Confidence and certainty: distinct probabilistic quantities for different goals
By Alexandre Pouget, Jan Drugowitsch, Adam Kepecs. Nature Neuroscience, 2016
468 citations recorded by the source corpus.
When facing uncertainty, adaptive behavioral strategies demand that the brain performs probabilistic computations. In this probabilistic framework, the notion of certainty and confidence would appear to be closely related, so much so that it is tempting to conclude that these two concepts are one and the same. We argue that there are computational reasons to distinguish between these two concepts. Specifically, we propose that confidence should be defined as the probability that a decision or a proposition, overt or covert, is correct given the evidence, a critical quantity in complex sequential decisions. We suggest that the term certainty should be reserved to refer to the encoding of all other probability distributions over sensory and cognitive variables. We also discuss strategies for studying the neural codes for confidence and certainty and argue that clear definitions of neural codes are essential to understanding the relative contributions of various cortical areas to decision making.
Subfields: Artificial Intelligence, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/494ce9e94fe1f93713376a37c90cbb1fe78d8941
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Citations in the corpus
- Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation (2017, background): “As we will outline in further detail below, decision confidence can be defined as a subjective probability of a decision being correct (Aitchison et al., 2015; Pouget et al., 2016), and is one of many forms of uncertainty that the brain may encode (Bach & Dolan, 2012; Meyniel, Schlunegger, & Dehaene”
- Post-decisional accounts of biases in confidence (2016, background): “This is because, from a normative perspective, confidence should reflect the probability of being correct [20].”