Reaction-times in perceptual tasks are the subject of many experimental and
theoretical studies. With the neural decision making process as main focus,
most of these works concern discrete (typically binary) choice tasks, implying
the identification of the stimulus as an exemplar of a category. Here we
address issues specific to the perception of categories (e.g. vowels, familiar
faces, ...), making a clear distinction between identifying a category (an
element of a discrete set) and estimating a continuous parameter (such as a
direction). We exhibit a link between optimal Bayesian decoding and coding
efficiency, the latter being measured by the mutual information between the
discrete category set and the neural activity. We characterize the properties
of the best estimator of the likelihood of the category, when this estimator
takes its inputs from a large population of stimulus-specific coding cells.
Adopting the diffusion-to-bound approach to model the decisional process, this
allows to relate analytically the bias and variance of the diffusion process
underlying decision making to macroscopic quantities that are behaviorally
measurable. A major consequence is the existence of a quantitative link between
reaction times and discrimination accuracy. The resulting analytical expression
of mean reaction times during an identification task accounts for empirical
facts, both qualitatively (e.g. more time is needed to identify a category from
a stimulus at the boundary compared to a stimulus lying within a category), and
quantitatively (working on published experimental data on phoneme
identification tasks)