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The representation underlying the identification and classification of semirealistic line drawings taken from a computer model of the face was investigated by using a speeded classification task and an identification task. These data were analyzed by using a multidimensional extension of signal detection theory, within which varieties of perceptual interactions between dimensions within and across stimuli can be characterized. The dimensions of interest here were eye separation, nose length, and mouth width. The response time and accuracy data from the speeded classification task suggest that processing of a given feature did depend on whether other features were present or absent, but given that other features were present, the results strongly support separability (a macrolevel, across-stimulus form of invariance) for all pairs of facial dimensions used. This separability was confirmed by the subsequent identification task. Owing to its greater resolution, the identification task can reveal interactions that might exist at more microlevels of processing. In fact, the identification data did indicate the presence of perceptual dependence between facial dimensions within a stimulus when the dimensions that were varied were close in spatial proximity (i.e., eye separation and nose length). Within the theoretical framework, perceptual dependence can be interpreted as correlated noise between otherwise separate channels (and hence, is logically distinct from separability). This dependence was greatly reduced for dimensions that were more distant (eyes and mouth). The relation between these results and the configural effects that have been observed with faces as stimuli in other studies is discussed.

Perceptual interactions of facial dimensions in speeded classification and identification