Using decision models to decompose anxiety-related bias in threat classification.

White, Corey N.; Skokin, Kimberly; Carlos, Brandon J; Weaver, Alexandria

doi:10.1037/emo0000109

Cited by 36 publications

(40 citation statements)

References 23 publications

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“…Indeed, these models have already been used to examine cognitive biases relating to threat processing and classification among subjects with high levels of self-reported trait anxiety [see Existing Computational Studies of Trait Anxiety; also Ref. (61–64)].…”

Section: Computational Backgroundmentioning

confidence: 99%

Modeling Trait Anxiety: From Computational Processes to Personality

2017

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Computational methods are increasingly being applied to the study of psychiatric disorders. Often, this involves fitting models to the behavior of individuals with subclinical character traits that are known vulnerability factors for the development of psychiatric conditions. Anxiety disorders can be examined with reference to the behavior of individuals high in “trait” anxiety, which is a known vulnerability factor for the development of anxiety and mood disorders. However, it is not clear how this self-report measure relates to neural and behavioral processes captured by computational models. This paper reviews emerging computational approaches to the study of trait anxiety, specifying how interacting processes susceptible to analysis using computational models could drive a tendency to experience frequent anxious states and promote vulnerability to the development of clinical disorders. Existing computational studies are described in the light of this perspective and appropriate targets for future studies are discussed.

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Section: Computational Backgroundmentioning

confidence: 99%

Modeling Trait Anxiety: From Computational Processes to Personality

2017

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“…Behavioural models of perceptual decision making, like drift-diffusion modelling (DDM; Ratcliff and McKoon, 54 2008), have shown that prior expectations may bias the starting point of evidence accumulation such that we are 55 predisposed towards one conclusion over another before the decision process has even begun (Barbosa et al, 2017, 56 Mulder et al, 2012, Wiech et al, 2014, Dunovan et al, 2014, White et al, 2018, White et al, 2016. Prior 57 expectations have also been shown to increase the drift rate of evidence accumulation (Dunovan et al, 2014, White 58 et al, 2016 and may lower the threshold for awareness (De Loof et al, 2016).…”

Section: Introduction 28mentioning

confidence: 99%

Surprising Threats Accelerate Evidence Accumulation for Conscious Perception

McFadyen

Smout

Tsuchiya

et al. 2019

Preprint

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15Our survival depends on how well we can rapidly detect threats in our environment. To facilitate this, the brain is 16 faster to bring threatening or rewarding visual stimuli into conscious awareness than neutral stimuli. Unexpected 17 events may indicate a potential threat, and yet we tend to respond slower to unexpected than expected stimuli. It 18 is unclear if or how these effects of emotion and expectation interact with one's conscious experience. To 19 investigate this, we presented neutral and fearful faces with different probabilities of occurance in a breaking 20 continuous flash suppression (bCFS) paradigm. Across two experiments, we discovered that fulfilled prior 21 expectations hastened responses to neutral faces but had either no significant effect (Experiment 1) or the opposite 22 effect (Experiment 2) on fearful faces. Drift diffusion modelling revealed that, while prior expectations accelerated 23 stimulus encoding time (associated with the visual cortex), evidence was accumulated at an especially rapid rate 24 for unexpected fearful faces (associated with activity in the right inferior frontal gyrus). Hence, these findings 25 demonstrate a novel interaction between emotion and expectation during bCFS, driven by a unique influence of 26 surprising fearful stimuli that expedites evidence accumulation in a fronto-occipital network. 27 Like predictable events, threatening stimuli are also prioritised for conscious access (Otten et al., 2017). For 63 example, fearful faces , snakes and spiders (Gomes et al., 2017), and fear-conditioned stimuli (Gayet et al., 2016) 64 are consciously perceived earlier than neutral stimuli during breaking continuous flash suppression (bCFS; 65 Tsuchiya and Koch, 2005, Jiang et al., 2007). Fearful stimuli have been shown to increase the rate of evidence 66To test the three hypotheses above, we conducted two bCFS experiments and one control experiment (see 101 Supplementary Materials).In Experiment 1, we established how expectation interacts with threat in bringing 102 stimuli into conscious perception. In Experiment 2, we adapted the design of Experiment 1 to incorporate EEG so 103 that we could observe the spatio-temporal maps of neural activity underlying the effects of emotion and expectation 104 during bCFS. We also conducted drift diffusion modelling (DDM) to examine which parameters of the decision-105 making process explained the differences in response time between conditions and how this was reflected in neural 106 activity. DDM has been used in previous studies investigating consciousness that equate the upper decision 107 boundary to the threshold for awareness (De Loof et al., 2016, Kang et al., 2017. Here, response times in the 108 bCFS paradigm reflected a perceptual decision (whether the face was rotated clockwise or anticlockwise) that 109 required conscious perception (Kang et al., 2017). We investigated how the rate of evidence accumulation (drift 110 rate; v), sensory processing and/or motor execution (non-decision time; t0), and the decision bounda...

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“…Thus, pathogen-avoidant strategies are likely to err on the side of caution by targeting both people who pose a disease threat and those who are only heuristically perceived to pose a disease threat. Indeed, one mechanism by which anxiety may increase threat perceptions involves lowering the decision criteria used to classify ambiguous stimuli as threatening (White, Skokin, Carlos, & Weaver, 2016).…”

Section: The Social Cognition Of Pathogen Avoidancementioning

confidence: 99%

State Anxiety and Pathogen Cues Jointly Promote Social Cognitive Responses to Pathogen Threats

2020

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Anxiety is an unpleasant, yet highly adaptive, aspect of human life. When coupled with the presence of specific situational threat cues, anxiety can promote functional threat-mitigating responses. Two experiments examined associations between anxiety and psychological responses that can reduce the threat of infectious pathogens. In each study, state anxiety was associated with heightened aversion toward possible sources of infectious disease (Study 1) and with prejudice toward a group heuristically associated with disease (Study 2), but only when the presence of situational pathogen cues was primed experimentally. State anxiety was unrelated to those outcomes in non-pathogen control priming conditions. These results highlight the joint roles that anxiety and situational pathogen cues may play in the avoidance of pathogens. At a broader level, findings provide insight into the processes by which general feelings of anxiety can promote functionally specific threat-management strategies.

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Using decision models to decompose anxiety-related bias in threat classification.

Cited by 36 publications

References 23 publications

Modeling Trait Anxiety: From Computational Processes to Personality

Modeling Trait Anxiety: From Computational Processes to Personality

Surprising Threats Accelerate Evidence Accumulation for Conscious Perception

State Anxiety and Pathogen Cues Jointly Promote Social Cognitive Responses to Pathogen Threats

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