Dataset Information

"I look in your eyes, honey": internal face features induce spatial frequency preference for human face processing.

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.

SUBMITTER: Keil MS

PROVIDER: S-EPMC2653192 | biostudies-other | 2009 Mar

REPOSITORIES: biostudies-other

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"I look in your eyes, honey": internal face features induce spatial frequency preference for human face processing.

Keil Matthias S MS

PLoS computational biology 20090327 3

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 ( ...[more]

PMID: 19325870

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Project description:Task-optimized convolutional neural networks (CNNs) show striking similarities to the ventral visual stream. However, human-imperceptible image perturbations can cause a CNN to make incorrect predictions. Here we provide insight into this brittleness by investigating the representations of models that are either robust or not robust to image perturbations. Theory suggests that the robustness of a system to these perturbations could be related to the power law exponent of the eigenspectrum of its set of neural responses, where power law exponents closer to and larger than one would indicate a system that is less susceptible to input perturbations. We show that neural responses in mouse and macaque primary visual cortex (V1) obey the predictions of this theory, where their eigenspectra have power law exponents of at least one. We also find that the eigenspectra of model representations decay slowly relative to those observed in neurophysiology and that robust models have eigenspectra that decay slightly faster and have higher power law exponents than those of non-robust models. The slow decay of the eigenspectra suggests that substantial variance in the model responses is related to the encoding of fine stimulus features. We therefore investigated the spatial frequency tuning of artificial neurons and found that a large proportion of them preferred high spatial frequencies and that robust models had preferred spatial frequency distributions more aligned with the measured spatial frequency distribution of macaque V1 cells. Furthermore, robust models were quantitatively better models of V1 than non-robust models. Our results are consistent with other findings that there is a misalignment between human and machine perception. They also suggest that it may be useful to penalize slow-decaying eigenspectra or to bias models to extract features of lower spatial frequencies during task-optimization in order to improve robustness and V1 neural response predictivity.

Dataset Information

"I look in your eyes, honey": internal face features induce spatial frequency preference for human face processing.

Publications

"I look in your eyes, honey": internal face features induce spatial frequency preference for human face processing.

Similar Datasets

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