Biology subjects

Le Denmat, P.

Publications and source records attributed to Le Denmat, P..

3 recordsLinked to original sources

bioRxiv2023

A low-dimensional approximation of optimal confidence

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 combinations of data is computationally intractable. Here, we describe a novel model of confidence implementing a low-dimensional approximation of this optimal yet intractable solution. Using a low number of free parameters, 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 individuals estimate of the reliability of evidence (captured by parameter ), from confidence biases resulting from general stimulus-independent under- and overconfidence (captured by parameter {beta}). 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 the first tractable and flexible account of the computation of confidence, our model provides concrete tools to construct computationally more plausible models, and offers a clear framework to interpret and further resolve different forms of confidence biases. Significance statementMathematical and computational work has shown that in order to optimize decision making, humans and other adaptive agents must compute confidence in their perception and actions. Currently, it remains unknown how this confidence is computed. We demonstrate how humans can approximate confidence in a tractable manner. Our computational model makes novel predictions about when confidence will be biased (e.g., over- or underconfidence due to selective environmental feedback). We empirically tested these predictions in a novel experimental paradigm, by providing continuous model-based feedback. We observed that different feedback manipulations elicited distinct patterns of confidence judgments, in ways predicted by the model. Overall, we offer a framework to both interpret optimal confidence and resolve confidence biases that characterize several psychiatric disorders.

animal behavior and cognition↗

bioRxiv2023

Modelling Speed-Accuracy Tradeoffs in the Stopping Rule for Confidence Judgments

Making a decision and reporting confidence in the accuracy of that decision are thought to be driven by the same mechanism: the accumulation of evidence. Previous research has shown that choices and reaction times are well accounted for by a computational model assuming noisy accumulation of evidence until crossing a decision boundary (e.g., the drift diffusion model). Decision confidence can be derived from the amount of evidence following post-decision evidence accumulation. Currently, the stopping rule for post-decision evidence accumulation is underspecified. In the current work, we quantitatively and qualitatively compare the ability of four prominent models of confidence couched within evidence accumulation to account for this stopping rule. In two experiments, participants were instructed to make fast or accurate decisions, and to give fast or carefully considered confidence judgments. We then compared the different models in their ability to capture the speed-accuracy effects on confidence. Both qualitatively and quantitatively, the data were best accounted for by our newly proposed Flexible Collapsing Boundaries model, in which post-decision accumulation terminates once it reaches one of two opposing slowly collapsing confidence boundaries. Inspection of the parameters of this model revealed that instructing participants to make fast versus accurate decisions influenced the height of the decision boundaries, while instructing participants to make fast versus careful confidence judgments influenced height of the confidence boundaries. Our data show that the stopping rule for confidence judgments can be well described as an accumulation-to-bound process, and that the height of these confidence boundaries are under strategic control.

neuroscience↗

bioRxiv2022

Manipulating prior beliefs causally induces under- and overconfidence

Making a decision is invariably accompanied by a sense of confidence in that decision. Across subjects and tasks, there is widespread variability in the exact level of confidence, even for tasks that do not differ in objective difficulty. Such expressions of under- and overconfidence are of vital importance, as they relate to fundamental life outcomes. Yet, a computational account specifying the mechanisms underlying under- and overconfidence is currently missing. Here, we propose that prior beliefs in the ability to perform a task, based on prior experience with this or a similar task, explain why confidence can differ dramatically across subjects and tasks, despite similar performance. In two perceptual decision-making experiments, we provide evidence for this hypothesis by showing that manipulating prior beliefs about task performance in a training phase causally influences reported levels of confidence in a test phase, while leaving objective performance in the test phase unaffected. This is true both when prior beliefs are induced via manipulated comparative feedback and via manipulating task difficulty during the training phase. We account for these results within an accumulation-to-bound model by explicitly modeling prior beliefs based on earlier exposure to the task. Decision confidence is then quantified as the probability of being correct conditional on these prior beliefs, leading to under- or overconfidence depending on the task context. Our results provide a fundamental mechanistic insight into the computations underlying under- and overconfidence in perceptual decision-making.

neuroscience↗