Timing of readiness potentials reflect a decision-making process in the human brain

Abstract

Perceptual decision making has several underlying components including stimulus encoding, perceptual categorization, response selection, and response execution. Evidence accumulation is believed to be the underlying mechanism of decision-making and plays a decisive role in determining response time. Previous studies in animals and humans have shown parietal cortex activity that exhibits characteristics of evidence accumulation in tasks requiring difficult perceptual categorization to reach a decision. In this study, we made use of a task where the challenge for the participants is to identify the stimulus and then from memory apply an abstract rule to select one of two possible actions. The task was designed so that stimulus identification was easy but response selection required cognitive computations and working memory. In simultaneous EEG recordings, we find a one-to-one relationship between the duration of the readiness potential observed prior to the response over motor areas, and decision-making time estimated by a drift-diffusion model of the response time distribution. This close relationship implies that the readiness potential reflects an evidence accumulation process for response selection, and supports the notion that evidence accumulation is a general neural implementation of decision-making. The evidence accumulation process that captures variability in decision-making time will depend on the location of the bottleneck in information processing.

The latency of a visual evoked potential tracks the onset of decision making

“…While the median posterior distributions range from 170 to 260 milliseconds, the best estimate of MET is given by the condition with the slope parameter posterior closest to 1, implied by the High Noise contrast condition in Experiment 1. This suggests that the best estimate for MET is 260 milliseconds, which matches estimates for MET we found in another study by Lui et al (2018).…”

Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans

“…By integrating single-trial EEG regressors with the cognitive model, we identified the accumulation rate to be affected by the rate of EEG activity changes between visual N100 and P300 components. This result contributes to a growing literature of EEG markers of evidence accumulation processes, including ERP components (Twomey et al 2015;Loughnane et al 2016;Nunez et al 2017), readiness potential (Lui et al 2018), and oscillatory power (van Vugt et al 2012). It further consolidates the validity of evidence accumulation as a common computational mechanism leading to voluntary choices of rewarding stimuli (Summerfield and Tsetsos 2012;Afacan-Seref et al 2018;Maoz et al 2019), beyond its common applications to perceptually difficult and temporally extended paradigms.…”

Thalamocortical excitability adjustments guide human perception under uncertainty

“…Notably, we observed a late frontal potential that increased in amplitude ( Figure S3D) and whose onset corresponded to the temporal NDT shift ( Figure S3C) after controlling for constant encoding duration ( Figure S3B). This suggests that baseline NDT estimates approximate the duration of probe encoding (Nunez, Vandekerckhove, & Srinivasan, 2017), whereas NDT increases characterize increased demands for transforming the sensory decision into a motor command (Lui et al, 2018). This further suggests that drift diffusion modelling successfully dissociated contributions from evidence integration, sensory encoding, and motor preparation.…”

“…CPP slopes (i.e., evidence drift) exhibited load-related decreases for each probed attribute, whereas no threshold modulation was indicated for any of the probed attributes. [* p <.05; ** p <.01; *** p < .001] Lui et al, 2018). Response counts (here shown for EEG session) were sorted into 40 bins of 50 ms each.…”