Using a Neural Network Model with Synaptic Depression to Assess the Dynamics of Feature-Based Versus Configural Processing in Face Identification
نویسندگان
چکیده
Accounting for the finding that brief prime durations facilitate perception of immediate word repetitions whereas long prime durations are detrimental, Huber and O’Reilly (2003) proposed a neural network model in which the unwanted effects of perceptual persistence are counteracted through activity dependent synaptic depression. Rieth and Huber (in prep) found similar results with immediate face repetitions, manipulating featural versus configural processing by means of face inversion. We extend the neural network model to face perception and account for individual differences by assuming some participants perform the task on the basis of feature identification, corresponding to the second layer of the 3-layer network, whereas other participants perform the task on the basis of configural identification, corresponding to the top layer. Under these assumptions, the model is used to describe the dynamics for each type of processing, with the resultant parameters revealing that configural identification integrates information at a faster rate than feature identification.
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