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Human decision making is accompanied by a sense of confidence. According to Bayesian decision theory, confidence reflects the learned probability of making a correct response, given available data (e.g., accumulated stimulus evidence and response time). Although optimal, independently learning these probabilities for all possible data combinations is computationally intractable. Here, we describe a novel model of confidence implementing a low-dimensional approximation of this optimal yet intractable solution. This model allows efficient estimation of confidence, while at the same time accounting for idiosyncrasies, different kinds of biases and deviation from the optimal probability correct. Our model dissociates confidence biases resulting from the estimate of the reliability of evidence by individuals (captured by parameter α), from confidence biases resulting from general stimulus independent under and overconfidence (captured by parameter β). We provide empirical evidence that this model accurately fits both choice data (accuracy, response time) and trial-by-trial confidence ratings simultaneously. Finally, we test and empirically validate two novel predictions of the model, namely that 1) changes in confidence can be independent of performance and 2) selectively manipulating each parameter of our model leads to distinct patterns of confidence judgments. As a tractable and flexible account of the computation of confidence, our model offers a clear framework to interpret and further resolve different forms of confidence biases., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Le Denmat et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Humans differ vastly in the confidence they assign to decisions. Although such under- and overconfidence relate to fundamental life outcomes, a computational account specifying the underlying mechanisms is currently lacking. We propose that prior beliefs in the ability to perform a task explain confidence differences across participants and tasks, despite similar performance. In two perceptual decision-making experiments, we show that manipulating prior beliefs about performance during training causally influences confidence in healthy adults ( N = 50 each; Experiment 1: 8 men, one nonbinary; Experiment 2: 5 men) during a test phase, despite unaffected objective performance. This is true when prior beliefs are induced via manipulated comparative feedback and via manipulated training-phase difficulty. Our results were accounted for within an accumulation-to-bound model, explicitly modeling prior beliefs on the basis of earlier task exposure. Decision confidence is quantified as the probability of being correct conditional on prior beliefs, causing under- or overconfidence. We provide a fundamental mechanistic insight into the computations underlying under- and overconfidence.

Affective valence and intensity form the core of our emotional experiences. It has been proposed that affect reflects the prediction error between expected and actual states, such that better/worse-than-expected discrepancies result in positive/negative affect. However, whether the same principle applies to progress prediction errors remains unclear. We empirically and computationally evaluate the hypothesis that affect reflects the difference between expected and actual progress in forming a perceptual decision. We model affect within an evidence accumulation framework where actual progress is mapped onto the drift-rate parameter and expected progress onto an expected drift-rate parameter. Affect is computed as the difference between the expected and actual amount of accumulated evidence. We find that expected and actual progress both influence affect, but in an additive manner that does not align with a prediction error account. Our computational model reproduces both task behavior and affective ratings, suggesting that sequential sampling models provide a promising framework to model progress appraisals. These results show that although affect is sensitive to both expected and actual progress, it does not reflect the computation of a progress prediction error., (© 2023. The Psychonomic Society, Inc.)

Performance monitoring is a key cognitive function, allowing to detect mistakes and adapt future behavior. Post-decisional neural signals have been identified that are sensitive to decision accuracy, decision confidence and subsequent adaptation. Here, we review recent work that supports an understanding of late error/confidence signals in terms of the computational process of post-decisional evidence accumulation. We argue that the error positivity, a positive-going centro-parietal potential measured through scalp electrophysiology, reflects the post-decisional evidence accumulation process itself, which follows a boundary crossing event corresponding to initial decision commitment. This proposal provides a powerful explanation for both the morphological characteristics of the signal and its relation to various expressions of performance monitoring. Moreover, it suggests that the error positivity -a signal with thus far unique properties in cognitive neuroscience - can be leveraged to furnish key new insights into the inputs to, adaptation, and consequences of the post-decisional accumulation process., Competing Interests: KD, KR, PM No competing interests declared, (© 2021, Desender et al.)

Human observers can reliably report their confidence in the choices they make. An influential framework conceptualizes decision confidence as the probability of a decision being correct, given the choice made and the evidence on which it was based. This framework accounts for three diagnostic signatures of human confidence reports, including an opposite dependence of confidence on evidence strength for correct and error trials. However, the framework does not account for the temporal evolution of these signatures, because it only describes the transformation of a static representation of evidence into choice and the associated confidence. Here, we combine this framework with another influential framework: dynamic accumulation of evidence over time, and build on the notion that confidence reflects the probability of being correct, given the choice and accumulated evidence up until that point. Critically, we show that such a dynamic model predicts that the diagnostic signatures of confidence depend on time; most critically, it predicts a stronger opposite dependence of confidence on evidence strength and choice correctness as a function of time. We tested, and confirmed, these predictions in human behaviour during random dot motion discrimination, in which confidence judgments were queried at different points in time. We conclude that human confidence reports reflect the dynamics of the probability of being correct given the accumulated evidence and choice., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The ability to predict the timing of forthcoming events, known as temporal expectation, has a strong impact on human information processing. Although there is growing consensus that temporal expectations enhance the speed and accuracy of perceptual decisions, it remains unclear whether they affect the decision process itself, or non-decisional (sensory/motor) processes. Here, healthy human participants ( N = 21; 18 female) used predictive auditory cues to anticipate the timing of low-contrast visual stimuli they were required to detect. Modeling of the behavioral data using a prominent sequential sampling model indicated that temporal expectations speeded up non-decisional processes but had no effect on decision formation. Electrophysiological recordings confirmed and extended this result: temporal expectations hastened the onset of a neural signature of decision formation but had no effect on its build-up rate. Anticipatory α band power was modulated by temporal expectation and co-varied with intrinsic trial-by-trial variability in behavioral and neural signatures of the onset latency of the decision process. These findings highlight how temporal predictions optimize our interaction with unfolding sensory events. SIGNIFICANCE STATEMENT Temporal expectation enhances performance, but the locus of this effect remains debated. Here, we contrasted the two dominant accounts: enhancement through (1) expedited decision onset, or (2) an increase in the quality of sensory evidence. We manipulated expectations about the onset of a dim visual target using a temporal cueing paradigm, and probed the locus of the expectation effect with two complementary approaches: drift diffusion modeling (DDM) of behavior, and estimation of the onset and progression of the decision process from a supramodal accumulation-to-bound signal in simultaneously measured EEG signals. Behavioral modeling and neural data provided strong, converging evidence for an account in which temporal expectations enhance perception by speeding up decision onset, without affecting evidence quality., (Copyright © 2021 the authors.)

When external feedback about decision outcomes is lacking, agents need to adapt their decision policies based on an internal estimate of the correctness of their choices (i.e., decision confidence). We hypothesized that agents use confidence to continuously update the tradeoff between the speed and accuracy of their decisions: When confidence is low in one decision, the agent needs more evidence before committing to a choice in the next decision, leading to slower but more accurate decisions. We tested this hypothesis by fitting a bounded accumulation decision model to behavioral data from three different perceptual choice tasks. Decision bounds indeed depended on the reported confidence on the previous trial, independent of objective accuracy. This increase in decision bound was predicted by a centro-parietal EEG component sensitive to confidence. We conclude that internally computed neural signals of confidence predict the ongoing adjustment of decision policies., Competing Interests: KD, AB, TV No competing interests declared, TD Reviewing editor, eLife, (© 2019, Desender et al.)

Theoretical work predicts that decisions made with low confidence should lead to increased information-seeking. This is an adaptive strategy because it can increase the quality of a decision, and previous behavioral work has shown that decision-makers engage in such confidence-driven information-seeking. The present study aimed to characterize the neural markers that mediate the relationship between confidence and information-seeking. A paradigm was used in which 17 human participants (9 male) made an initial perceptual decision, and then decided whether or not they wanted to sample more evidence before committing to a final decision and confidence judgment. Predecisional and postdecisional event-related potential components were similarly modulated by the level of confidence and by information-seeking choices. Time-resolved multivariate decoding of scalp EEG signals first revealed that both information-seeking choices and decision confidence could be decoded from the time of the initial decision to the time of the subsequent information-seeking choice (within-condition decoding). No above-chance decoding was visible in the preresponse time window. Crucially, a classifier trained to decode high versus low confidence predicted information-seeking choices after the initial perceptual decision (across-condition decoding). This time window corresponds to that of a postdecisional neural marker of confidence. Collectively, our findings demonstrate, for the first time, that neural indices of confidence are functionally involved in information-seeking decisions. SIGNIFICANCE STATEMENT Despite substantial current interest in neural signatures of our sense of confidence, it remains largely unknown how confidence is used to regulate behavior. Here, we devised a task in which human participants could decide whether or not to sample additional decision-relevant information at a small monetary cost. Using neural recordings, we could predict such information-seeking choices based on a neural signature of decision confidence. Our study illuminates a neural link between decision confidence and adaptive behavioral control., (Copyright © 2019 Desender et al.)

There is currently little direct evidence regarding the function of subjective confidence in decision making: The tight correlation between objective accuracy and subjective confidence makes it difficult to distinguish each variable's unique contribution. Here, we created conditions in a perceptual decision task that were matched in accuracy but differed in subjective evaluation of accuracy by orthogonally varying the strength versus variability of evidence. Confidence was reduced with variable (vs. weak) evidence, even across conditions matched for difficulty. Building on this dissociation, we constructed a paradigm in which participants ( N = 20) could choose to seek further information before making their decision. The data provided clear support for the hypothesis that subjective confidence predicts information seeking in decision making: Participants were more likely to sample additional information before giving a response in the condition with low confidence, despite matched accuracy. In a preregistered replication ( N = 50), these findings were replicated with increased task difficulty levels.