RT Journal Article
SR Electronic
T1 Attention selectively reshapes the geometry of distributed semantic representation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 045252
DO 10.1101/045252
A1 Samuel A. Nastase
A1 Andrew C. Connolly
A1 Nikolaas N. Oosterhof
A1 Yaroslav O. Halchenko
A1 J. Swaroop Guntupalli
A1 Matteo Visconti di Oleggio Castello
A1 Jason Gors
A1 M. Ida Gobbini
A1 James V. Haxby
YR 2016
UL http://biorxiv.org/content/early/2016/07/23/045252.abstract
AB Humans prioritize different semantic qualities of a complex stimulus depending on their behavioral goals. These semantic features are encoded in distributed neural populations, yet it is unclear how attention might operate across these distributed representations. To address this, we presented participants with naturalistic video clips of animals behaving in their natural environments while the participants attended to either behavior or taxonomy. We used models of representational geometry to investigate how attentional allocation affects the distributed neural representation of animal behavior and taxonomy. Attending to animal behavior transiently increased the discriminability of distributed population codes for observed actions in anterior intraparietal, pericentral, and ventral temporal cortices, while collapsing task-irrelevant taxonomic information. Attending to animal taxonomy while viewing the same stimuli increased the discriminability of distributed animal category representations in ventral temporal cortex and collapsed behavioral information. For both tasks, attention selectively enhanced the categoricity of response patterns along behaviorally relevant dimensions. These findings suggest that behavioral goals alter how the brain extracts semantic features from the visual world. Attention effectively disentangles population responses for downstream read-out by sculpting representational geometry in late-stage perceptual areas.