Seeing different objects in different ways: measuring ventral visual tuning to sensory and semantic features with dynamically adaptive imaging.

ABSTRACT: A key challenge of object recognition is achieving a balance between selectivity for relevant features and invariance to irrelevant ones. Computational and cognitive models predict that optimal selectivity for features will differ by object, and here we investigate whether this is reflected in visual representations in the human ventral stream. We describe a new real-time neuroimaging method, dynamically adaptive imaging (DAI), that enabled measurement of neural selectivity along multiple feature dimensions in the neighborhood of single referent objects. The neural response evoked by a referent was compared to that evoked by 91 naturalistic objects using multi-voxel pattern analysis. Iteratively, the objects evoking the most similar responses were selected and presented again, to converge

SUBMITTER: Cusack R 

PROVIDER: S-EPMC6870384 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature