Confidence in value-based choice

By B. De Martino, Stephen M. Fleming, Neil Garrett, R. J. Dolan. 2012

495 citations recorded by the source corpus.

Decisions are never perfect, with confidence in one's choices fluctuating over time. How subjective confidence and valuation of choice options interact at the level of brain and behavior is unknown. Using a dynamic model of the decision process, we show that confidence reflects the evolution of a decision variable over time, explaining the observed relation between confidence, value, accuracy and reaction time. As predicted by our dynamic model, we show that a functional magnetic resonance imaging signal in human ventromedial prefrontal cortex (vmPFC) reflects both value comparison and confidence in the value comparison process. Crucially, individuals varied in how they related confidence to accuracy, allowing us to show that this introspective ability is predicted by a measure of functional connectivity between vmPFC and rostrolateral prefrontal cortex. Our findings provide a mechanistic link between noise in value comparison and metacognitive awareness of choice, enabling us both to want and to express knowledge of what we want.

Subfields: Cognitive Neuroscience, General Decision Sciences

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Citations in the corpus

Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation (2017, background): “…built upon evidence accumulation models of decision-making, accounting for key interrelationships between choice, confidence and response time (De Martino et al., 2013; Kiani & Shadlen, 2009; Kiani, Corthell, & Shadlen, 2014; Merkle & Van Zandt, 2006; Pleskac & Busemeyer, 2010; Ratcliff &…”
The Cognitive Neuroscience of Metacognition (2014, methodology): “For instance, Markowitz and Swets [14] found that estimates of s in auditory detection tasks depend on signal strength for pseudo, but not true, type 1 ROC curves; Van Zandt [23] found that estimates of s based on pseudo type 1 ROC curves varied depending on the degree of bias in the”
How to measure metacognition (2014, methodology): “An alternative approach is to compute an aggregate measure of metacognitive sensitivity as the difference in slope between psychometric functions constructed from high and low confidence trials (e.g., De Martino et al., 2013; de Gardelle and Mamassian, 2014).”
Neural computations underlying arbitration between model-based and model-free learning (2013, background): “…in which we found reliability signals are also close to the region of right lateral prefrontal cortex found to process subjective confidence (De Martino et al., 2013) and the region of lateral anterior prefrontal cortex previously implicated in metacognitive processes (Baird et al., 2013),…”