A Model of Representational Spaces in Human Cortex
Current models of the functional architecture of human cortex emphasize areas that capture coarse scale features of cortical topography but provide no account for population responses that encode information in fine scale patterns of activity. Here, we present a linear model of shared representational spaces in human cortex that captures fine scale distinctions among population responses with response tuning basis functions that are common across brains and models cortical patterns of neural responses with individual specific topographic basis functions. We derive a common model space for the whole cortex using a new algorithm, searchlight hyperalignment, and complex, dynamic stimuli that provide a broad sampling of visual, auditory, and social percepts. The model aligns representations across brains in occipital, temporal, parietal, and prefrontal cortices, as shown by between subject multivariate pattern classification and intersubject correlation of representational geometry, indicating that structural principles for shared neural representations apply across widely divergent domains of information. The model provides a rigorous account for individual variability of well known coarse scale topographies, such as retinotopy and category selectivity, and goes further to account for fine scale patterns that are multiplexed with coarse scale topographies and carry finer distinctions.
Cerebral cortex