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Adam Sanborn (Associate Professor)

 Adam Sanborn



Explaining human categorisation and perception as rational behaviour.

Examining how people use approximate solutions in difficult cognitive tasks.

Methods for data collection and analysis.


Contact Details


Representative Publications:

  • Sanborn, A.N. & Chater, N. (2016). Bayesian brains without probabilities. Trends in Cognitive Sciences, 20(12), 883-893.
  • Sanborn A.N. & Beierholm U.R. (2016). Fast and accurate learning when making discrete numerical estimates. PLoS Computational Biology 12(4): e1004859.
  • Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2013). Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychological Review, 120, 411-437.
  • Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, 117, 1144-1167.
  • Sanborn, A. N., Griffiths, T. L., & Shiffrin, R. M. (2010). Uncovering mental representations with Markov chain Monte Carlo. Cognitive Psychology, 60, 63-106.


Supervisor to:
Alexandra Surdina