Abstract
Sequential sampling decision-making models have been successful in accounting for reaction time (RT) and accuracy data in two-alternative forced choice tasks. These models have been used to describe the behavior of populations of participants, and explanatory structures have been proposed to account for between individual variability in model parameters. In this study we show that individual differences in behavior from a novel perceptual decision making task can be attributed to (1) differences in evidence accumulation rates, (2) differences in variability of evidence accumulation within trials, and (3) differences in non-decision times across individuals. Using electroencephalography (EEG), we demonstrate that these differences in cognitive variables, in turn, can be explained by attentional differences as measured by phase-locking of steady-state visual evoked potential (SSVEP) responses to the signal and noise components of the visual stimulus. Parameters of a cognitive model (a diffusion model) were obtained from accuracy and RT distributions and related to phase-locking indices (PLIs) of SSVEPs with a single step in a hierarchical Bayesian framework. Participants who were able to suppress the SSVEP response to visual noise in high frequency bands were able to accumulate correct evidence faster and had shorter non-decision times (preprocessing or motor response times), leading to more accurate responses and faster response times. We show that the combination of cognitive modeling and neural data in a hierarchical Bayesian framework relates physiological processes to the cognitive processes of participants, and that a model with a new (out-of-sample) participant's neural data can predict that participant's behavior more accurately than models without physiological data.
Highlights
The joint analysis of physiological and behavioral data has been a topic of recent interest
We apply a cognitive model constrained by EEG data to fit accuracy and response times of multiple individuals from a perceptual decision making task
The variability of evidence units gained per second ςk increased as bar rotation (BR) variance grew
Summary
The joint analysis of physiological and behavioral data has been a topic of recent interest. In a string of publications, a number of research groups (Forstmann et al, 2010; Turner et al, 2013; Cassey et al, 2014) have presented work in which neurophysiological data are linked to parameters of cognitive or behavioral process models (see Palmeri et al, in preparation). The goal of these modeling exercises is to evaluate the predictive power of brain activity for behavior, and to elucidate the nature of this prediction. The goal of the model fit is twofold: (1) to demonstrate the superior generalizability of such a model as compared to model variants without neural input components and (2) to evaluate the hypothesis that individual differences in enhancement or suppression of visual attention, as measured by EEG, contribute to individual differences in cognition and to individual differences in accuracy and/or reaction time in the task
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