Recent studies have begun to elucidate the neural correlates of evidence accumulation in perceptual decision making, but few of them have used a combined modeling-electrophysiological approach to studying evidence accumulation. We introduce a multivariate approach to EEG analysis with which we can perform a comprehensive search for the neural correlate of dynamics predicted by accumulator models. We show that the dynamics of evidence accumulation are most strongly correlated with ramping of oscillatory power in the 4–9 Hz theta band over the course of a trial, although it also correlates with oscillatory power in other frequency bands. The rate of power decrease in the theta band correlates with individual differences in the parameters of drift diffusion models fitted to individuals’ behavioral data.

Functional magnetic resonance imaging (fMRI) using blood oxygenation contrast has rapidly spread into many application areas. In this paper, a new statistical model is used to evaluate the reliability of fMRI activation in a finger opposition motor paradigm for both within‐session and between‐session data and in a working memory paradigm for between‐session data. A slice prescription procedure for between‐session reproducibility is introduced. Estimates are made for the probabilities of correctly and falsely classifying voxels as active or inactive and receiver operator characteristic curves are generated. In the motor paradigm, estimated between‐session reliability was found to be somewhat reduced relative to within‐session reliability; however, this includes additional sources of variation and may not reflect intrinsically lower reliability. After matching false‐positive classification probabilities, between‐session reliability was found to be nearly identical for both motor and cognitive activation paradigms.

Background We assessed whether evidence accumulation could be observed in the BOLD signal during perceptual decision making. This presents a challenge since the hemodynamic response is slow, while perceptual decisions are typically fast. New method Guided by theoretical predictions of the drift diffusion model, we slowed down decisions by penalizing participants for incorrect responses. Second, we distinguished BOLD activity related to stimulus detection (modeled using a boxcar) from activity related to integration (modeled using a ramp) by minimizing the collinearity of GLM regressors. This was achieved by dissecting a boxcar into its two most orthogonal components: an “up-ramp” and a “down-ramp.” Third, we used a control condition in which stimuli and responses were similar to the experimental condition, but that did not engage evidence accumulation of the stimuli. Results The results revealed an absence of areas in parietal cortex that have been proposed to drive perceptual decision making but have recently come into question; and newly identified regions that are candidates for involvement in evidence accumulation. Comparison with existing methods Previous fMRI studies have either used fast perceptual decision making, which precludes the measurement of evidence accumulation, or slowed down responses by gradually revealing stimuli. The latter approach confounds perceptual detection with evidence accumulation because accumulation is constrained by perceptual input. Conclusions We slowed down the decision making process itself while leaving perceptual information intact. This provided a more sensitive and selective observation of brain regions associated with the evidence accumulation processes underlying perceptual decision making than previous methods.