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The Brain behind Facial Recognition

ntally by exposing volunteers to images of faces that could be precisely "morphed" with a computer program to subtly alter the facial features. And at the same time, the subjects' brains were scanned by using functional magnetic resonance imaging (fMRI) to detect patterns of activity in the FFA. The technique of fMRI involves using harmless magnetic fields and radio waves to measure blood flow in brain regions, which reflects their activity. The researchers found that the results from the fMRI studies agreed with those of the computational model.

The researchers concluded that "we have shown that a computational implementation of a physiologically plausible neural model of face processing can quantitatively account for key data, leading to the prediction that human face discrimination is based on a sparse population code of sharply tuned face neurons.

"In particular, we have shown that a shape-based model can account for the face inversion effect, can produce 'configural' effects without explicit configural coding, and can quantitatively account for the experimental data. The model thus constitutes a computational counterexample to theories that posit that human face discrimination necessarily relies on face-specific processes."

In a preview of the paper in the same issue of Neuron, Tzvi Ganel wrote that Riesenhuber and colleagues "provide a compelling array of evidence supporting the idea that the processing of faces and objects do not rely on qualitatively different mechanisms. In a series of experiments, Jiang et al. present and integrate findings from neural modeling, behavior, and fMRI, showing that face classification, similarly to object classification, can be achieved by a simple-to-complex architecture based on hierarchical shape detectors.

"Jiang et al.'s modeling and behavioral findings have strong implications for understanding how faces and objects are processed in the human brain," wrote Ganel.

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