Psych 128/290Q: Probabilistic models of cognition Fall, 2011

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1 Class Times: *** Psych 128/290Q: Probabilistic models of cognition Fall, 2011 Course Website: The course has a website through bspace. Office Hours: *** Contact Information: The best way to reach me is by Course summary: This seminar explores parallels between human cognition and ideas in probability and statistics, with an emphasis on statistical machine learning. Minds and machines face similar computational problems, meaning that we can develop new hypotheses about human cognition by seeing how those problems are solved in statistics and find new challenges for machine learning by studying human cognition. Topics will include causal learning, clustering, Markov chain Monte Carlo, function learning, and randomness. Students will complete an independent research pro ject related to computational modeling of human cognition. Prerequisites are Psych 123/Cogsci 131 or Computer Science 188 or 281A, or an equivalent familiarity with ideas from statistics and machine learning. Readings: There is no required textbook. You will have the opportunity to read and review a new book in progress on probabilistic models of cognition. In addition to this book, there will be a number of primary sources, available as PDF files through the class website. Course requirements: Requirement Percentage of final grade Chapter reviews (10) 20% In-class presentations 20% Final project 60% Each student will submit 10 reviews of chapters from the book, identifying errors, places where the presentation could be clearer, or other thoughts on the content of each chapter. The final project will be an independent research project presenting a simple experiment, testing a new model, or analyzing an existing model.

2 Schedule of classes and readings: MTG 1: Introduction to the class MTG 2: Probabilistic models of cognition Chapter 1: The approach. MTG 3: Discussion: Levels of analysis Anderson, J. R. (1990). The adaptive character of thought. Hillsdale, NJ: Erlbaum. Chapter 1. MTG 4: Approaches to modeling cognition Chapter 2: Historical context. MTG 5: Discussion: Connectionist and probabilistic approaches McClelland, J. L. (1998). Connectionist models and Bayesian inference. In M. Oaksford & N. Chater (Eds.), Rational models of cognition. Oxford: Oxford University Press. McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T.T., Seidenberg, M. S., & Smith, L. B. (2010). Letting Structure Emerge: Connectionist and Dynamical Systems Approaches to Understanding Cognition. Trends in Cognitive Sciences, 14, Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14, MTG 6: Bayesian inference 1 Chapter 3: Bayesian inference. Case study: The number game (Jack, Jake) Tenenbaum, J. B. (1999). Rules and similarity in concept learning. Advances in Neural Information Processing Systems 12. MTG 7: Bayesian inference 2 Case study: Categories and memory (Theresa) Huttenlocher, J., Hedges, L.V., & Vevea, J.L. (2000). Why do categories affect stimulus judgment? Journal of Experimental Psychology, General, 129, Case study: Predicting the future (Hye Young, Paul) Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17, MTG 8: Graphical models Chapter 4: Graphical models. Case study: Causal reasoning (Natalia) Rehder, B. & Burnett, R. (2005). Feature inference and the causal structure of categories. Cognitive Psychology, 50, Case study: Visual inference (Stacy, Benj) Kersten, D., & Yuille, A. (2003). Bayesian models of object perception. Current Opinion in Neurobiology, 13, 1-9. MTG 9: Causal learning Case study: Structure and strength (Dave) Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51,

3 MTG 10: Mixture models Chapter 5: Richer generative models. Case study: The perceptual magnet effect (Wendy) Feldman, N. H., & Griffiths, T. L. (2007). A rational account of the perceptual magnet effect. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. Case study: Categorization models (Samy) Rosseel, Y. (2002). Mixture models of categorization. Journal of Mathematical Psychology, 46, Case study: Topic models (Matthew B., Michael J.) Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114, MTG 11: Dynamical models Case study: Melody (Dave S., Joanna) Temperley, D. (2008). A probabilistic model of melody perception. Cognitive Science, 32, Case study: Changepoint detection (Chris) Steyvers, M., & Brown, S. (2006). Prediction and change detection. Advances in Neural Information Processing Systems 18. Case study: Object tracking (Brent) Vul, E., Frank, M. C., Alvarez, G. A. & Tenenbaum, J. B. (2009). Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model. Advances in Neural Information Processing Systems 22. MTG 12: Importance sampling and particle filters Chapter 6: Approximate probabilistic inference Case study: Exemplar models (Brett, Becky) Shi, L., Feldman, N. H., & Griffiths, T. L. (2008). Performing Bayesian inference with exemplar models. Proceedings of the 30th Annual Conference of the Cognitive Science Society. Case study: Particle filters (Josh A.) Abbott, J. T., & Griffiths, T. L. (2011). Exploring the influence of particle filter parameters on order effects in causal learning. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. MTG 13: Markov chain Monte Carlo Case study: Markov chain Monte Carlo with People (Daniel) Sanborn, A. N., & Griffiths, T. L. (2008). Markov chain Monte Carlo with people.advances in Neural Information Processing Systems 20. Case study: Iterated learning (Julia, Alex D.) Kalish, M. L., Griffiths, T. L., & Lewandowsky, S. (2007). Iterated learning: Intergenerational knowledge transmission reveals inductive biases. Psychonomic Bulletin and Review, 14, MTG 14: Making decisions Chapter 7: Decision and action Case study: Active learning (Morgan, Josh M.) Oaksford, M. & Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101,

4 MTG 15: Inverting decisions Case study: Inverse decision theory (John S.) Lucas, C., Griffiths, T. L., Xu, F., & Fawcett, C. (2009). A rational model of preference learning and choice prediction by children. Advances in Neural Information Processing Systems 21. Case study: Theory of mind (Kevin, Peter) Baker, C. L., Tenenbaum, J. B., & Saxe, R. R. (2006). Bayesian models of human action understanding. Advances in Neural Information Processing Systems 19. MTG 16: Hierarchical Bayes Chapter 8: Hierarchical Bayesian models Case study: The shape bias (Michelle) Kemp, C., Perfors, A., & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science 10, Case study: Multiple levels of abstraction and memory (Alex Ca.) Hemmer, P., & Steyvers, M. (2009). Integrating episodic memories and prior knowledge at multiple levels of abstraction. Psychonomic Bulletin & Review, 16, MTG 17: Discussion: Critiques of Bayesian models Jones, M., & Love, B. C. (2011). Bayesian fundamentalism or enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behavioral and Brain Sciences, 34, MTG 18: Nonparametric Bayes for clusters Project proposals due Chapter 9: Models of unbounded complexity Case study: The rational model of categorization (Nick) Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98, Case study: Evolution of word frequencies (Rey) Reali, F., & Griffiths, T. L. (2010). Words as alleles: Connecting language evolution with Bayesian learners to models of genetic drift. Proceedings of the Royal Society, Series B, 277, MTG 19: Nonparametric Bayes for features (Guest lecture by Joe Austerweil) Case study: Feature learning Austerweil, J. & Griffiths, T. L. (2009). Analyzing human feature learning as nonparametric Bayesian inference. Advances in Neural Information Processing Systems 21. MTG 20: Probabilistic grammars in language Chapter 10: Probabilistic grammars Case study: Syntactic comprehension (Andy H., Andrew M.) Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106, Case study: Poverty of the stimulus (Thurston, Matthew L.) Perfors, A., Tenenbaum, J.B., Regier, T. (2011) The learnability of abstract syntactic principles. Cognition, 118, Foraker, S., Regier, T., Khetarpal, N., Perfors, A., Tenenbaum, J. (2009) Indirect evidence and the poverty of the stimulus: The case of anaphoric one. Cognitive Science, 33,

5 MTG21: Probabilistic grammars in cognition Case study: Rational rules (Gigi) Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. (2008). A rational analysis of rule-based concept learning. Cognitive Science, 32, Case study: Structure learning (Matei) Kemp, C., & Tenenbaum, J. B. (2008). The discovery of structural form. Proceedings of the National Academy of Sciences. 105, MTG 22: Relational models Chapter 11: Probabilistic logic and relational models Case study: Learning relational theories (Andrew, Alex Ch.) Kemp, C., Tenenbaum, J. B., Niyogi, S. & Griffiths, T. L. (2010). A probabilistic model of theory formation. Cognition. 114, MTG 23: Probability and logic Case study: Theory-based causal induction (Mike P.) Griffiths, T. L., & Tenenbaum, J. B. (2009). Theory-based causal induction. Psychological Review, 116, MTG 24: Kolmogorov complexity Chapter 12: Probabilistic models as programs Case study: Concept learning (Caren) Feldman, J. (2001). Minimization of Boolean complexity in human concept learning. Nature, 407, Case study: Randomness (Anwar) Griffiths, T. L., & Tenenbaum, J. B. (2004). From algorithmic to subjective randomness. Advances in Neural Information Processing Systems 16. MTG 25: Probabilistic programming (Guest lecture by Noah Goodman) November 22: Summary and conclusions Chapter 13: Conclusions and future directions MTG 26: NO CLASS - THANKSGIVING MTG 27: Project presentations 1 MTG 28: Project presentations 2 December 12: Final projects due!

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