Rapid efficient coding of correlated complex acoustic properties

Natural sounds are complex, typically changing along multiple acoustic dimensions that covary in accord with physical laws governing sound-producing sources. We report that, after passive exposure to novel complex sounds, highly correlated features initially collapse onto a single perceptual dimension, capturing covariance at the expense of unitary stimulus dimensions. Discriminability of sounds respecting the correlation is maintained, but is temporarily lost for sounds orthogonal or oblique to experienced covariation. Following extended experience, perception of variance not captured by the correlation is restored, but weighted only in proportion to total experienced covariance. A Hebbian neural network model captures some aspects of listener performance; an anti-Hebbian model captures none; but, a principal components analysis model captures the full pattern of results. Predictions from the principal components analysis model also match evolving listener performance in two discrimination tasks absent passive listening. These demonstrations of adaptation to correlated attributes provide direct behavioral evidence for efficient coding.