Abstract
Numerous psychophysical experiments found that humans preferably rely on a narrow band of spatial frequencies for recognition of face identity. A recently conducted theoretical study by the author suggests that this frequency preference reflects an adaptation of the brain's face processing machinery to this specific stimulus class (i.e., faces). The purpose of the present study is to examine this property in greater detail and to specifically elucidate the implication of internal face features (i.e., eyes, mouth, and nose). To this end, I parameterized Gabor filters to match the spatial receptive field of contrast sensitive neurons in the primary visual cortex (simple and complex cells). Filter responses to a large number of face images were computed, aligned for internal face features, and response-equalized (“whitened”). The results demonstrate that the frequency preference is caused by internal face features. Thus, the psychophysically observed human frequency bias for face processing seems to be specifically caused by the intrinsic spatial frequency content of internal face features.
Highlights
In the brain, the structure of neuronal circuits for processing sensory information matches the statistical properties of the sensory signals [1]
Visual neurons would have equal sensitivities or response amplitudes independent of their spatial frequency preference [16]. According to this response equalization hypothesis, gain should be incremented with increasing spatial frequency, such that the distribution of response amplitudes of frequency-tuned neurons to a typical natural image is flat
The results presented in [20] indicate that the maxima in the amplitude spectra are caused by the compound effect of horizontally oriented internal face features
Summary
The structure of neuronal circuits for processing sensory information matches the statistical properties of the sensory signals [1]. As for visual stimuli, natural images reveal (on the average) a conspicuous statistical regularity that comes as an approximately linear decrease of their (logarithmically scaled) amplitude spectra as a function of (log) spatial frequency [15,16,17]. This means that pairs of luminance values are strongly correlated [18], and this property could be exploited for gain controlling of visual neurons. According to this response equalization hypothesis, gain should be incremented with increasing spatial frequency, such that the distribution of response amplitudes of frequency-tuned neurons to a typical natural image is flat
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