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Object Representation                                            [back]

In everyday life, people categorize objects so easily that they do not realize how complex this task is. Objects from the same category are very different from one another. Even the same object never projects the exact same image to the retina, because of changes in the surrounding illumination, the particular viewpoint from which the person is looking at the object, how close the person is to the object, etc. Furthermore, we are capable of extracting different forms of categorical information from the same objects, depending on environmental demands (e.g., from a single face we can extract information about age, gender, race, etc.).

Extending multidimensional signal detection theory to study independent object representations

Visual processing in the brain results in a re-description of the sensory input in terms of a number of “high-level” object properties. Much effort has been dedicated to understanding which object properties are represented “independently” or “invariantly” from other properties, and which properties are represented in a more “holistic” manner. The best current framework for the study of different forms of perceptual independence is general recognition theory (GRT).  Unlike other approaches, GRT offers precise formal definitions to concepts such as “holistic,” “independent,” and “invariant” processing. Furthermore, GRT is an extension to signal detection theory and inherits from it the ability to dissociate perceptual from decisional factors in the study of perceptual independence.

Our work has extended GRT in two directions. First, we developed GRT with
Individual Differences (GRT-wIND), a model that
extending GRT
solves several problems with traditional GRT models and allows the study of individual differences in perceptual decision making. Among other applications, we have used GRT-wIND to study independent processing of face dimensions, such as identity and emotional expression.

Second, we have developed GRT tests of separability (a form of perceptual independence) that are applicable to neurobiological data. We have applied these tests to the analysis of fMRI and EEG data, with the goal of understanding how separable are the representations of face dimensions in different brain areas.

We are currently using these new GRT tools to study how new separable object dimensions are learned.

Independence of face dimensions in health and disease

Faces are some of the most important objects encountered by people in their everyday lives, and several psychological disorders (e.g., depression) involve abnormal processing of face categories (in particular, emotional expression). More specifically, previous research has shown that emotional expression interferes with processing of other face dimensions (e.g., gender) to a greater degree in people with depressive symptoms than in people without such symptoms. We are currently studying this effect using tools from multidimensional signal detection theory to determine the independence of face dimensions.