September 2005
Volume 5, Issue 8
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2005
Predicting human face discrimination performance and FFA activation using a computational model of face neurons
Author Affiliations
  • Xiong Jiang
    Dept. of Neuroscience, Georgetown University Medical Center, Washington, DC, USA
  • Tom Zeffiro
    Dept. of Neurology and Center for Functional and Molecular Imaging, Georgetown University Medical Center, Washington, DC, USA
  • John VanMeter
    Dept. of Neurology and Center for Functional and Molecular Imaging, Georgetown University Medical Center, Washington, DC, USA
  • Volker Blanz
    Max Planck Center for Visual Computing and Communication, Saarbrucken, Germany/Stanford, CA, USA
  • Maximilian Riesenhuber
    Dept. of Neuroscience, Georgetown University Medical Center, Washington, DC, USA
Journal of Vision September 2005, Vol.5, 631. doi:10.1167/5.8.631
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      Xiong Jiang, Tom Zeffiro, John VanMeter, Volker Blanz, Maximilian Riesenhuber; Predicting human face discrimination performance and FFA activation using a computational model of face neurons. Journal of Vision 2005;5(8):631. doi: 10.1167/5.8.631.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

The nature of the quantitative relationship between stimuli, neural activation and behavior is crucial to understanding how the brain performs complex cognitive tasks such as face perception, yet it is still poorly understood. We have recently presented a computational model of face processing in cortex (Rosen & Riesenhuber, VSS, 2004). The model shows that a population of highly selective “face neurons” (FN) can explain human face discrimination performance and effects such as the “Face Inversion Effect”. This predicts a direct link between FN tuning specificity and discrimination performance: If face discrimination is based on the comparisons of FN activation patterns, performance should increase with dissimilarity between target (T) and distractor (D) faces, as the corresponding activation patterns get increasingly dissimilar. Crucially, due to the tight tuning of FNs, for some T-D dissimilarity, both will activate disjoint subpopulations of FNs, and performance should asymptote, as further increasing the T-D dissimiliarity will not increase the dissimilarity of FN activation patterns. Likewise, in an fMRI rapid adaptation paradigm (fMRI-RA), adaptation of FFA FN stimulated with pairs of faces of increasing dissimilarity should decrease, and asymptote when the faces activate different subpopulations of FN. We used the model of FN to quantitatively predict the T-D dissimilarity (using morphed faces of parametrically varied similarity (Blanz & Vetter, 1999)) for which BOLD adaptation and behavior are expected to asymptote. We then conducted psychophysical (2AFC, 9 subjects) and fMRI-RA experiments (6 subjects, 3T Siemens Trio magnet) to test these predictions. We find that i) behavioral performance asymptotes as predicted, ii) BOLD adaptation decreases and asymptotes with increasing T-D dissimilarity, as predicted, and iii) the asymptotes in behavior and fMRI are in good agreement with model predictions. This supports the predicted quantitative link of FN tuning, FFA response, and behavior.

Jiang, X. Zeffiro, T. VanMeter, J. Blanz, V. Riesenhuber, M. (2005). Predicting human face discrimination performance and FFA activation using a computational model of face neurons [Abstract]. Journal of Vision, 5(8):631, 631a, http://journalofvision.org/5/8/631/, doi:10.1167/5.8.631. [CrossRef]
Footnotes
 NIH 1P20MH66239-01A1
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