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Refining understanding of human decision making by testing integrated neurocognitive models of EEG, choice and reaction time

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The cognitive process and time course of quick human decision making was evaluated using reaction time, choice distributions, and human electrophysiology as recorded by EEG. These data were used to evaluate drift-diffusion models, a class of decision-making models that assume a stochastic accumulation of evidence on each trial, within hierarchical Bayesian frameworks. The first goal was to elucidate the effect of visual attention on decision making. To this aim two studies were performed. In the first study it was found that individual differences in evidence accumulation rates and non-decision time (preprocessing and motor response times) can be explained by attentional differences as measured by steady-state visual evoked potential (SSVEP) responses to the flicker frequency of signal and noise components of the visual stimulus. Participants who were able to suppress their SSVEP response to visual noise in high frequency bands were able to accumulate correct evidence faster and had shorter non-decision times, leading to more accurate responses and faster response times. In the second study it was found 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. That is, 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. The second goal was obtain inference about the time course of quick decision making. A method of estimating and verifying individuals' visual encoding time is proposed using traditional event-related potential (ERP) measures. The possibility of using single-trial N200 and trial-averaged N200 ERP latencies as estimates of human visual encoding time is explored using both simple linear regression and complex hierarchical Bayesian modeling. Posterior distributions of linear-effect parameters suggest that EEG responses to the onset of visual stimuli reflect stimulus encoding times. The possibility of using a verifiable EEG measure of the time course of motor preparation is also explored. Finally, a theoretical cognitive framework for quick decision making is proposed which assumes differential mechanisms of visual encoding, drift-diffusion evidence accumulation, and motor response.

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