TY - JOUR

T1 - Gaussian counter models for visual identification of briefly presented, mutually confusable single stimuli in pure accuracy tasks

AU - Tamborrino, Massimiliano

AU - Ditlevsen, Susanne

AU - Markussen, Bo

AU - Kyllingsbæk, Søren

PY - 2017

Y1 - 2017

N2 - When identifying confusable visual stimuli, accumulation of information over time is an obviousstrategy of the observer. However, the nature of the accumulation process is unresolved: for exampleit may be discrete or continuous in terms of the information encoded. Another unanswered questionis whether or not stimulus sampling continues after the stimulus offset. In the present paper wepropose various continuous Gaussian counter models of the time course of visual identification of brieflypresented, mutually confusable single stimuli in a pure accuracy task. During stimulus analysis, tentativecategorizations that stimulus i belongs to category j are made until a maximum time after the stimulusdisappears. Two classes of models are proposed. First, the overt response is based on the categorizationthat had the highest value at the time the stimulus disappears (race models). Second, the overt responseis based on the categorization that made the minimum first passage time through a constant boundary(first passage time models).Within this framework, multivariateWiener and Ornstein–Uhlenbeck countermodels are considered under different parameter regimes, assuming either that the stimulus samplingstops immediately or that it continues for some time after the stimulus offset. Each type of model wasevaluated by Monte Carlo tests of goodness of fit against observed probability distributions of responsesin two extensive experiments. A comparison of these continuous models with a simple discrete Poissoncounter model proposed by Kyllingsbæk, Markussen, and Bundesen (2012) was carried out, togetherwith model selection among the competing candidates. Both the Wiener and the Ornstein–Uhlenbeckrace 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.

AB - When identifying confusable visual stimuli, accumulation of information over time is an obviousstrategy of the observer. However, the nature of the accumulation process is unresolved: for exampleit may be discrete or continuous in terms of the information encoded. Another unanswered questionis whether or not stimulus sampling continues after the stimulus offset. In the present paper wepropose various continuous Gaussian counter models of the time course of visual identification of brieflypresented, mutually confusable single stimuli in a pure accuracy task. During stimulus analysis, tentativecategorizations that stimulus i belongs to category j are made until a maximum time after the stimulusdisappears. Two classes of models are proposed. First, the overt response is based on the categorizationthat had the highest value at the time the stimulus disappears (race models). Second, the overt responseis based on the categorization that made the minimum first passage time through a constant boundary(first passage time models).Within this framework, multivariateWiener and Ornstein–Uhlenbeck countermodels are considered under different parameter regimes, assuming either that the stimulus samplingstops immediately or that it continues for some time after the stimulus offset. Each type of model wasevaluated by Monte Carlo tests of goodness of fit against observed probability distributions of responsesin two extensive experiments. A comparison of these continuous models with a simple discrete Poissoncounter model proposed by Kyllingsbæk, Markussen, and Bundesen (2012) was carried out, togetherwith model selection among the competing candidates. Both the Wiener and the Ornstein–Uhlenbeckrace 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.

U2 - 10.1016/j.jmp.2017.02.003

DO - 10.1016/j.jmp.2017.02.003

M3 - Journal article

VL - 79

SP - 85

EP - 103

JO - Journal of Mathematical Psychology

JF - Journal of Mathematical Psychology

SN - 0022-2496

ER -