The Misestimation of Uncertainty in Affective Disorders

Pulcu, Erdem; Browning, Michael

doi:10.1016/j.tics.2019.07.007

Cited by 124 publications

(188 citation statements)

References 40 publications

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“…Expanding on those findings, here we evaluated whether temporary anxious states in healthy individuals influence reward learning in a volatile environment through changes in informational and environmental uncertainty. Evidence for a link between anxiety and inaccurate estimation of uncertainty would lend support to recent theoretical accounts suggesting that difficulties learning from incomplete information and misestimations of uncertainty are crucial to understanding affective disorders (Pulcu and Browning, 2019).…”

Section: Introductionsupporting

confidence: 53%

“…Our approach is not the first in proposing a role of uncertainty estimates in cognitive biases in anxiety. A recent account of affective disorders suggested that difficulties with uncertainty estimation underlie some of the psychiatric symptoms in these populations (Pulcu and Browning, 2019). This work distinguished between different types of uncertainty, corresponding to irreducible, informational, and environmental uncertainty as described here, and assigned a particular relevance of environmental ("unexpected") uncertainty in explaining anxiety.…”

Section: States Of Anxiety Bias Computations Of Different Types Of Unmentioning

confidence: 78%

“…This model allowed us to assess the effect of state anxiety on an array of relevant computational quantities during task performance. Above and beyond revealing a misestimation of volatility (environmental uncertainty) -as proposed by Pulcu and Browning (2019) -our approach identified biases in uncertainty on various levels that drive suboptimal learning in state anxiety.…”

Section: States Of Anxiety Bias Computations Of Different Types Of Unmentioning

confidence: 96%

“…Entertaining overly precise beliefs may represent a strategy to regain a sense of control because uncertainty is experienced as aversive, such as observed in obsessive compulsive disorder (Carleton, 2016) and ritualistic behaviour (Lang et al, 2015). In turn, this emergence of biased estimates could increase the symptoms of anxiety over time through recursive inaccurate assessments of threat from uncertainty, thereby fitting a profile of anxious responses similar to those of anxiety-related disorders (Grupe and Nitschke, 2013;Pulcu and Browning, 2019).…”

Section: States Of Anxiety Bias Computations Of Different Types Of Unmentioning

confidence: 99%

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State anxiety biases estimates of uncertainty during reward learning in volatile environments

Hein

Weber

Fockert

et al. 2019

Preprint

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Previous research established that clinical anxiety impairs decision making and that high trait anxiety interferes with learning rates. Less understood are the effects of temporary anxious states on learning and decision making in healthy populations. Here we follow proposals that anxious states in healthy individuals elicit a pattern of aberrant behavioural, neural, and physiological responses comparable with those found in anxiety disorders, particularly when processing uncertainty in unstable environments. In our study, both a state anxious and a control group learned probabilistic stimulus-outcome mappings in a volatile task environment while we recorded their electrophysiological (EEG) signals. By using a hierarchical Bayesian model, we assessed the effect of state anxiety on Bayesian belief updating with a focus on uncertainty estimates. State anxiety was associated with an underestimation of environmental and informational uncertainty, and an increase in uncertainty about volatility estimates. Anxious individuals deemed their beliefs about reward contingencies to be more precise and to require less updating, ultimately leading to impaired reward-based learning. We interpret this pattern as evidence that state anxious individuals are less tolerant to informational uncertainty about the contingencies governing their environment and more uncertain about the level of stability of the world itself. Further, we tracked the neural representation of belief update signals in the trial-bytrial EEG amplitudes. In control participants, both lower-level precision-weighted prediction errors (pwPEs) about the reward outcomes and higher-level volatility-pwPEs were represented in the ERP signals with an anterior distribution. A different pattern emerged under state anxiety, where a neural representation of pwPEs was only found for updates about volatility. Expanding previous computational work on trait anxiety, our findings establish that temporary anxious states in healthy individuals impair reward-based learning in volatile environments, primarily through changes in uncertainty estimates and potentially a degradation of the neuronal representation of hierarchically-related pwPEs, considered to play a central role in current Bayesian accounts of perceptual inference and learning.

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Section: Introductionsupporting

confidence: 53%

Section: States Of Anxiety Bias Computations Of Different Types Of Unmentioning

confidence: 78%

Section: States Of Anxiety Bias Computations Of Different Types Of Unmentioning

confidence: 96%

Section: States Of Anxiety Bias Computations Of Different Types Of Unmentioning

confidence: 99%

See 2 more Smart Citations

State anxiety biases estimates of uncertainty during reward learning in volatile environments

Hein

Weber

Fockert

et al. 2019

Preprint

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show abstract

“…The other regressors that we were interested in while constructing the pupil model were those quantitatively defining how the social interactive decision-making environment changed (i.e. environmental volatility and environmental noise 29 ), as well as participants’ response to those changes (i.e. surprise).…”

Section: Resultsmentioning

confidence: 99%

Affective biases encoded by the central arousal systems dynamically modulate inequality aversion in human interpersonal negotiations

Murphy

Harmer

Browning

et al. 2019

Preprint

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AbstractNegotiating with others about how finite resources should be distributed is an important aspect of human social life. However, little is known about mechanisms underlying human social-interactive decision-making. Here, we report results from a novel iterative Ultimatum Game (UG) task, in which the proposer’s facial emotions and offer amounts were sampled probabilistically based on the participant’s decisions, creating a gradually evolving social-interactive decision-making environment. Our model-free results confirm the prediction that both the proposer’s facial emotions and the offer amount influence human choice behaviour. These main effects demonstrate that biases in facial emotion recognition also contribute to violations of the Rational Actor model (i.e. all offers should be accepted). Model-based analyses extend these findings, indicating that participants’ decisions are guided by an aversion to inequality in the UG. We highlight that the proposer’s facial responses to participant decisions dynamically modulate how human decision-makers perceive self–other inequality, relaxing its otherwise negative influence on decision values. In iterative games, this cognitive model underlies how offers initially rejected can gradually become more acceptable under increasing affective load, and accurately predicts 86% of participant decisions. Activity of the central arousal systems, assessed by measuring pupil size, encode a key element of this model: proposer’s affective reactions in response to participant decisions. Taken together, our results demonstrate that, under affective load, participants’ aversion to inequality is a malleable cognitive process which is modulated by the activity of the pupil-linked central arousal systems.

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Two‐stage reinforcement learning task predicts psychological traits

et al. 2023

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External sources of information influence human actions. However, psychological traits (PTs), considered internal variables, also play a crucial role in decision making. PTs are stable across time and contexts and define the set of behavioral repertoires that individuals express. Here, we explored how multiple metrics of adaptive behavior under uncertainty related to several PTs. Participants solved a reversal‐learning task with volatile contingencies, from which we characterized a detailed behavioral profile based on their response sequences. We then tested the relationship between this multimetric behavioral profile and scores obtained from self‐report psychological questionnaires. The PT measurements were based on the Hierarchical Taxonomy Of Psychopathology (HiTOP) model. By using multiple linear regression models (MLRMs), we found that the learning curves predicted important differences in the PTs and task response times. We confirmed the significance of these relationships by using random permutations of the predictors of the MLRM. Therefore, the behavioral profile configurations predicted the PTs and served as a “fingerprint” to identify participants with a high certainty level. We discuss briefly how this characterization and approach could contribute to better nosological classifications.

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The Misestimation of Uncertainty in Affective Disorders

Cited by 124 publications

References 40 publications

State anxiety biases estimates of uncertainty during reward learning in volatile environments

State anxiety biases estimates of uncertainty during reward learning in volatile environments

Affective biases encoded by the central arousal systems dynamically modulate inequality aversion in human interpersonal negotiations

Two‐stage reinforcement learning task predicts psychological traits

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