What shape are the neural response functions underlying opponent coding in face space? A psychophysical investigation

Recent evidence has shown that face space represents facial identity information using two-pool opponent coding. Here we ask whether the shape of the monotonic neural response functions underlying such coding is linear (i.e. face space codes all equal-sized physical changes with equal sensitivity) or nonlinear (e.g. face space shows greater coding sensitivity around the average face). Using adaptation aftereffects and pairwise discrimination tasks, our results for face attributes of eye height and mouth height demonstrate linear shape; including for bizarre faces far outside the normal range. We discuss how linear coding explains some results in the previous literature, including failures to find that adaptation enhances face discrimination, and suggest possible reasons why face space can maintain detailed coding of values far outside the normal range. We also discuss specific nonlinear coding models needed to explain other findings, and conclude face space appears to use a mixture of linear and nonlinear representations.