Modeling visual search using three-parameter probability functions in a hierarchical Bayesian framework

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Modeling visual search using three-parameter probability functions in a hierarchical Bayesian framework. / Lin, Yi-Shin; Heinke, Dietmar; Humphreys, Glyn.

In: Attention, perception & psychophysics, Vol. 77, No. 3, 04.2015, p. 985-1010.

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@article{53b63ca81f344a9d89ecccc00eea55d8,
title = "Modeling visual search using three-parameter probability functions in a hierarchical Bayesian framework",
abstract = "In this study, we applied Bayesian-based distributional analyses to examine the shapes of response time (RT) distributions in three visual search paradigms, which varied in task difficulty. In further analyses we investigated two common observations in visual search—the effects of display size and of variations in search efficiency across different task conditions—following a design that had been used in previous studies (Palmer, Horowitz, Torralba, & Wolfe, Journal of Experimental Psychology: Human Perception and Performance, 37, 58–71, 2011; Wolfe, Palmer, & Horowitz, Vision Research, 50, 1304–1311, 2010) in which parameters of the response distributions were measured. Our study showed that the distributional parameters in an experimental condition can be reliably estimated by moderate sample sizes when Monte Carlo simulation techniques are applied. More importantly, by analyzing trial RTs, we were able to extract paradigm-dependent shape changes in the RT distributions that could be accounted for by using the EZ2 diffusion model. The study showed that Bayesian-based RT distribution analyses can provide an important means to investigate the underlying cognitive processes in search, including stimulus grouping and the bottom-up guidance of attention.",
author = "Yi-Shin Lin and Dietmar Heinke and Glyn Humphreys",
year = "2015",
month = apr,
doi = "10.3758/s13414-014-0825-x",
language = "English",
volume = "77",
pages = "985--1010",
journal = "Attention, perception & psychophysics",
issn = "1943-3921",
publisher = "Springer",
number = "3",

}

RIS

TY - JOUR

T1 - Modeling visual search using three-parameter probability functions in a hierarchical Bayesian framework

AU - Lin, Yi-Shin

AU - Heinke, Dietmar

AU - Humphreys, Glyn

PY - 2015/4

Y1 - 2015/4

N2 - In this study, we applied Bayesian-based distributional analyses to examine the shapes of response time (RT) distributions in three visual search paradigms, which varied in task difficulty. In further analyses we investigated two common observations in visual search—the effects of display size and of variations in search efficiency across different task conditions—following a design that had been used in previous studies (Palmer, Horowitz, Torralba, & Wolfe, Journal of Experimental Psychology: Human Perception and Performance, 37, 58–71, 2011; Wolfe, Palmer, & Horowitz, Vision Research, 50, 1304–1311, 2010) in which parameters of the response distributions were measured. Our study showed that the distributional parameters in an experimental condition can be reliably estimated by moderate sample sizes when Monte Carlo simulation techniques are applied. More importantly, by analyzing trial RTs, we were able to extract paradigm-dependent shape changes in the RT distributions that could be accounted for by using the EZ2 diffusion model. The study showed that Bayesian-based RT distribution analyses can provide an important means to investigate the underlying cognitive processes in search, including stimulus grouping and the bottom-up guidance of attention.

AB - In this study, we applied Bayesian-based distributional analyses to examine the shapes of response time (RT) distributions in three visual search paradigms, which varied in task difficulty. In further analyses we investigated two common observations in visual search—the effects of display size and of variations in search efficiency across different task conditions—following a design that had been used in previous studies (Palmer, Horowitz, Torralba, & Wolfe, Journal of Experimental Psychology: Human Perception and Performance, 37, 58–71, 2011; Wolfe, Palmer, & Horowitz, Vision Research, 50, 1304–1311, 2010) in which parameters of the response distributions were measured. Our study showed that the distributional parameters in an experimental condition can be reliably estimated by moderate sample sizes when Monte Carlo simulation techniques are applied. More importantly, by analyzing trial RTs, we were able to extract paradigm-dependent shape changes in the RT distributions that could be accounted for by using the EZ2 diffusion model. The study showed that Bayesian-based RT distribution analyses can provide an important means to investigate the underlying cognitive processes in search, including stimulus grouping and the bottom-up guidance of attention.

U2 - 10.3758/s13414-014-0825-x

DO - 10.3758/s13414-014-0825-x

M3 - Article

VL - 77

SP - 985

EP - 1010

JO - Attention, perception & psychophysics

JF - Attention, perception & psychophysics

SN - 1943-3921

IS - 3

ER -