A Dynamic Object Recognition Model for Decisions Made Over Dynamic Stimuli

AbstractWe report a model for object identification based on an experiment that varies the arrival times of different features of the objects. A single object, a circle with four spokes extending in different directions, is presented and must be classified as either one of four well trained target stimuli, or one of four well trained foil stimuli. The features (spokes) are presented either simultaneously or successively at intervals of 16, 33, or 50 ms., with target diagnostic features arriving first or last. All durations are short enough that the display appears simultaneous. The data show that individual decisions vary with both timing and diagnosticity. We apply a dynamic model based on one reported in \cite{Cox2017a} for episodic recognition memory. Our model assumes features are perceived at varying times following presentation, possibly in error. At each moment the current features are compared to the well learned memory representations of the eight stimuli, producing a likelihood ratio for target vs foil. A decision is made when the log likelihood first exceeds a target decision boundary or falls below a foil decision boundary. The model implements a form of Bayesian optimal decision making given the assumptions concerning feature perception. It predicts the key findings quite well.

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