Gaussian Counter Models for Visual Identification of Briefly Presented, Mutually Confusable Single Stimuli in Pure Accuracy Tasks
Sprache des Titels:
When identifying confusable visual stimuli, accumulation of information over time is an obvious strategy of the observer. However, the nature of the accumulation process is unresolved: for example it may be discrete or continuous in terms of the information encoded. Another unanswered question is whether or not stimulus sampling continues after the stimulus offset. In the present paper we propose various continuous Gaussian counter models of the time course of visual identification of briefly presented, mutually confusable single stimuli in a pure accuracy task. During stimulus analysis, tentative categorizations that stimulus i belongs to category j are made until a maximum time after the stimulus disappears. Two classes of models are proposed. First, the overt response is based on the categorization that had the highest value at the time the stimulus disappears (race models). Second, the overt response is based on the categorization that made the minimum first passage time through a constant boundary (first passage time models). Within this framework, multivariate Wiener and Ornstein?Uhlenbeck counter models are considered under different parameter regimes, assuming either that the stimulus sampling stops immediately or that it continues for some time after the stimulus offset. Each type of model was evaluated by Monte Carlo tests of goodness of fit against observed probability distributions of responses in two extensive experiments. A comparison of these continuous models with a simple discrete Poisson counter model proposed by Kyllingsbæk, Markussen, and Bundesen (2012) was carried out, together with model selection among the competing candidates. Both the Wiener and the Ornstein?Uhlenbeck race models provide a close fit to individual data on identification of both digits and Landolt rings, outperforming the first passage time model and the Poisson counter race model.