Interference effects of choice on confidence: Quantum characteristics of evidence accumulation

Abstract

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...

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