No abstract

Categorical choices are preceded by the accumulation of sensory evidence in favor of one action or another. Current models describe evidence accumulation as a continuous process occurring at a constant rate, but this view is inconsistent with accounts of a psychological refractory period during sequential information processing. During multisample perceptual categorization, we found that the neural encoding of momentary evidence in human electrical brain signals and its subsequent impact on choice fluctuated rhythmically according to the phase of ongoing parietal delta oscillations (1-3 Hz). By contrast, lateralized beta-band power (10-30 Hz) overlying human motor cortex encoded the integrated evidence as a response preparation signal. These findings draw a clear distinction between central and motor stages of perceptual decision making, with successive samples of sensory evidence competing to pass through a serial processing bottleneck before being mapped onto action.

An optimal agent will base judgments on the strength and reliability of decision-relevant evidence. However, previous investigations of the computational mechanisms of perceptual judgments have focused on integration of the evidence mean (i.e., strength), and overlooked the contribution of evidence variance (i.e., reliability). Here, using a multielement averaging task, we show that human observers process heterogeneous decision-relevant evidence more slowly and less accurately, even when signal strength, signal-to-noise ratio, category uncertainty, and low-level perceptual variability are controlled for. Moreover, observers tend to exclude or downweight extreme samples of perceptual evidence, as a statistician might exclude an outlying data point. These phenomena are captured by a probabilistic optimal model in which observers integrate the log odds of each choice option. Robust averaging may have evolved to mitigate the influence of untrustworthy evidence in perceptual judgments.decision making | diffusion model | information integration P erceptual judgments typically involve a deliberative process in which evidence concerning the current state of the external world is considered. Over recent years, the twin goals of characterizing the computational mechanisms and the neural representations underlying this deliberation have come to the fore (1, 2). Because sensory evidence coming from the external world is intrinsically noisy, decisions will benefit from repeated sampling and accumulation of the collected evidence (3-5). Mathematical modeling studies support the view that serial sampling is a basic principle of choice behavior (3, 6-9), and recent neurophysiological recordings identify the parietal cortex as a candidate site for evidence accumulation in psychophysical tasks (10-14). However, the precise computations by which a decision variable (DV) is constructed and updated during decision making remain controversial (2, 4).One popular framework posits that integration is a simple summation process under which choices and their latencies depend linearly on the strength of sensory input (3,15,16). This mechanism is often illustrated by analogy to a court of law, where the jury tots up evidence for or against a guilty verdict (1). However, in a stochastic environment, committing to an action on the basis of evidence strength alone can be suboptimal, because evidence may strongly favor one option over another just by chance (17, 18). Rather, a statistically optimal policy is to base decisions on independent estimates of the strength (i.e., mean) and reliability (i.e., variance) of the currently extant sensory evidence, just as a researcher might compare two samples of data on the basis of an inferential statistic rather than merely calculating their central tendencies (19). To continue the courtroom analogy, a shrewd jury will consider not only how incriminating evidence is, but also the trustworthiness of the source of the evidence. These two factors are not necessarily correlated: For example, severely indic...

Understanding how people rate their confidence is critical for characterizing a wide range of perceptual, memory, motor, and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations, and fields of study. The data from each study are structured in a common,