Breaking deadlocks: reward probability and spontaneous preference shape voluntary decisions and electrophysiological signals in humans

Choosing between equally valued options is a common conundrum, for which classical decision theories predicted a prolonged response time (RT). This contrasts with the notion that an optimal decision maker in a stable environment should make fast and random choices, as the outcomes are indifferent. Here, we characterize the neurocognitive processes underlying such voluntary decisions by integrating cognitive modelling of behavioral responses and EEG recordings in a probabilistic reward task. Human participants performed binary choices between pairs of unambiguous cues associated with identical reward probabilities at different levels. Higher reward probability accelerated RT, and participants chose one cue faster and more frequent over the other at each probability level. The behavioral effects on RT persisted in simple reactions to single cues. By using hierarchical Bayesian parameter estimation for an accumulator model, we showed that the probability and preference effects were independently associated with changes in the speed of evidence accumulation, but not with visual encoding or motor execution latencies. Time-resolved MVPA of EEG-evoked responses identified significant representations of reward certainty and preference as early as 120 ms after stimulus onset, with spatial relevance patterns maximal in middle central and parietal electrodes. Furthermore, EEG-informed computational modelling showed that the rate of change between N100 and P300 event-related potentials modulated accumulation rates on a trial-by-trial basis. Our findings suggest that reward probability and spontaneous preference collectively shape voluntary decisions between equal options, providing a mechanism to prevent indecision or random behavior

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

Encoding of a sensory stimulus is believed to be the first step in perceptual decision making. Previous research has shown that electrical signals recorded from the human brain track evidence accumulation during perceptual decision making (Gold and Shadlen, 2007; O'Connell et al., 2012; Philiastides et al., 2014). In this study we directly tested the hypothesis that the latency of the N200 recorded by EEG (a negative peak occurring between 150 and 275 ms after stimulus presentation in human participants) reflects the visual encoding time (VET) required for completion of figure-ground segregation before evidence accumulation. We show that N200 latencies vary across individuals, are modulated by external visual noise, and increase response time by x milliseconds when they increase by x milliseconds, reflecting a linear regression slope of 1. Simulations of cognitive decision-making theory show that variation in human response times not related to evidence accumulation (non-decision time; NDT), including VET, are tracked by the fastest response times. Evidence that VET is tracked by N200 latencies was found by fitting a linear model between trial-averaged N200 latencies and the 10th percentiles of response times, a model-independent estimate of NDT. Fitting a novel neuro-cognitive model of decision making also yielded a slope of 1 between N200 latency and model-estimated NDT in multiple visual noise conditions, indicating that N200 latencies track the completion of visual encoding and the onset of evidence accumulation. The N200 waveforms were localized to the cortical surface at distributed temporal and extrastriate locations, consistent with a distributed network engaged in figure-ground segregation of the target stimulus

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

In this study, we directly tested the hypothesis that N200 latencies (EEG stimulus-locked negative peaks occurring between 150 and 275 milliseconds in human subjects) reflect visual processing time that occurs before evidence accumulation during quick decision making