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Abstract:
A central challenge in cognitive science is to measure and quantify the mental representations humans develop in
other words, to `read' subject's minds. In order to elimi-
nate potential biases in reporting mental contents due
to verbal elaboration, subjects' responses in experiments
are often limited to binary decisions or discrete choices
that do not require conscious re
ection upon their mental
contents. However, it is unclear what such impoverished
data can tell us about the potential richness and dy-
namics of subjects' mental representations. To address
this problem, we used ideal observer models that for-
malise choice behaviour as (quasi-)Bayes-optimal, given
subjects' representations in long-term memory, acquired
through prior learning, and the stimuli currently avail-
able to them. Bayesian inversion of such ideal observer
models allowed us to infer subjects' mental representation
from their choice behaviour in a variety of psychophysical
tasks. The inferred mental representations also allowed
us to predict future choices of subjects with reasonable
accuracy, even in tasks that were dierent from those in
which the representations were estimated. These results
demonstrate a signicant potential in standard binary
decision tasks to recover detailed information about sub-
jects' mental representations.
