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Nikolaus Kriegeskorte; Vision as transformation of representational geometry. Journal of Vision 2014;14(10):1476. doi: https://doi.org/10.1167/14.10.1476.
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© ARVO (1962-2015); The Authors (2016-present)
Vision can be understood as the transformation of representational geometry from one visual area to the next, and across time, as recurrent dynamics converge within a single area. The geometry of a representation can be usefully characterized by a representational distance matrix computed by comparing the patterns of brain activity elicited a set of visual stimuli. This approach enables to compare representations between brain areas, between different latencies after stimulus onset, between different individuals and between brains and computational models. I will present results from human functional imaging of early and ventral-stream visual representations. Results from fMRI suggest that the early visual image representation is transformed into an object representation that emphasizes behaviorally important categorical divisions more strongly than accounted for by visual-feature computational models that are not explicitly optimized to distinguish categories. The categorical clusters appear to be consistent across individual human brains. However, the continuous representational space is unique to each individual and predicts individual idiosyncrasies in object similarity judgements. The representation flexibly emphasizes task-relevant category divisions through subtle distortions of the representational geometry. MEG results further suggest that the categorical divisions emerge dynamically, with the latency of categoricality peaks suggesting a role for recurrent processing.
Meeting abstract presented at VSS 2014
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