PT - JOURNAL ARTICLE AU - Bankson, Brett B. AU - Boring, Matthew J. AU - Richardson, R. Mark AU - Ghuman, Avniel Singh TI - Dynamic Domain Specificity In Human Ventral Temporal Cortex AID - 10.1101/2020.11.11.378877 DP - 2020 Jan 01 TA - bioRxiv PG - 2020.11.11.378877 4099 - http://biorxiv.org/content/early/2020/11/12/2020.11.11.378877.short 4100 - http://biorxiv.org/content/early/2020/11/12/2020.11.11.378877.full AB - An enduring neuroscientific debate concerns the extent to which neural representation is restricted to networks of patches specialized for particular domains of perceptual input (Kaniwsher et al., 1997; Livingstone et al., 2019), or distributed outside of these patches to broad areas of cortex as well (Haxby et al., 2001; Op de Beeck, 2008). A critical level for this debate is the localization of the neural representation of the identity of individual images, (Spiridon & Kanwisher, 2002) such as individual-level face or written word recognition. To address this debate, intracranial recordings from 489 electrodes throughout ventral temporal cortex across 17 human subjects were used to assess the spatiotemporal dynamics of individual word and face processing within and outside cortical patches strongly selective for these categories of visual information. Individual faces and words were first represented primarily only in strongly selective patches and then represented in both strongly and weakly selective areas approximately 170 milliseconds later. Strongly and weakly selective areas contributed non-redundant information to the representation of individual images. These results can reconcile previous results endorsing disparate poles of the domain specificity debate by highlighting the temporally segregated contributions of different functionally defined cortical areas to individual level representations. Taken together, this work supports a dynamic model of neural representation characterized by successive domain-specific and distributed processing stages.SIGNIFICANCE STATEMENT The visual processing system performs dynamic computations to differentiate visually similar forms, such as identifying individual words and faces. Previous models have localized these computations to 1) circumscribed, specialized portions of the brain, or 2) more distributed aspects of the brain. The current work combines machine learning analyses with human intracranial recordings to determine the neurodynamics of individual face and word processing in and outside of brain regions selective for these visual categories. The results suggest that individuation involves computations that occur first in primarily highly selective parts of the visual processing system, then later recruits highly and non-highly selective regions. These results mediate between extant models of neural specialization by suggesting a dynamic domain specificity model of visual processing.Competing Interest StatementThe authors have declared no competing interest.