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,

Decision-making relies on a process of evidence accumulation which generates support for possible hypotheses. Models of this process derived from classical stochastic theories assume that information accumulates by moving across definite levels of evidence, carving out a single trajectory across these levels over time. In contrast, quantum decision models assume that evidence develops over time in a superposition state analogous to a wavelike pattern and that judgments and decisions are constructed by a measurement process by which a definite state of evidence is created from this indefinite state. This constructive process implies that interference effects should arise when multiple responses (measurements) are elicited over time. We report such an interference effect during a motion direction discrimination task. Decisions during the task interfered with subsequent confidence judgments, resulting in less extreme and more accurate judgments than when no decision was elicited. These results provide qualitative and quantitative support for a quantum random walk model of evidence accumulation over the popular Markov random walk model. We discuss the cognitive and neural implications of modeling evidence accumulation as a quantum dynamic system.ecisions in a wide range of tasks (e.g., inferring the presence or absence of a disease, the guilt or innocence of a suspect, and the left or right direction of enemy movement) require evidence to be accumulated in support of different hypotheses. Arguably, the most successful theory of evidence accumulation in humans and other animals is Markov random walk (MRW) theory (and diffusion models, their continuous space extensions) (1, 2). MRWs can be viewed as psychological implementations of a first-order Bayesian inference process that assigns a posterior probability to each hypothesis (3). MRWs can account for choices, response times, and confidence for a variety of different decision types (2, 4). Moreover, these models of the accumulation process have been connected to neural activity during decision-making (5, 6).According to MRW models, when deciding between two hypotheses, the cumulative evidence for or against each hypothesis realizes different levels at different times to generate a single particle-like trajectory of evidence levels across time (Fig. 1). At any point in time, the decision-maker has a definite level of evidence, and choices are made by comparing the existing level of evidence against a criterion. Evidence above the criterion favors one option, and evidence below it favors the alternative. Other responses are modeled in a similar manner; for example, confidence ratings are modeled by mapping evidence states onto one or more ratings (4). However, this idea that judgments and decisions are simply read out from the existing level of evidence-henceforth referred to as the "read-out" assumption-is inconsistent with the well-established idea that preferences and beliefs are constructed rather than revealed by judgments and decisions (7).We present an alternati...

Evidence for different hypotheses is often treated as a singular construct, but it can be dissociated into two parts: its strength, the proportion of pieces of information favoring one hypothesis; and its weight, the total number of pieces of information available. However, cognitive and neural models of evidence accumulation often make a proportional representation assumption, implying that people take these two factors into account equally when making their decisions and judgments. We examine this assumption by directly manipulating the number of samples and the proportion favoring either of two alternatives in dynamic decision making and judgment tasks. The results suggest that people tend to over-emphasize the strength of evidence relative to its weight in both an optional-stopping decision task and a probability judgment task. In a drift-diffusion model, this is reflected by drift rates that are determined foremost by strength with a smaller influence of weight. This result challenges the proportional representation assumption made by existing models of judgment and decision-making, and calls into question modeling evidence accumulation as a Bayesian belief updating process.