Data from: Emerging Representational Geometries in the Visual System Predict Reaction Times for Object Categorization

Recognizing an object takes just a fraction of a second, less than the
blink of an eye. Applying multivariate pattern analysis, or "brain
decoding", methods to magnetoencephalography (MEG) data has allowed
researchers to characterize, in high temporal resolution, the emerging
representation of objects that underlie our capacity for rapid
recognition. Shortly after stimulus onset, exemplar stimuli cluster by
category in high-dimensional activation spaces. In these emerging
activation spaces, the decodability of exemplar category varies over time,
reflecting the brain's transformation of visual inputs into coherent
categorical representations. How do these emerging representations relate
to categorization behavior? Recently it has been proposed that the
distance of an exemplar representation from a categorical boundary in an
activation space is critical for perceptual decision-making, and that
reaction times should therefore correlate with distance from the boundary.
The predictions of this distance hypothesis have been born out in human
inferior temporal cortex (IT), an area of the brain crucial for the
representation of object categories. The time of peak decoding is the
optimal time for category information to be "read out" from the
brain's time varying representation of the stimuli. In this study, we
tested the distance hypothesis, and specifically whether or not the brain
reads out at the optimal time for choice behavior. Using MEG decoding
methods, we show that the distance of a pattern of activity from a
decision boundary through a high-dimensional activation space correlates
with reaction times in a visual categorization task, but only during the
period of peak decodability. Our results suggest the brain uses the
optimal stimulus representation for choice behavior, and that neural
representations for objects are partially constitutive of the decision
process in visual perception.