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J Math Psychol. 2017 Feb;76(Pt B):117-130. doi: 10.1016/j.jmp.2016.03.003. Epub 2016 Apr 11.

How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters.

Author information

1
Department of Cognitive Sciences, University of California, Irvine, CA, USA.
2
Department of Statistics, University of California, Irvine, CA, USA.
3
Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA.
4
Department of Biomedical Engineering, University of California, Irvine, CA, USA.

Abstract

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.

KEYWORDS:

Diffusion Models; Electroencephalography (EEG); Hierarchical Bayesian modeling; Neurocognitive modeling; Perceptual decision making; Visual attention

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