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Ritchie, J. B.

Publications and source records attributed to Ritchie, J. B..

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Asymmetric compression of representational space for object animacy categorization under degraded viewing conditions

Animacy is a robust organizing principle amongst object category representations in the human brain. Using multivariate pattern analysis methods (MVPA), it has been shown that distance to the decision boundary of a classifier trained to discriminate neural activation patterns for animate and inanimate objects correlates with observer reaction times for the same animacy categorization task (Carlson, Ritchie, Kriegeskorte, Durvasula, & Ma, 2014; Ritchie, Tovar, & Carlson, 2015). Using MEG decoding, we tested if the same relationship holds when a stimulus manipulation (degradation) increases task difficulty, which we predicted would systematically decrease the distance of activation patterns from the decision boundary, and increase reaction times. In addition, we tested whether distance to the classifier boundary correlates with drift rates in the Linear Ballistic Accumulator (Brown & Heathcote, 2008). We found that distance to the classifier boundary correlated with reaction time, accuracy, and drift rates in an animacy categorization task. Split by animacy, the correlations between brain and behavior were sustained for longer over the time course for animate than for inanimate stimuli. Interestingly, when examining the distance to the classifier boundary during the peak correlation between brain and behavior, we found that only degraded versions of animate, but not inanimate, objects had systematically shifted towards the classifier decision boundary as predicted. Our results support an asymmetry in the representation of animate and inanimate object categories in the human brain.

neuroscience

Decoding The Brain: Neural Representation And The Limits Of Multivariate Pattern Analysis In Cognitive Neuroscience

Since its introduction, multivariate pattern analysis (MVPA), or \"neural decoding\", has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoders Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the Dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the Dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the Dictum can be improved on, in order to better justify inferences about neural representation using MVPA.

neuroscience