Journal
JOURNAL OF MATHEMATICAL PSYCHOLOGY
Volume 76, Issue -, Pages 117-130Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jmp.2016.03.003
Keywords
Visual attention; Perceptual decision making; Diffusion models; Neurocognitive modeling; Electroencephalography (EEG); Hierarchical Bayesian modeling
Categories
Funding
- NIH [2R01MH68004]
- National Science Foundation (NSF) [1230118]
- Methods, Measurements, and Statistics panel [1534472]
- John Templeton Foundation [48192]
- Direct For Social, Behav & Economic Scie [1534472, 1230118] Funding Source: National Science Foundation
- Divn Of Social and Economic Sciences [1534472, 1230118] Funding Source: National Science Foundation
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Perceptual decision making can be accounted for by drift-diffusion models, a class of decision making models that assume a stochastic accumulation of evidence on each trial. Fitting response time and accuracy to a drift-diffusion model produces evidence accumulation rate and non-decision time parameter estimates that reflect cognitive processes. Our goal is to elucidate the effect of attention on visual decision making. In this study, we show that measures of attention obtained from simultaneous EEG recordings can explain per-trial evidence accumulation rates and perceptual preprocessing times during a visual decision making task. Models assuming linear relationships between diffusion model parameters and EEG measures as external inputs were fit in a single step in a hierarchical Bayesian framework. The EEG measures were features of the evoked potential (EP) to the onset of a masking noise and the onset of a task-relevant signal stimulus. Single-trial evoked EEG responses, P200s to the onsets of visual noise and N200s to the onsets of visual signal, explain single-trial evidence accumulation and preprocessing times. Within-trial evidence accumulation variance was not found to be influenced by attention to the signal or noise. Single-trial measures of attention lead to better out-of-sample predictions of accuracy and correct reaction time distributions for individual subjects. (C) 2016 Elsevier Inc. All rights reserved.
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