Trait somatic anxiety is associated with reduced directed exploration and underestimation of uncertainty

Fan, Haoxue; Gershman, Samuel J.; Phelps, Elizabeth A.

doi:10.1038/s41562-022-01455-y

Cited by 28 publications

(42 citation statements)

References 75 publications

(76 reference statements)

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“…Empirical studies in AI have only very recently begun to examine differences in preference precision (and hence informationseeking; see (Smith, Schwartenbeck, et al, 2020;Smith, Taylor, Stewart, et al, 2021)). However, work on exploratory drives within expanded RL models has suggested altered patterns of information-seeking in depression, anxiety, and other psychiatric disorders (Aberg et al, 2022;Fan et al, 2021;Smith, Taylor, Wilson, et al, 2021;Waltz et al, 2020). Whether and how information-seeking drives relate to SWB has not been empirically assessed (for some interesting recent theoretical discussion, see (Miller et al, 2022)).…”

Section: Information-seeking and Sensitivity To Uncertaintymentioning

confidence: 99%

“…By extension, these model elements also offer possible explanations for cultural differences, which can be viewed as computational phenotypes that are similar in individuals within a culture but differ from individuals in other cultures. For example, perhaps some cultures place more value than others on reflective practices (e.g., heightened attention to one's own uncertainty, more frequent engagement in prospection/retrospection) that would be expected to increase information-seeking and planning horizon (e.g., see (Fan et al, 2021;Gershman, 2018;Jackson et al, 2020;Kaplan & Friston, 2018;Smith, Taylor, Wilson, et al, 2022)). Or perhaps some cultures tend to share implicit expectations that the social environment is more volatile than others.…”

Section: Potential Empirical Applicationsmentioning

confidence: 99%

“…Reward-seeking vs. information-seeking Explore-exploit and multi-arm bandit tasks (Fan et al, 2021;Smith, Schwartenbeck, et al, 2020;Smith, Taylor, Stewart, et al, 2021;Wilson et al, 2014) Increasing awareness of uncertainty (cognitive and behavioral therapeutic interventions; (Barlow et al, 2016;Segal et al, 2004)), altering incentive structure (Ederer & Manso, 2013) Reduced avoidance behavior, impulsivity, and/or uncertainty due to optimizing the balance between reward-seeking and information-seeking…”

Section: Preference Precision (C)mentioning

confidence: 99%

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A computational neuroscience perspective on subjective wellbeing within the active inference framework

Smith¹,

Varshney²,

Nagayama³

et al. 2022

Intnl. J. Wellbeing

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Understanding and promoting subjective wellbeing (SWB) has been the topic of increasing research, due in part to its potential contributions to health and productivity. To date, the conceptualization of SWB has been grounded within social psychology and largely focused on self-report measures. In this paper, we explore the potentially complementary tools and theoretical perspectives offered by computational neuroscience, with a focus on the active inference (AI) framework. This framework is motivated by the fact that the brain does not have direct access to the world; to select actions, it must instead infer the most likely external causes of the sensory input it receives from both the body and the external world. Because sensory input is always consistent with multiple interpretations, the brain’s internal model must use background knowledge, in the form of prior expectations, to make a “best guess” about the situation it is in and how it will change by taking one action or another. This best guess arises by minimizing an error signal representing the deviation between predicted and observed sensations given a chosen action—quantified mathematically by a variable called free energy (FE). Crucially, recent proposals have illustrated how emotional experience may emerge within AI as a natural consequence of the brain keeping track of the success of its model in selecting actions to minimize FE. In this paper, we draw on the concepts and mathematics in AI to highlight how different computational strategies can be used to minimize FE—some more successfully than others. This affords a characterization of how diverse individuals may adopt unique strategies for achieving high SWB. It also highlights novel ways in which SWB could be effectively improved. These considerations lead us to propose a novel computational framework for understanding SWB. We highlight several parameters in these models that could explain individual and cultural differences in SWB, and how they might inspire novel interventions. We conclude by proposing a line of future empirical research based on computational modelling that could complement current approaches to the study of wellbeing and its improvement.

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Section: Information-seeking and Sensitivity To Uncertaintymentioning

confidence: 99%

Section: Potential Empirical Applicationsmentioning

confidence: 99%

Section: Preference Precision (C)mentioning

confidence: 99%

See 1 more Smart Citation

A computational neuroscience perspective on subjective wellbeing within the active inference framework

Smith¹,

Varshney²,

Nagayama³

et al. 2022

Intnl. J. Wellbeing

View full text Add to dashboard Cite

show abstract

“…Remark that the MOS model is not aimed at producing the cognitive process in the brain with high fidelity but a descriptive approach whose fitted weight parameters can be used to infer the subjects' decision propensity that underlies their behavioral data (Daw et al, 2011;Fan et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Individuals with anxiety and depression use atypical decision strategies in an uncertain world

Fang¹,

Zhao²,

Xu³

et al. 2023

Preprint

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Previous studies have identified that enhanced learning rate is a key mechanism for adapting to fast-changing reward environments with high volatility, and that impairments in flexible learning rate may be associated with several psychiatric conditions. However, these studies have mostly assumed a single strategy manifested in the human probabilistic learning process. In contrast, we propose an alternative perspective and develop a hybrid mixture-of-strategy (MOS) model that incorporates three strategies: the expected utility strategy, which aims to maximize rewards; the magnitude-oriented strategy, which only considers reward magnitude; and the habitual strategy, which tends to repeat previous decisions. While the expected utility strategy is statistically optimal, the magnitude-oriented and habitual strategies are suboptimal but computationally simpler. In an open dataset where healthy controls and patients with anxiety and depression performed a probabilistic reversal learning task with distinct volatility conditions, our MOS model outperforms previous best-fitting models. Parameter analyses suggest that individuals with anxiety and depression exhibit weaker preferences for the optimal expected utility strategy but stronger preferences for the magnitude-oriented heuristic. The relative strength of these two strategies also predicts individual variation in symptom severity. Importantly, our MOS model explains the slower learning in patients by the different weighting of the different strategies rather than learning rate per se. These findings underscore the complexity of human learning and decision-making and suggest the possibility of mixed strategies in learning and decision-making. Further research is necessary to distinguish the flexible-learning-rate account from the mixture-of-strategy account.

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“…Past work has identified the behavioral signatures of these two kinds of uncertaintydriven exploration by experimentally separating directed and random exploration by manipulating relative and total uncertainty respectively (16,19,23). Beyond the behavioral level, there is also evidence that these two uncertainty-driven exploration strategies are neurally dissociable (26)(27)(28), have distinct development trajectories (29,30), and are associated with trait anxiety in different ways (31,32). Given NE's role in signaling uncertainty and guiding behavior under uncertainty (3,(33)(34)(35), it is likely that the LC-NE system is involved in guiding uncertainty-driven exploration.…”

mentioning

confidence: 99%

Pupil size encodes uncertainty during exploration

Fan¹,

Burke

Sambrano³

et al. 2023

Preprint

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Exploration is an important part of decision making and is crucial to maximizing long-term reward. Past work has shown that people use different forms of uncertainty to guide exploration. In this study, we investigate the role of the pupil-linked arousal system in uncertainty-guided exploration. We measured participants’ pupil dilation (N = 48) while they performed a two- armed bandit task. Consistent with previous work, we found that people adopted a hybrid of directed, random and undirected exploration, which are sensitive to relative uncertainty, total uncertainty and value difference between options, respectively. We also found a positive correlation between pupil size and total uncertainty. Furthermore, augmenting the choice model with subject-specific total uncertainty estimates decoded from the pupil size improved predictions of held-out choices, suggesting that people used the uncertainty estimate encoded in pupil size to decide which option to explore Together, the data shed light on the computations underlying uncertainty-driven exploration. Under the assumption that pupil size reflects Locus Coeruleus-Norepinephrine (LC-NE) neuromodulatory activity, these results also extend the theory of LC-NE function in exploration, highlighting its selective role in driving uncertainty- guided random exploration.

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Trait somatic anxiety is associated with reduced directed exploration and underestimation of uncertainty

Cited by 28 publications

References 75 publications

A computational neuroscience perspective on subjective wellbeing within the active inference framework

A computational neuroscience perspective on subjective wellbeing within the active inference framework

Individuals with anxiety and depression use atypical decision strategies in an uncertain world

Pupil size encodes uncertainty during exploration

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