Neural correlates, computation and behavioural impact of decision confidence

Abstract

Humans and other animals must often make decisions on the basis of imperfect evidence. Statisticians use measures such as P values to assign degrees of confidence to propositions, but little is known about how the brain computes confidence estimates about decisions. We explored this issue using behavioural analysis and neural recordings in rats in combination with computational modelling. Subjects were trained to perform an odour categorization task that allowed decision confidence to be manipulated by varying the distance of the test stimulus to the category boundary. To understand how confidence could be computed along with the choice itself, using standard models of decision-making, we defined a simple measure that quantified the quality of the evidence contributing to a particular decision. Here we show that the firing rates of many single neurons in the orbitofrontal cortex match closely to the predictions of confidence models and cannot be readily explained by alternative mechanisms, such as learning stimulus-outcome associations. Moreover, when tested using a delayed reward version of the task, we found that rats' willingness to wait for rewards increased with confidence, as predicted by the theoretical model. These results indicate that confidence estimates, previously suggested to require 'metacognition' and conscious awareness are available even in the rodent brain, can be computed with relatively simple operations, and can drive adaptive behaviour. We suggest that confidence estimation may be a fundamental and ubiquitous component of decision-making.

Rats track odour trails accurately using a multi-layered strategy with near-optimal sampling

“…The hippocampus has been shown to have olfactory 33,34 as well as spatial representations 35 . The orbitofrontal cortex has been shown to encode uncertainty 36 . These parameters may be revealed by further studies involving single-unit recordings from these brain regions.…”

High-order feature-based mixture models of classification learning predict individual learning curves and enable personalized teaching

“…5 and 22)-our model included in the mixture a set of features that allowed describing any rule, i.e., a spanning set of features. This features set could correspond to neural correlates of decision-making (23,24), probabilistic inference (13,25), and learning and strategy shifts (26,27) that were observed in single-unit recordings in primate and mammalian cortex. Seeking neural correlates of the model presented here would be of particular interest in light of the characterization of the role of memory systems involved in WP (28,29) and other learning and decision-making tasks (30,31), and theoretical models of incremental learning through spike timing-dependent plasticity (32,33).…”

Consequences of Adolescent Ethanol Consumption on Risk Preference and Orbitofrontal Cortex Encoding of Reward

“…The orbitofrontal portion of the prefrontal cortex (OFC) in particular has been found to have a role in decisions based on outcome value and reward expectations (Kepecs et al, 2008;McDannald et al, 2014;Padoa-Schioppa and Cai, 2011;Stalnaker et al, 2014). When rewards are uncertain, the magnitude of response in OFC has been shown to modulate proportionally with risk (Tobler et al, 2009), and lesions of OFC disrupt decisions that involve risk (Hsu et al, 2005).…”