Kalish, M., Newell, B., & Dunn, J. (2017). More is generally better: Higher working memory capacity does not impair perceptual category learning. Journal of Experimental Psychology: Learning, Memory and Cognition, 43, 503-514.

Kalish, M., Dunn, J., Burdakov, O. & Sysoev, O. (2016). A statistical test of the equality of latent ordersJournal of Mathematical Psychology, 70, 1-11.

Donkin, C., Newell, B., Kalish, M., Dunn, J. & Nosofsky, R. M. (2015). Identifying strategy use in category learning tasks: a case for more diagnostic data and models. Journal of Experimental Psychology: Learning, Memory and Cognition, 41, 933-948.

Lucas, C., Griffiths, T., Williams, J. & Kalish, M. (2015).  A rational model of function learning.  Psychonomic Bulletin and Review, 22, 1193-1215.

Dunn, J., Kalish, M. & Newell, B. (2014). State-trace analysis can be an appropriate tool for assessing the number of cognitive systems: A reply to Ashby. Psychonomic Bulletin & Review, 21, 947-954.

Canini, K., Griffiths, T., Vanpaemel, W. & Kalish, M. (2014). Revealing human inductive biases for category learning by simulating cultural transmissionPsychonomic Bulletin & Review, 21, 785-793.

Kalish, M. (2013).  Learning and extrapolating a periodic functionMemory & Cognition, 41, 886-96.

Griffiths, T. L., Lewandowsky, S., & Kalish, M. L. (2013). The effects of cultural transmission are modulated by the amount of information transmitted. Cognitive Science, 37, 953-67.

Kalish, M. & Dunn, J. (2012). What could cognitive neuroscience tell us about recognition memory? Australian Journal of Psychology, 64, 29-36.

Dunn, J., Newell, B. & Kalish, M. (2012). The effect of feedback delay and feedback type on perceptual category learningJournal of Experimental Psychology: Learning, Memory and Cognition, 38, 840-859.

Lewandowsky, S., Yang, L., Newell, B. & Kalish, M. (2012). Working memory does not dissociate between different perceptual categorization tasksJournal of Experimental Psychology: Learning, Memory and Cognition, 38, 881-904.

Kwantes, P., Neal, A. & Kalish, M. (2012).  Item order matters in a function learning task.  Canadian Journal of Experimental Psychology, 66, 90-97.

Trigg, J. & Kalish, M. (2011). Explaining how the mind works: on the relation between cognitive science and philosophyTopics in Cognitive Science3, 399-424.

Newell, B., Dunn, J., & Kalish, M. (2011). Systems of category learning: fact or fantasy. In B. Ross (Ed.) The Psychology of Learning and Motivation, Vol 54 (pp. 167-215), Burlington: Academic Press.

Trigg, J. & Kalish, M. (2010). Thought, language and mental representation. In S. Ohlsson & R. Catrambone (Eds.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society (p188-193).

Robinette, L., Feist, M., & Kalish, M. (2010). Framed: Factors influencing reference frame choice in tabletop space. In S. Ohlsson & R. Catrambone (Eds.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society (p1064-1069).

Newell, B., Dunn, J., & Kalish, M. (2010). The dimensionality of perceptual category learning: A state-trace analysis. Memory and Cognition38, 563-581.

Lewandowsky, S., Griffiths, T. & Kalish, M. (2009). The wisdom of individuals: Exploring people’s knowledge about everyday events using iterated learning. Cognitive Science, 33, 969-998.

Griffiths, T., Lucas, C., Williams, J. & Kalish, M. (2009). Modeling human function learning with Gaussian processes. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou (Eds.), Advances in Neural Information Processing Systems, vol. 21 (pp. 553–560). Red Hook, NY: Curran Associates.

Griffiths, T., Kalish, M. & Lewandowsky, S. (2008). Theoretical and empirical evidence for the impact of inductive biases on cultural evolution.  Philosophical Transactions of the Royal Society, Series B. 363, 3503–3514.

Griffiths, T.L., Christian, B.R., & Kalish, M.L. (2008). Using category structures to test iterated learning as a method for revealing inductive biases. Cognitive Science, 32, 68-107.

Kalish, M., Griffiths, T. & Lewandowsky, S. (2007). Iterated learning: Intergenerational knowledge transmission reveals inductive biases. Psychonomic Bulletin and Review14, 288-294.

Griffiths, T. & Kalish, M. (2007). Language evolution by iterated learning with Bayesian agents.  Cognitive Science, 31, 441-480.

Griffiths, T., Christian, B. & Kalish, M. (2006).  Revealing priors on category structures through iterated learning.  Proceedings of the 28th Annual Conference of the Cognitive Science Society

Kalish, M., Lewandowsky, S. & Davies, M. (2005).  Error-driven knowledge restructuring in category learning.  Journal of Experimental Psychology: Learning, Memory and Cognition, 31, 846-861.

Griffiths, T., & Kalish, M. (2005).  A Bayesian view of language evolution by iterated learning.  In B. G. Bara, L. Barsalou, & M. Bucciarelli (Eds.), Proceedings of the 27th Annual Conference of the Cognitive Science Society (p. 827-832). Mahwah, NJ: Erlbaum.

Kalish, M., Lewandowsky, S., & Kruschke, J. (2004).  Population of Linear Experts: Knowledge Partitioning and Function Learning.  Psychological Review, 111, 1072-1099.

Griffiths, T. L. & Kalish, M. (2002). A multidimensional scaling approach to mental multiplication. Memory and Cognition, 30, 97-106.

Lewandowsky, S. L, Kalish, M. & Ngang, S. K. (2002). Simplified learning in complex situations: Knowledge partitioning in function learning. Journal of Experimental Psychology: General, 131, 163-193.

Kalish, M. (2001). An inverse base rate effect with continuously valued stimuli. Memory and Cognition, 29, 587-597.

Kalish, M. & Kruschke, J. (2000). The role of attention shifts in categorization of continuous dimensioned stimuli. Psychological Research, 64, 105-116.

Lewandowsky, S. L, Kalish, M. & Griffiths, T. L. (2000). Categorization using context: Expedient errors and resistance to knowledge restructuring. Journal of Experimental Psychology: Learning, Memory and Cognition, 26, 1666-1684.

Kalish, M., Lewandowsky, S. & Dennis, S. (1999). Remote delivery of cognitive science laboratories: A solution for small disciplines in large countries. Behavior Research Methods, Instruments and Computers, 31, 270-274.

Wynne, C.D. & Kalish, M. (1999). Effects of occasional short interfood intervals on temporal control in pigeons. Behavioral Processes, 45, 207-218.

Goertzel, B. & Kalish, M. (1998). Similarity as compression. The Noetic Journal, 1, 174-182.

Henmi, T., & Kalish, M. (1998). Dynamics of iterated perception. Complexity International, 6. Available on line at

Goertzel, B. & Kalish, M. (1998). Mindspace curvature: The non-Euclidean geometry of perception and illusion. The Noetic Journal, 1, 207-230.

Kalish, M. & Kruschke, J. (1997). Decision boundaries in one dimensional categorization. Journal of Experimental Psychology: Learning, Memory and Cognition. 23, 1362-1377.

Kalish, M. (1994). Idiosyncratic errors in visually directed reaching. Journal of Motor Behavior, 26, 296-300.

Kalish, M. (1994). Adaptive learning of Gaussian categories leads to decision bounds and response surfaces incompatible with optimal decision making. Proceedings of 16th Annual Conference of the Cognitive Science Society, 479-484.

Kalish, M. (1993). Affordance learning as a problem of information integration. In S. Valenti & J. Pittenger (Eds.) Studies in Perception and Action II, (pp. 130-134). Hillsdale, NJ: LEA.

Kalish, M. (1991). Human performance in visually directed reaching results in systematic, idiosyncratic error. Proceedings of 13th Annual Conference of the Cognitive Science Society, 770-774.

Warren, W., Blackwell, A., Kurtz, K., Hatsopoulos, N. & Kalish, M. (1991). On the sufficiency of the velocity field for perception of heading. Biological Cybernetics, 65, 770-774.

Warren, W., Morris, M. & Kalish, M. (1988). Perception of translational heading from optical flow. Journal of Experimental Psychology: Human Perception and Performance, 14, 646-660.

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