Forward masking of faces by spatially quantized random and structured masks: On the roles of wholistic configuration, local features, and spatial-frequency spectra in perceptual identification

The forward masking of faces by spatially quantized masking images was studied. Masks were used in order to exert different types of degrading effects on the early representations in facial information processing. Three types of source images for masks were used: Same-face images (with regard to targets), different-face images, and random Gaussian noise that was spectrally similar to facial images. They were all spatially quantized over the same range of quantization values. Same-face masks had virtually no masking effect at any of the quantization values. Different-face masks had strong masking effects only with fine-scale quantization, but led to the same efficiency of recognition as in the same-face mask condition with the coarsest quantization. Moreover, compared with the noise-mask condition, coarsely quantized different-face masks led to a relatively facilitated level of recognition efficiency. The masking effect of the noise mask did not vary significantly with the coarseness of quantization. The results supported neither a local feature processing account, nor a generalized spatial-frequency processing account, but were consistent with the microgenetic configuration-processing theory of face recognition. Also, the suitability of a spatial quantization technique for image configuration processing research has been demonstrated.