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Jeremy M Wolfe, Antonio Torralba, Todd S Horowitz; Remodeling visual search: How gamma distributions can bring those boring old RTs to life. Journal of Vision 2002;2(7):735. https://doi.org/10.1167/2.7.735.
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© ARVO (1962-2015); The Authors (2016-present)
Subjects in even simple visual search tasks often produce RT distributions with long positive tails. Typically, these long RTs are treated as noise resulting from vigilance or motor errors and are discarded. We propose instead that the skewed shape of RT distributions might tell us about the underlying cognitive architecture. These distributions turn out to be well modeled by gamma distributions (Schneider and Shiffrin Psych Rev, 1977,84, 1; McElree & Carrasco, JEPHPP, 2000, 25,1517). Gamma distributions are produced by summing processes whose durations are distributed exponentially. The distribution has two parameters: One reflects the number of sub-processes being summed; the other, the time constant of the exponential distribution of those sub-processes. Many models of visual search assume that attention is deployed from one item to the next at a relatively constant rate. If we suppose, however, that deployments are exponentially distributed in time, then we would predict gamma distributed RTs, though matters are complicated by added components in the measured RT such as motor response times. We evaluated this supposition using 4000 trials from each of 10 subjects performing a difficult spatial configuration search task. Target present and absent trials were gamma distributed. Gamma parameters are most readily interpreted for absent trials because the number of items selected by attention should be roughly constant across trials. Here fits of the parameter reflecting the number of deployments of attention increased linearly with set size while the time constant parameter remained relatively constant across set size. We describe a revised version of the Guided Search model, employing exponentially distributed deployment times, which produces a good fit to the experimental data. Supported by AFOSR
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