Data from: Emerging Representational Geometries in the Visual System Predict Reaction Times for Object Categorization
Ritchie, J. Brendan; Tovar, David A.; Carlson, Thomas A. (2015), Data from: Emerging Representational Geometries in the Visual System Predict Reaction Times for Object Categorization, Dryad, Dataset, https://doi.org/10.5061/dryad.fv8g8
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.