Informing computational models of perceptual and risky decision making with EEG and individual differences
We use hierarchical estimation of a drift diffusion model (HDDM) in conjunction with neural data (EEG) and individual differences to understand and compare perceptual and value-based choice. For perceptual decisions, participants selected the more horizontally-oriented grating among a pair, with orientations across pairs designed to produce easy vs. difficult trials. For value-based choice, participants selected their preference among pairs of gambles with two equiprobable outcomes. Gamble pairs had equal expected values but different outcome ranges (risk), and we varied the difference between their ranges to produce similar vs. different levels of risk. We collected EEG data throughout both tasks and calculated a variety of frequency-based (N200, CPP) and time-based (parietal theta, gamma) measures to serve as continuous regressors in determining the HDDM model parameters. Finally, participants self-reported individual difference variables on decision-making styles, impulsivity, and personality. We present results that show the effects of task type, stimulus condition, and EEG signals on model parameters, such as lower drift rates for more difficult perceptual tasks and more similar risk levels. We also provide correlations between individually-estimated model parameters and relevant individual difference measures, such as lower thresholds for more intuitive decision makers. In total, we deploy a unique collection of behavioral tasks, physiological data, psychometric variables, and computational modeling to better understand decision processes.
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