Abstract

A central challenge in cognitive science is to measure and quantify experimentally the mental representations humans (and other animals) develop—in other words, to “read” subjects’ minds. In order to eliminate potential biases in reporting mental contents due to verbal elaboration, subjects’ responses in experiments are often limited to simple binary decisions or discrete choices that do not require conscious reflection upon their mental contents. However, it is unclear what such impoverished data can tell us about the potential richness and dynamics of subjects’ mental representations. To address this problem, we used ideal observer models that formalise choice behaviour as a quasi-optimal (stochastic) function of subjects’ representations in long-term memory, acquired through prior learning, and the information currently available to them. Bayesian inversion of such models allowed us to infer subjects’ mental representation from their choice behaviour in a task as simple as the standard one-back task—in which successively presented items have to be judged as being the same or different. In comparison with earlier methods developed along similar lines (eg. Sanborn & Griffiths, NIPS 2008 ), our method does not require the introduction of several trials of a special-purpose psychophysics task and thus has sufficient temporal resolution to track as mental representations develop through learning. In this talk I will introduce our statistical generative model for subject’s behaviour in cognitive scientific experiments and present Markov chain Monte Carlo inference under a simplified model,where the subject’s mental representation is assumed to remain constant through the analysed segment of the experiment. As subjects are assumed to learn during the experiment, it is desired to allow for changes in mental representation during the course of the experiment. I will present the challenge of incorporating temporal dynamics of mental representations in the present statistical model.