Mark Steyvers

Ph.D. Indiana University, 2000 (Cognitive Psychology & Cognitive Science).

Postdoctoral Fellow, Psychology Department, Stanford University.

My research interests span a diverse set of topics in cognitive psychology and cognitive science including episodic memory, semantic memory, causal reasoning, and object perception. In research on semantic memory, the goal is to develop semantic representations that can efficiently predict the semantic information required in a given context. My work on the "topics model" has shown that the large-scale structure of the model's representation has statistical properties that correspond well with those of semantic networks produced by humans. I am currently extending this model to make precise predictions for episodic memory tasks such as recognition and recall where semantic properties play an important role (e.g. "false memory").

In research on causal reasoning, we study peoples ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of purely observational data. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal Bayesian networks and the inferential principles of optimal Bayesian decision-making and maximizing expected information gain.

Representative Publications

Steyvers, M., Tenenbaum, J., & Wagenmakers, E.J. (in press). Causal Reasoning in Humans and Bayesian Networks. Cognitive Science.

Steyvers, M. (to appear in 2002). Multidimensional Scaling. In: Encyclopedia of Cognitive Science. Nature Publishing Group, London, UK.

Griffiths, T.L., & Steyvers, M. (to appear in 2002). Topics in Semantic Representation. Advances in Neural Information Processing Systems.

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