Morphed expression discrimination task

To test whether expression-selective units exhibit a human-like categorical perception of morphed facial expressions, we designed a morphed expression discrimination task that was comparable to the ABX discrimination task designed for human beings (36, 39, 40). Taking the happiness-anger continuum for example, the expression-selective units whose preferred expression was happiness or anger were selected to perform the task. At first, a binary SVC model was trained on the prototypic expressions (happy and angry expressions of all 104 identities in stimulus set 1) and then the trained SVC model was used to predict the expressions of morphed expression images (the middle 199 morph levels besides the two prototypic expressions). For each morph level, the identification frequency of anger was defined to be the network’s identification rate at the current expression morph level.

To quantitatively characterize the shape of the identification curve, we fitted the linear function, quadratic function (poly2), and logistic function to the curve, respectively. If the network perceived the morphed expressions like a human, the identification curve should be nonlinear and should show an abrupt category boundary. Thus, the goodness of fit (R²) of the logistic function (S-shaped) to identification curves should be the best.