Exploring expression space: adaptation to orthogonal and anti-expressions.

The present study sought to better understand the nature of the neural representation of expression. Specifically, we sought to compare the coding of naturally occurring expressions with the dimensional representation of facial identity (face space). Individual frames depicting the naturalistic facial expressions of a single individual were analyzed and used to estimate the mean posture and image texture of a dynamic sequence. The dimensionality present within the optic flow variation was extracted through the application of principal component analysis (PCA). Pairs of static anti-expressions were subsequently created by reconstructing postures corresponding to ±2.15 standard deviations along the axes defined by the first and second principal components comprising the computed "expression space." Using an adaptation procedure, we show that adapting to an expression selectively biases perception of subsequently viewed stimuli in the direction of its anti-expression, analogous to similar findings with identity, but does not bias perception in the orthogonal direction. These findings suggest that the representation of naturally occurring expressions can be modeled using the same kind of multidimensional framework as has been proposed for identity.