Miscellaneous
Nov 18, 2019
There is a long tail of “other stuff” which usually stems out of the other areas but doesn’t properly fit into any of them. This usually includes thinking about the role and interpretation of computational and mathematical models in cognitive science, the development of statistical approaches to applied problems, and various other bits and pieces. There is no coherent plan to this: I just write about things when I have good ideas about them!
Andrew Perfors
Professor
I seek to understand how people reason and think, both on their own and in groups.
Publications
Janet Box-Steffensmeier, Jean Burgess, Maurizio Corbetta, Kate Crawford, Esther Duflo, Laurel Fogarty, Alison Gopnik, Sari Hanafi, Mario Herrero, Ying-yi Hong, Yasuko Kameyama, Tatia Lee, Gabriel Leung, Daniel Nagin, Anna Nobre, Merete Nordentoft, Aysu Okbay, Andrew Perfors, Laura Rival, Cassidy Sugimoto, Bertil Tungodden, Claudia Wagner
(2022).
The future of human behaviour research.
Nature Human Behavior 6: 15-24.
Piers Howe, Andrew Perfors
(2018).
An argument for how (and why) to incentivise replication.
Commentary. Behavioral and Brain Sciences 41.
Lauren Kennedy, Danielle Navarro, Andrew Perfors, Nancy Briggs
(2017).
Not every credible interval is credible: On the importance of robust methods in Bayesian data analysis.
Behavioral Research Methods, 49(6): 2219-2234.
Sean Tauber, Danielle Navarro, Andrew Perfors, Marc Steyvers
(2017).
Bayesian models of cognition revisited: Setting optimality aside and letting data drive psychological theory.
Psychological Review 124(4): 410-441.
Andrew Perfors
(2016).
Piaget, probability, causality, and contradiction.
Human Development 59: 26-33.
Andrew Perfors
(2014).
Representations, approximations, and limitations within a computational framework for cognitive science: Commentary on article by Tecumseh Fitch.
Physics of Life Reviews 11: 369-370.
Danielle Navarro, Andrew Perfors, Wai Keen Vong
(2013).
Learning time-varying categories.
Memory and Cognition 41: 917-927.
Andrew Perfors
(2012).
Bayesian models of cognition: What's built in after all?.
Philosophy Compass 7: 127-138.
Andrew Perfors, Danielle Navarro
(2012).
What Bayesian modelling can tell us about statistical learning: What it requires and why it works.
In P Rebuschat and J Williams (Eds.) Statistical learning and language acquisition: 383-408.
Andrew Perfors
(2012).
Levels of explanation and the workings of science.
Australian Journal of Psychology 64: 52-59.
Andrew Perfors, Josh Tenenbaum, Tom Griffiths, Fei Xu
(2011).
A tutorial introduction to Bayesian models of cognitive development.
Cognition 120: 302-321.
Danielle Navarro, Andrew Perfors
(2011).
Enlightenment grows from fundamentals: Comment on Jones and Love.
Behavioral and Brain Sciences 34: 207-208.
Tom Griffiths, Nick Chater, Charles Kemp, Andrew Perfors, Josh Tenenbaum
(2010).
Probabilistic models of cognition: Exploring representations and inductive biases.
Trends in Cognitive Sciences 14: 357-364.
Andrew Perfors
(2008).
Learnability, representation, and language: A Bayesian approach.
PhD Thesis. Massachusetts Institute of Technology Department of Brain and Cognitive Sciences. Cambridge, MA.