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M. Ida Gobbini

Publications and source records attributed to M. Ida Gobbini.

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Disentangling the representation of identity from head view along the human face processing pathway.

Neural models of a distributed system for face perception implicate a network of regions in the ventral visual stream for recognition of identity. Here, we report an fMRI neural decoding study in humans that shows that this pathway culminates in a right inferior frontal cortex face area (rIFFA) with a representation of individual identities that has been disentangled from variable visual features in different images of the same person. At earlier stages in the pathway, processing begins in early visual cortex and the occipital face area (OFA) with representations of head view that are invariant across identities, and proceeds to an intermediate level of representation in the fusiform face area (FFA) in which identity is emerging but still entangled with head view. Three-dimensional, view-invariant representation of identities in the rIFFA may be the critical link to the extended system for face perception, affording activation of person knowledge and emotional responses to familiar faces.\n\nSignificance StatementIn this fMRI decoding experiment, we address how face images are processed in successive stages to disentangle the view-invariant representation of identity from variable visual features. Representations in early visual cortex and the occipital face area distinguish head views, invariant across identities. An intermediate level of representation in the fusiform face area distinguishes identities but still is entangled with head view. The face-processing pathway culminates in the right inferior frontal area with representation of view-independent identity. This paper clarifies the homologies between the human and macaque face processing systems. The findings show further, however, the importance of the inferior frontal cortex in decoding face identity, a result that has not yet been reported in the monkey literature.

Neuroscience

Attention selectively reshapes the geometry of distributed semantic representation

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. Attending to animal taxonomy while viewing the same stimuli increased the discriminability of distributed animal category representations in ventral temporal cortex. For both tasks, attention selectively enhanced the discriminability 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.

Neuroscience