Non-isomorphism in efficient coding of complex sound properties

Abstract

To the extent that sensorineural systems are efficient, stimulus redundancy should be captured in ways that optimize information transmission. Consistent with this principle, neural representations of sounds have been proposed to become "non-isomorphic," increasingly abstract and decreasingly resembling the original (redundant) input. Here, non-isomorphism is tested in perceptual learning using AXB discrimination of novel sounds with two highly correlated complex acoustic properties and a randomly varying third dimension. Discrimination of sounds obeying the correlation became superior to that of sounds violating it despite widely varying physical acoustic properties, suggesting non-isomorphic representation of stimulus redundancy.