Explaining human categorisation and perception as rational behaviour.
Examining how people use approximate solutions in difficult cognitive tasks.
Methods for data collection and analysis.
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.