Payoff information biases a fast guess process in perceptual decision making under deadline pressure: Evidence from behavior, evoked potentials, and quantitative model comparison

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Abstract

We used electroencephalography (EEG) and behavior to examine the role of payoff bias in a difficult two-alternative perceptual decision under deadline pressure in humans. The findings suggest that a fast guess process, biased by payoff and triggered by stimulus onset, occurred on a subset of trials and raced with an evidence accumulation process informed by stimulus information. On each trial, the participant judged whether a rectangle was shifted to the right or left and responded by squeezing a right- or left-hand dynamometer. The payoff for each alternative (which could be biased or unbiased) was signaled 1.5 s before stimulus onset. The choice response was assigned to the first hand reaching a squeeze force criterion and reaction time was defined as time to criterion. Consistent with a fast guess account, fast responses were strongly biased toward the higher-paying alternative and the EEG exhibited an abrupt rise in the lateralized readiness potential (LRP) on a subset of biased payoff trials contralateral to the higher-paying alternative ~150 ms after stimulus onset and 50 ms before stimulus information influenced the LRP. This rise was associated with poststimulus dynamometer activity favoring the higherpaying alternative and predicted choice and response time. Quantitative modeling supported the fast guess account over accounts of payoff effects supported in other studies. Our findings, taken with previous studies, support the idea that payoff and prior probability manipulations produce flexible adaptations to task structure and do not reflect a fixed policy for the integration of payoff and stimulus information.

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Noorbaloochi, S., Sharon, D., & McClelland, J. L. (2015). Payoff information biases a fast guess process in perceptual decision making under deadline pressure: Evidence from behavior, evoked potentials, and quantitative model comparison. Journal of Neuroscience, 35(31), 10989–11011. https://doi.org/10.1523/JNEUROSCI.0017-15.2015

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