The cognitive concept of representation plays a key role in theories of brain information processing. However, linking brain activity patterns to representational content and using them to test cognitive and computational theories remains challenging. Recent studies have characterized the geometry of brain representations by means of response-pattern dissimilarity matrices, enabling researchers to compare representations across stages of processing and to test cognitive and computational theories. I will briefly introduce a Matlab toolbox for representational similarity analysis (RSA) that supports statistical inference comparing multiple cognitive and computational models of a representation. The toolbox supports an analysis approach that is simultaneously strongly data- and strongly hypothesis-driven. 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.
|Date||20 June 2014|
|Time||16:00 - 17:00|