August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
Testing the independence of neural representations of face identity and expression through multidimensional signal detection theory
Author Affiliations
  • Fabian Soto
    Department of Psychology, Florida International University
  • Lauren Vucovich
    Department of Psychological & Brain Sciences, University of California, Santa Barbara
  • F. Greg Ashby
    Department of Psychological & Brain Sciences, University of California, Santa Barbara
Journal of Vision September 2016, Vol.16, 1242. doi:10.1167/16.12.1242
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      Fabian Soto, Lauren Vucovich, F. Greg Ashby; Testing the independence of neural representations of face identity and expression through multidimensional signal detection theory . Journal of Vision 2016;16(12):1242. doi: 10.1167/16.12.1242.

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

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Abstract

Many research questions in visual perception–particularly those dealing with notions such as "independence," "invariance," and "holism" of visual representations–are special cases of the problem of perceptual separability. In visual neuroscience, there is great interest in determining to what extent important object dimensions are represented separately in the brain. However, progress in the study of separability has been hindered by inadequate methods for its detection. In particular, the definitions of independent or separable representations used in most published research are operational, lacking a theory to guide the interpretation of results. Here we describe a new test of perceptual separability for fMRI data, based on a theoretical definition from multidimensional signal detection theory. The test is essentially an extension to multi-voxel pattern analyses: a linear classifier is used to classify activity patterns related to individual stimulus presentations on the basis of a specific stimulus dimension (e.g., "sad" vs. "neutral" faces). The data points are then projected to a line orthogonal to the classification bound (i.e., the "emotion" dimension) and used to estimate a probabilistic perceptual distribution for each stimulus, as proposed by signal detection theory. These estimated perceptual distributions can be used to directly assess perceptual separability. This test has a strong theoretical basis and can be related to behavioral tests of separability that have been widely applied in the past. We apply the test to the study of separability of human face identity and emotional expression. Twenty-one participants completed a face identification task with faces varying in identity and emotional expression (neutral/sad). fMRI data was analyzed using the new separability test. Violations of separability were present for both emotional expression and identity, they were widespread across areas in the face network, and they were more prevalent in the left hemisphere.

Meeting abstract presented at VSS 2016

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