Measuring internal representations from behavioral and brain data

Smith, M.L., Gosselin, F. and Schyns, P.G. (2012) Measuring internal representations from behavioral and brain data. Current Biology, 22(3), pp. 191-196. (doi:10.1016/j.cub.2011.11.061)

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Abstract

The study of internal knowledge representations is a cornerstone of the research agenda in the interdisciplinary study of cognition. An influential proposal assumes that the brain uses its internal knowledge of the external world to constrain, in a top-down manner, high-dimensional sensory data into a lower-dimensional representation that enables perceptual decisions and other higher-level cognitive functions [ [1], [2], [3], [4], [5], [6], [7], [8] and [9]]. This proposal relies on a precise formulation of the observer-specific internal knowledge (i.e., the internal representations, or models) that guides reduction of the high-dimensional retinal input onto a low-dimensional code. Here, we directly revealed the content of subjective internal representations by instructing five observers to detect a face in the presence of only white noise, to force a pure top-down, knowledge-based task. We used reverse correlation methods to visualize each observer's internal representation that supports detection of an illusory face. Using reverse correlation again, this time applied to observers' electroencephalogram activity, we established where and when in the brain specific internal knowledge conceptually interprets the input white noise as a face. We show that internal representations can be reconstructed experimentally from behavioral and brain data, and that their content drives neural activity first over frontal and then over occipitotemporal cortex.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Schyns, Professor Philippe
Authors: Smith, M.L., Gosselin, F., and Schyns, P.G.
College/School:College of Science and Engineering > School of Psychology
Journal Name:Current Biology
ISSN:0960-9822
ISSN (Online):1879-0445
Published Online:19 January 2012

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
237861Basic-level Categorisation: A Computational Model and its Empirical TestingPhilippe SchynsEconomic & Social Research Council (ESRC)R000237901Cognitive Neuroimaging & Neuroengineering Technologies