Cognitive Science Literature

• 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. html


• 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. html (abstract)


• 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. html (abstract)


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


• Christiansen, M.H., & Chater, N. (2001). Connectionist Psycholinguistics: Capturing the Empirical Data. Trends in Cognitive Sciences, 5(2), 82-88. 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. html (abstract)


• 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. html (abstract)


• 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. html (abstract)


• 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. html (abstract)


• 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. html (abstract)


• Jordan, M.I. (1986). Serial Order: A parallel distributed processing approach. UCSD Cognitive Science Technical Report 8604. html (abstract)


• Jordan, M.I., & Rumelhart, D.E. (1992). Forward models: 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. html (abstract)


• 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


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


• Kohonen, T. (1982). Self-organized formation of topologically 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. html


• Larkey, L.B., & Love, B.C. (2003). CAB: Connectionist analogy builder. Cognitive Science, 27, 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


• Li, P., Farkas, I., & MacWhinney, B. (2004). Early lexical 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)


• 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
  


• McClelland, J.L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(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


• McClelland, J.L., & Elman, J.L. (1986). The TRACE Model of Speech Perception. Cognitive Psychology, 18, 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


• 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


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


• 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


• McRae, K., Spivey-Knowlton, M.J., Tanenhaus, M.K. (1997). Modeling the influence of thematic fit (and other constraints) in on-line sentence comprehension. Paper presented at the 10th CUNY Sentence Processing Conference, Santa Monica, CA, March, 1997. 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. html (abstract)


• 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. html (abstract)


• 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


• 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)


• 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


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


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


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


• 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


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


• 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


• Oaksford, M. & Chater N. (1994). A Rational Analysis of the Selection Task as Optimal Data Selection. Psychological Review, 101(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
  


• 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


• 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


• 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


• 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


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


• Palmeri, T.J., & Gauthier, I. (2004). Visual object understanding. Nature Reviews Neuroscience, 5, 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


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


• 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


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


• 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)


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


• 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


• 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


• 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


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


• 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


• 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


• 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


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


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


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


• Shepard, R.N. (1987). Toward a Universal Law of Generalization for Psychological Science. Science, 237(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


• 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)


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


• 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)


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


• 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. html (abstract)


• 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. html (abstract)


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


• 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. html (abstract)


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


• Smith, J.M. (1987). When learning guides evolution. Nature, 329, 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


• 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


• Stewart, N., Chater, N., & Brown, G.D.A. (2006). Decision by sampling. Cognitive Psychology, 53, 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


• 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


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


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


• 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


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


• 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


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


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• 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. html
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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)."

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."

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)."