%0 Journal Article %A K. Braunlich %A B. C. Love %T Occipitotemporal Representations Reflect Individual Differences in Conceptual Knowledge %D 2018 %R 10.1101/264895 %J bioRxiv %P 264895 %X Through selective attention, decision-makers can learn to ignore behaviorally-irrelevant stimulus dimensions. This can improve learning and increase the perceptual discriminability of relevant stimulus information. To account for this effect, popular contemporary cognitive models of categorization typically include of attentional parameters, which provide information about the importance of each stimulus dimension in decision-making. The effect of these parameters on psychological representation is often described geometrically, such that perceptual differences over relevant psychological dimensions are accentuated (or stretched), and differences over irrelevant dimensions are down-weighted (or compressed). In sensory and association cortex, representations of stimulus features are known to covary with their behavioral relevance. Although this implies that neural representational space might closely resemble that hypothesized by formal categorization theory, to date, attentional effects in the brain have been demonstrated through powerful experimental manipulations (e.g., contrasts between relevant and irrelevant features). This approach sidesteps the role of idiosyncratic conceptual knowledge in guiding attention to useful information sources. To bridge this divide, we used formal categorization models, which were fit to behavioral data, to make inferences about the concepts and strategies used by individual participants during decision-making. We found that when greater attentional weight was devoted to a particular visual feature (e.g., “color”), its value (e.g., “red”) was more accurately decoded from occipitotemporal cortex. We additionally found that this effect was sufficiently sensitive to reflect individual differences in conceptual knowledge. The results indicate that occipitotemporal stimulus representations are embedded within a space closely resembling that proposed by classic categorization models.We thank the authors of the original studies for sharing their data. This work was supported by NIH Grant 1P01HD080679, Leverhulme Trust grant RPG-2014-075 and Wellcome Trust Senior Investigator Award WT106931MA to BCL. %U https://www.biorxiv.org/content/biorxiv/early/2018/08/02/264895.full.pdf