Language Evolution by Iterated Learning With Bayesian Agents. - Thomas L. Griffiths 001; Michael L. Kalish (Cogn. Sci., 2007)

Modeling human function learning with Gaussian processes. - Thomas L. Griffiths 001; Christopher G. Lucas; Joseph Jay Williams; Michael L. Kalish (NIPS, 2008)

Using Category Structures to Test Iterated Learning as a Method for Identifying Inductive Biases. - Thomas L. Griffiths 001; Brian R. Christian; Michael L. Kalish (Cogn. Sci., 2008)

The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning. - Stephan Lewandowsky; Thomas L. Griffiths 001; Michael L. Kalish (Cogn. Sci., 2009)

Grounding lexical choice in Bayesian inference. - Kyle Albarado; Michael L. Kalish (CogSci, 2011)

Discovering Inductive Biases in Categorization through Iterated Learning. - Kevin Robert Canini; Thomas L. Griffiths 001; Wolf Vanpaemel; Michael L. Kalish (CogSci, 2011)

Explaining How the Mind Works: On the Relation Between Cognitive Science and Philosophy. - Jonathan Trigg; Michael L. Kalish (Top. Cogn. Sci., 2011)

The Effects of Cultural Transmission Are Modulated by the Amount of Information Transmitted. - Thomas L. Griffiths 001; Stephan Lewandowsky; Michael L. Kalish (Cogn. Sci., 2013)

A Bayesian Latent Mixture Approach to Modeling Individual Differences in Categorization Using General Recognition Theory. - Irina Danileiko; Michael D. Lee 001; Michael L. Kalish (CogSci, 2015)

The Categorization Task is Insufficient to Distinguish between Strategies: A Case for Partial-XOR-like Tasks. - Yu-Wei Chang; Michael L. Kalish (CogSci, 2023)