Explaining distortions in metacognition with an attractor network model of decision uncertainty

Abstract

Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. Distortions in metacognition are common in mental health disorders, though the neural underpinnings of such dysfunction are unknown. One reason for this is that models of key components of metacognition, such as decision confidence, are generally specified at an algorithmic or process level. While such models can be used to relate brain function to psychopathology, they are difficult to map to a neurobiological mechanism. Here, we develop a biologically-plausible model of decision uncertainty in an attempt to bridge this gap. We first relate the model’s uncertainty in perceptual decisions to standard metrics of metacognition, namely mean confidence level (bias) and the accuracy of metacognitive judgments (sensitivity). We show that dissociable shifts in metacognition are associated with isolated disturbances at higher-order levels of a circuit associated with self-monitoring, akin to neuropsychological findings that highlight the detrimental effect of prefrontal brain lesions on metacognitive performance. Notably, we are able to account for empirical confidence judgements by fitting the parameters of our biophysical model to first-order performance data, specifically choice and response times. Lastly, in a reanalysis of existing data we show that self-reported mental health symptoms relate to disturbances in an uncertainty-monitoring component of the network. By bridging a gap between a biologically-plausible model of confidence formation and observed disturbances of metacognition in mental health disorders we provide a first step towards mapping theoretical constructs of metacognition onto dynamical models of decision uncertainty. In doing so, we provide a computational framework for modelling metacognitive performance in settings where access to explicit confidence reports is not possible.

Value certainty and choice confidence are multidimensional constructs that guide decision-making

“…For decisions with objectively correct responses, the degree to which confidence correlates with accuracy provides measures of metacognitive sensitivity and efficiency (Fleming and Lau, 2014). Metacognitive skills have been linked to individual differences in psychiatric symptomatology, radical political beliefs, intelligence, and academic performance (Atiya et al, 2021;Hoven et al, 2019;Ohtani and Hisasaka, 2018;Rollwage et al, 2018;Rouault et al, 2018). Thus, confidence and metacognition appear to have a substantial impact on a wide range of human thoughts and behaviors.…”

Exogenous and endogenous sources of uncertainty inform global performance monitoring

“…The importance of uncertainty, regardless of its origin, as a contributing factor to local metacognitive judgments has been highlighted before (Atiya et al 2021;Denison et al 2018;Geurts et al 2022;Honig, Ma, and Fougnie 2020;Mole et al 2018;Rahnev 2021). Our results suggest that uncertainty contributes not only to local metacognitive judgments, i.e., judgment of an isolated performance, but also to global performance monitoring, i.e., over a series of performances and events.…”

Adaptive neurons compute confidence in a decision network

“…The underling neural mechanism of uncertainty was investigated using a computational model consisting of one decision module and one uncertainty monitoring module 18 – 20 . These models not only explain the formation of uncertainty but also predict the change-of-mind during the decision making 18 and even after the decision 19 . The key mechanism of the uncertainty model is that the uncertain neuron pool was inhibited by the decision module via a group of inhibitory neurons and received topdown tonic excitation from another cortical area 18 , 19 .…”

“…These models not only explain the formation of uncertainty but also predict the change-of-mind during the decision making 18 and even after the decision 19 . The key mechanism of the uncertainty model is that the uncertain neuron pool was inhibited by the decision module via a group of inhibitory neurons and received topdown tonic excitation from another cortical area 18 , 19 . Although uncertainty was mathematically thought of as the opposite of confidence, the two metrics cannot be considered equivalent and the uncertainty cannot be translated into confidence through a simple action mapping.…”