Confidence and certainty: distinct probabilistic quantities for different goals

Pouget, Alexandre; Drugowitsch, Jan; Kepecs, Ádám

doi:10.1038/nn.4240

Cited by 486 publications

(629 citation statements)

References 67 publications

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“…Much confusion still surrounds the formalization of subjective confidence (12,30,31). In studies that investigate decision confidence (32-35), confidence formally corresponds to the probability of the decision being correct.…”

Section: Discussionmentioning

confidence: 99%

Brain networks for confidence weighting and hierarchical inference during probabilistic learning

Meyniel

Dehaene

2017

Proc. Natl. Acad. Sci. U.S.A.

111

146

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Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This "confidence weighting" implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an idealobserver analysis, we demonstrate that the brain's learning algorithm relies on confidence weighting. While in the fMRI scanner, human adults attempted to learn the transition probabilities underlying an auditory or visual sequence, and reported their confidence in those estimates. They knew that these transition probabilities could change simultaneously at unpredicted moments, and therefore that the learning problem was inherently hierarchical. Subjective confidence reports tightly followed the predictions derived from the ideal observer. In particular, subjects managed to attach distinct levels of confidence to each learned transition probability, as required by Bayes-optimal inference. Distinct brain areas tracked the likelihood of new observations given current predictions, and the confidence in those predictions. Both signals were combined in the right inferior frontal gyrus, where they operated in agreement with the confidenceweighting model. This brain region also presented signatures of a hierarchical process that disentangles distinct sources of uncertainty. Together, our results provide evidence that the sense of confidence is an essential ingredient of probabilistic learning in the human brain, and that the right inferior frontal gyrus hosts a confidence-based statistical learning algorithm for auditory and visual sequences.T he sensory data that we receive from our environment are often captured by temporal regularities-for instance the colors of traffic lights change according to a predictable greenyellow-red pattern; thunder is often followed by rain, etc. Knowledge of those hidden regularities is often acquired through learning, by aggregating successive observations into summary estimates (e.g., the probability of the light turning red when it is currently yellow). When sensory data are received sequentially, learning can be described as an iterative process that updates the internal estimates each time a new observation is received. Learners must therefore constantly balance two sources of information: their current estimates and the new incoming observations. Any learning algorithm must find a solution to this balancing act. Finding the correct balance is especially critical in a world that is both stochastic and changing (1), i.e., where observations are governed by probabilities that can change over time (a situation called volatility). An excessive reliance on incoming observations will make the learned estimates dominated by fluctuations instead of converging to the true underlying probabilities. Conversely, an excessive reliance o...

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Section: Discussionmentioning

confidence: 99%

Brain networks for confidence weighting and hierarchical inference during probabilistic learning

Meyniel

Dehaene

2017

Proc. Natl. Acad. Sci. U.S.A.

111

146

View full text Add to dashboard Cite

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“…This is because, from a normative perspective, confidence should reflect the probability of being correct [20]. In this context, processing information with bias implies under or overweighting evidence for the chosen option (regardless of its validity) leading to suboptimal estimates of confidence (Box 1).…”

Section: Origins and Function Of Post-decisional Biasesmentioning

confidence: 99%

“…Here, we review state-of-the-art models of confidence and explore possible answers to these questions. In particular, we focus on how post-decisional processes affect our "metacognitive accuracy", namely, the extent to which our confidence is consistent with our probability of being correct [20]. Far from being idle curiosity, knowing when we are better judges of ourselves could benefit us in several ways: it could help us cooperate effectively [21,22] and reduce aversive counterfactual thinking [23] that otherwise leads to negative emotions such as regret [24].…”

Section: Introductionmentioning

confidence: 99%

Post-decisional accounts of biases in confidence

Navajas

Bahrami

Latham

2016

Current Opinion in Behavioral Sciences

103

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Most models of decision-making suggest that confidence, the "feeling of knowing" that accompanies our choices, is constructed as the decision unfolds. However, more recent studies have noted that processes occurring after we commit to a particular choice also affect this subjective belief. This leads to following question: when are we better judges of ourselves?If, after a decision, evidence continues to accumulate in an unbiased manner, then our confidence judgements should improve. Conversely, if post-decisional information processing is biased, our sense of confidence could be distorted, and so our confidence judgements should degrade with time. We briefly discuss recently proposed models of post-decisional evidence accumulation, and explore whether, and how, biases in confidence could arise. Highlights: Confidence in decision-making is often regarded as a process that is constructed during choice. Recent experimental findings suggest, however, that post-decisional processes modulate our sense of confidence. These processes can improve our confidence judgements, but also distort them if information processing is biased. We discuss models of decision-making that contemplate different biases in postdecisional processing. We explore possible explanations for the origin and function of these biases Acknowledgements: JN and BB

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“…Accompanying such decisions is a sense of confidence in having made the right choice which can be used to guide behaviour (1,2). For example, after having made a difficult choice, an animal might correctly estimate that its decision is unlikely to be correct and thus avoid wasting time waiting for a reward that may not arrive (3).…”

Section: Introductionmentioning

confidence: 99%

“…Several computational models propose that decision confidence reflects an estimate of the probability that a choice is correct (1,2). A ubiquitous paradigm for studying the neural basis of decision confidence is sensory psychophysics (6).…”

Section: Introductionmentioning

confidence: 99%

Distinct encoding of decision confidence in human medial prefrontal cortex

Bang

Fleming

2018

Preprint

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Our confidence in a choice and the reliability of evidence pertaining to that choice appear to be inseparable quantities. However, recent computational frameworks suggest that the brain should encode distinct estimates of these quantities for adaptive behavioural control. Consider a line judge in a tennis match deciding whether a ball is out. When the ball is moving quickly, the sensory signal is unreliable. Nevertheless, if the ball lands far from the line, a decision may be easy and confidence high. Decision confidence should reflect the probability that a choice is correct and must therefore, unlike other estimates of certainty such as evidence reliability, be flexibly conditioned on a choice. Here we devised a psychophysical approach to decouple confidence in a sensory choice from the reliability of the sensory evidence upon which a choice is based. Using human fMRI, we observed that an area in medial prefrontal cortex (perigenual cingulate cortex, pgACC) tracked probability correct, the normative definition of decision confidence, whereas neural areas previously thought to track decision confidence, such as ventral striatum and posterior parietal cortex, instead tracked sensory reliability. We show that pgACC activity does not simply co-vary with objective performance but is also linked to withinsubject and between-subject variation in a subjective sense of decision confidence. Our study is consistent with the recent proposal that the brain separately maintains choice-dependent and choice-independent estimates of certainty, and sheds light on why dysfunctional confidence estimates often emerge following prefrontal lesions and/or degeneration.

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Confidence and certainty: distinct probabilistic quantities for different goals

Cited by 486 publications

References 67 publications

Brain networks for confidence weighting and hierarchical inference during probabilistic learning

Brain networks for confidence weighting and hierarchical inference during probabilistic learning

Post-decisional accounts of biases in confidence

Distinct encoding of decision confidence in human medial prefrontal cortex

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