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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.



Representative Publications:

  • Sanborn, A. N. & Silva, R. (2013). Constraining bridges between levels of analysis: A computational justification for Locally Bayesian Learning. Journal of Mathematical Psychology, 57, 94-106.

  • 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.