(2), 163-178. html (abstract)

Reed, S.K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3 , 382-407. pdf

, 382-407. pdf

Ritter, F.E. (2003) Social processes in validation: Comments on Grant (1962) and Roberts and Pashler (2000). Symposium on Model Fitting and Parameter Estimation at the ACT-R Workshop . pdf

. pdf

Ritter, F.E., & Bibby, P.A. (2008). Modeling How, When, and What Is Learned in a Simple Fault-Finding Task. Cognitive Science, 32 (5), 862-892. pdf

(5), 862-892. pdf

Ritter, F.E., Schoelles, M.J., Quigley, K.S., & Klein, L.C. (2010). Determining the number of simulation runs: Treating simulations as theories by not sampling their behavior. To appear in: S. Narayanan and L. Rothrock (Eds.), Human-in-the-loop Simulations: Methods and Practice . pdf

. pdf

Roberts, S., & Pashler, H. (2000). How Persuasive Is a Good Fit? A Comment on Theory Testing. Psychological Review, 107 (2), 358-367. pdf

(2), 358-367. pdf

Robinson, A.E., Hammon, P.S., & de Sa, V.R. (2007). Explaining brightness illusions using spatial filtering and local response normalization. Vision Research, 47 (12), 1631-1644. pdf

(12), 1631-1644. pdf

Rogers, T.T., Lambon Ralph, M.A., Garrard, P., Bozeat, S., McClelland, J.L., Hodges, J.R., & Patterson, K. (2004). The structure and deterioration of semantic memory: A neuropsychological and computational investigation. Psychological Review, 111 , 205-235. pdf

, 205-235. pdf

Rumelhart, D.E., & McClelland, J.L. (1982). An Interactive Activation Model of Context Effects in Letter Perception: Part 2. The Contextual Enhancement Effect and Some Tests and Extensions of the Model. Psychological Review, 89 (1), 60-94. pdf

(1), 60-94. pdf

Rumelhart, D.E., & McClelland, J.L. (1986). On learning the past tenses of English verbs. In J.L. McClelland, D.E. Rumelhart, and the PDP research group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition. Volume II. Cambridge, MA: MIT Press. Chapter 18, pp. 216-271 pdf

Cambridge, MA: MIT Press. Chapter 18, pp. 216-271 pdf

Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning representations by back-propagating errors. Nature, 323 , 533-536. pdf

, 533-536. pdf

Sanborn, A.N., Griffiths, T.L., & Navarro, D.J. (2010). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, 117 (4), 1144-1167. pdf

(4), 1144-1167. pdf

Schneider, W., & Shiffrin, R.M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review, 84, 1-66. pdf

Shenoy, P & Yu, A J (2011). Rational decision-making in inhibitory control. Frontiers in Human Neuroscience,5:48. doi: 10.3389/fnhum.2011.00048. ( pdf )



)

Shepard, R.N. (1984). Ecological Constraints on Internal Representation: Resonant Kinematics of Perceiving, Imagining, Thinking, and Dreaming. Psychological Review, 91 , 417-447. pdf

, 417-447. pdf

Shepard, R.N. (1987). Toward a Universal Law of Generalization for Psychological Science. Science, 237 (4820), 1317-1323. pdf

(4820), 1317-1323. pdf

Shi, L., Griffiths, T.L., Feldman, N.H, & Sanborn, A.N. (2010). Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review, 17 (4), 443-464. pdf

(4), 443-464. pdf

Shiffrin, R.M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory. Psychological Review, 84 , 127-190. html (abstract)

, 127-190. html (abstract)

Shiffrin, R.M., & Steyvers, M. (1997). A model for recognition memory: REM-retrieving effectively from memory. Psychonomic Bulletin & Review, 4 (2), 145-166. pdf

(2), 145-166. pdf

Shiffrin, R.M., Lee, M.D., Kim, W., & Wagenmakers, E.-J. (2008). A Survey of Model Evaluation Approaches with a Tutorial on Hierarchical Bayesian Methods. Cognitive Science, 32 , 1248-1284. pdf

, 1248-1284. pdf

Shiffrin, R.M., Ratcliff, R., & Clark, S.E. (1990). List-Strength Effect .2. Theoretical Mechanisms. Journal of Experimental Psychology-Learning Memory and Cognition, 16 (2), 179-195. html (abstract)

(2), 179-195. html (abstract)

Shultz, T.R. (1998). A computational analysis of conservation. Developmental Science, 1 , 103-126. pdf

, 103-126. pdf

Shultz, T.R. (2006). Constructive learning in the modeling of psychological development. In Y. Munakata & M. H. Johnson (Eds.), Processes of change in brain and cognitive development: Attention and performance XXI , 61-86. Oxford: Oxford University Press. pdf

, 61-86. Oxford: Oxford University Press. pdf

Shultz, T.R., & Bale, A.C. (2006). Neural networks discover a near-identity relation to distinguish simple syntactic forms. Minds and Machines, 16 , 107-139. pdf

, 107-139. pdf

Shultz, T.R., & Lepper, M.R. (1996). Cognitive dissonance reduction as constraint satisfaction. Psychological Review, 103 , 219-240. pdf

, 219-240. pdf

Shultz, T.R., Rivest, F., Egri, L., Thivierge, J.-P., & Dandurand, F. (2007). Could knowledge-based neural learning be useful in developmental robotics? The case of KBCC. International Journal of Humanoid Robotics, 4 , 245–279. pdf

, 245–279. pdf

Sirois, S., & Shultz, T.R. (1998). Neural network modeling of developmental effects in discrimination shifts. Journal of Experimental Child Psychology, 71 , 235-274. pdf

, 235-274. pdf

Smith, J.M. (1987). When learning guides evolution. Nature, 329 , 761-762. pdf

, 761-762. pdf

Spivey, M.J., & Dale, R. (2004). On the continuity of mind: Toward a dynamical account of cognition. In B.H. Ross (Ed.), Psychology of learning and motivation, 45 , 85-142. Amsterdam: Elsevier. pdf

, 85-142. Amsterdam: Elsevier. pdf

St. Clair, M.C., Monaghan, P., & Christiansen, M.H. (2010). Learning grammatical categories from distributional cues: Flexible frames for language acquisition. Cognition, 116 , 341-360. pdf

, 341-360. pdf

St. John, M.F., & McClelland, J.L. (1990). Learning and Applying Contextual Constraints in Sentence Comprehension. Artificial Intelligence, 46 , 217-257. pdf

, 217-257. pdf

Stewart, N., Chater, N., & Brown, G.D.A. (2006). Decision by sampling. Cognitive Psychology, 53 , 1-26. pdf

, 1-26. pdf

Steyvers, M., Griffiths, T.L., & Dennis, S. (2006). Probabilistic inference in human semantic memory. Trends in Cognitive Science, 10 , 327-334. pdf

, 327-334. pdf

Steyvers, M., Lee, M.D., Miller, B., & Hemmer, P. (2009). The Wisdom of Crowds in the Recollection of Order Information. In Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williams and A. Culotta (Eds.), Advances in Neural Information Processing Systems, 22 , 1785-1793. MIT Press. pdf

, 1785-1793. MIT Press. pdf

Tabor, W. (2009). A dynamical systems perspective on the relationship between symbolic and non-symbolic computation. Cognitive Neurodynamics, 3 (4), 415-427. pdf

(4), 415-427. pdf

Tenenbaum, J.B. (1999). Bayesian modeling of human concept learning. In M.S. Kearns, S.A. Solla, & D.A. Cohn (Eds.), Advances in Neural Information Processing Systems, 11 . Cambridge, MA: MIT Press. pdf

. Cambridge, MA: MIT Press. pdf

Tenenbaum, J.B. (2000). Rules and Similarity in Concept Learning. In S.A. Solla, T.K. Leen, & K.-R. Muller (Eds.), Advances in Neural Information Processing Systems, 12 , 59-65. Cambridge, MA: MIT Press. pdf

, 59-65. Cambridge, MA: MIT Press. pdf

Tenenbaum, J.B., & Griffiths, T.L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24 , 629-640. pdf

, 629-640. pdf

Tenenbaum, J.B., & Griffiths, T.L. (2003). Theory-based causal inference. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in Neural Information Processing Systems, 15 , 35-42. Cambridge, MA: MIT Press. pdf

, 35-42. Cambridge, MA: MIT Press. pdf

Tenenbaum, J.B., Griffiths, T.L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10 (7), 309–318. pdf

(7), 309–318. pdf

Thomas, M.S.C., & Karmiloff-Smith, A. (2003). Modeling Language Acquisition in Atypical Phenotypes. Psychological Review, 110 (4), 647-682. pdf

(4), 647-682. pdf

Todd, P.M., & Gigerenzer, G. (2000). Précis of Simple heuristics that make us smart. Behavioral and Brain Sciences, 23 , 727-741. pdf

, 727-741. pdf

Tong, M.H., Joyce, C.A., & Cottrell, G.W. (2008). Why is the fusiform face area recruited for novel categories of expertise? A neurocomputational investigation. Brain Research, 1202 , 14-24. pdf

, 14-24. pdf

Torralba, A., Oliva, A., Castelhano, M.S., & Henderson, J.M. (2006). Contextual guidance of eye movements and attention in real-world scenes: The role of global features on object search. Psychological Review, 113 (4), 766-786. pdf

(4), 766-786. pdf

Trommershauser, J., Maloney, L.T., & Landy, M.S. (2008). Decision making, movement planning and statistical decision theory. Trends in Cognitive Sciences, 12 , 291–97. pdf

, 291–97. pdf

Tversky, A. (1977). Features of Similarity. Psychological Review, 84 (4), 327-352. pdf

(4), 327-352. pdf

Tversky, A. (2004) Preference, Belief, and Similarity: Selected Writings. E. Shafir (Ed.). Cambridge, MA: MIT Press. pdf



van Rooij, I., Bongers, R.M., & Haselager, W.F.G. (2002). A non-representational approach to imagined action. Cognitive Science, 26 , 345-375. pdf

, 345-375. pdf

Vul, E., Frank, M., Alvarez, G., & Tenenbaum, J. (2009). Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, A. Culotta (Eds.), Advances in Neural Information Processing Systems, 22 , 1955–1963. pdf

, 1955–1963. pdf

Weiss, Y., Simoncelli, E.P., & Adelson, E.H. (2002). Motion illusions as optimal percepts. Nature Neuroscience, 5 (6), 598-604 pdf

(6), 598-604 pdf

Woollams, A., Lambon Ralph, M.A., Plaut, D.C., & Patterson, K. (2007). SD-squared: On the association between semantic dementia and surface dyslexia. Psychological Review, 114, 316-339. pdf



Yu, A J & Dayan, P (2005). Uncertainty, neuromodulation, and attention. Neuron , 46: 681-692. (pdf).



Yu, A J, Dayan, P, & Cohen J D (2009). Dynamics of attentional selection under conflict: Toward a rational

Bayesian account. Journal of Experimental Psychology: Human Perception and Performance, 35: 700-717. (pdf)



Bayesian account. Journal of Experimental Psychology: Human Perception and Performance, 35: 700-717. (pdf)

Zhang, L., Tong, M.H., Marks, T.K., Shan, H., & Cottrell, G.W. (2008). SUN: A Bayesian Framework for Saliency Using Natural Statistics. Journal of Vision, 8(7):32, 1-20. pdf

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"



"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

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."