Proceedings of the Annual Meeting of the Cognitive Science Society

A key challenge for cognitive neuroscience is to decipher therepresentational schemes of the brain. A recent class of decodingalgorithms for fMRI data, stimulus-feature-based encodingmodels, is becoming increasingly popular for inferring thedimensions of neural representational spaces from stimulus-feature spaces. We argue that such inferences are not always valid,because decoding can occur even if the neural representationalspace and the stimulus-feature space use different representationalschemes. This can happen when there is a systematic mappingbetween them. In a simulation, we successfully decoded the binaryrepresentation of numbers from their decimal features. Sincebinary and decimal number systems use different representations,we cannot conclude that the binary representation encodes decimalfeatures. The same argument applies to the decoding of neuralpatterns from stimulus-feature spaces and we urge caution ininferring the nature of the neural code from such methods. Wediscuss ways to overcome these inferential limitations.