Changes-of-mind in the absence of new post-decision evidence

Atiya, Nadim A. A.; Zgonnikov, Arkady; O’Hora, Denis; Schoemann, Martin; Scherbaum, Stefan; Wong‐Lin, KongFatt

doi:10.1371/journal.pcbi.1007149

Cited by 24 publications

(40 citation statements)

References 49 publications

(145 reference statements)

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“…Typical winner-take-all behaviour is seen in the sensorimotor module. Activity of the uncertainty-monitoring population follows a phasic profile (see Atiya et al (2019Atiya et al ( , 2020 and Methods). Trial simulated with dot difference between the two boxes set at 20 (see Methods).…”

Section: Neural Circuit Modelmentioning

confidence: 99%

“…To provide insight into the interaction between decision formation and metacognitive processes in this task, we simulated and fitted our neural circuit model of decision uncertainty to subjects' choices and response times (Atiya et al 2019(Atiya et al , 2020. This allowed us to use subjects' explicit confidence reports as an out-of-sample test of the model's ability to account for individual differences in metacognition.…”

Section: Model Fits To Subject Datamentioning

confidence: 99%

“…The subplex optimisation method is an evolution of the simplex method (Nelder and Mead 1965) -one that is better suited for optimising noisy objective functions. Importantly, when parameterising our model, we initially set the values of all parameters to those found in our previous work (Atiya et al 2020), allowing only two parameters to vary in the fitting procedure. The first parameter is a 'gain' parameter, which maps the dot difference to input current flowing into the sensorimotor populations (see Methods).…”

Section: Model Fits To Subject Datamentioning

confidence: 99%

“…For instance, Wong and Wang (2006) have accounted for most of the behavioural results addressed by Wang (2002)'s model using the two slowest N-Methyl-D-aspartic acid (NMDA) dynamical variables. More recently, Atiya et al (2019) extended Wong and Wang (2006)'s model to account for decision confidence reports and other metacognitive behaviours, such as an ability to flexibly change one's mind and correct errors (Atiya et al 2020). More specifically, guided by neurophysiological evidence that supports an encoding of confidence within higher-order prefrontal brain regions (Kepecs et al 2008;Fleming and Dolan 2012), the authors introduced the idea of a third 'uncertaintymonitoring' neuronal population (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…To gain insight into potential mechanisms underlying shifts in metacognition, we first demonstrate that our biologically-motivated model (Atiya et al 2019(Atiya et al , 2020 can account for human confidence reports. Crucially, we show that the intrinsic dynamics of the model, constrained by first-order performance, are sufficient to account for subjects' confidence reports, going beyond existing methods of fitting models to empirical confidence data (Kepecs et al 2008;Kiani and Shadlen 2009;Pleskac and Busemeyer 2010;Sanders, Hangya, and Kepecs 2016).…”

Section: Introductionmentioning

confidence: 99%

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

Atiya

Huys

Dolan

et al. 2020

Preprint

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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. In contrast to existing theoretical work, we account for empirical confidence judgements by fitting our biophysical model solely 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.

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Section: Neural Circuit Modelmentioning

confidence: 99%

Section: Model Fits To Subject Datamentioning

confidence: 99%

Section: Model Fits To Subject Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Atiya

Huys

Dolan

et al. 2020

Preprint

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Motor outcomes congruent with intentions may sharpen metacognitive representations

et al. 2023

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Similar network compositions, but distinct neural dynamics underlying belief updating in environments with and without explicit outcomes

Fiore

2022

NeuroImage

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Classic decision theories typically assume the presence of explicit value-based outcomes after action selections to update beliefs about action-outcome contingencies. However, ecological environments are often opaque, and it remains unclear whether the neural dynamics underlying belief updating vary under conditions characterized by the presence or absence of such explicit value-based information, after each choice selection. We investigated this question in healthy humans ( n = 28) using Bayesian inference and two multi-option fMRI tasks: a multi-armed bandit task, and a probabilistic perceptual task, respectively with and without explicit value-based feedback after choice selections. Model-based fMRI analysis revealed a network encoding belief updating which did not change depending on the task. More precisely, we found a confidence-building network that included anterior hippocampus, amygdala, and medial prefrontal cortex (mPFC), which became more active as beliefs about action-outcome probabilities were confirmed by newly acquired information. Despite these consistent responses across tasks, dynamic causal modeling estimated that the network dynamics changed depending on the presence or absence of trial-by-trial value-based outcomes. In the task deprived of immediate feedback, the hippocampus increased its influence towards both amygdala and mPFC, in association with increased strength in the confidence signal. However, the opposite causal relations were found (i.e., from both mPFC and amygdala towards the hippocampus), in presence of immediate outcomes. This finding revealed an asymmetric relationship between decision confidence computations, which were based on similar computational models across tasks, and neural implementation, which varied depending on the availability of outcomes after choice selections.

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Changes-of-mind in the absence of new post-decision evidence

Cited by 24 publications

References 49 publications

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

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

Motor outcomes congruent with intentions may sharpen metacognitive representations

Similar network compositions, but distinct neural dynamics underlying belief updating in environments with and without explicit outcomes

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