Interpretation bias of high trait anxiety Chinese military servicemen in ambiguous military scenarios

Abstract Background: Anxiety has become one of the most common psychological problems affecting the combat effectiveness of soldiers. As the generation, maintenance, and recurrence of anxiety have an important interaction with interpretation bias, yet none proof was for the existence of interpretation bias in military personnel.Methods: 112 military officers and soldiers were recruited. Based on scores of the Trait-anxiety Inventory, participants were divided into the high trait anxiety group and the low trait anxiety group. the Picture Sentence Association Paradigm comprised of military-simulated ambiguous scenarios and emotional facial expressions was used to test the differences of the interpretation bias between the two groups.Results: Military personnel with high trait anxiety showed interpretation bias by endorsing more negative valence to the ambiguous scenarios and reject the positive valence. Especially in a self-related scenario, the interpretation bias was more remarkable.Conclusion: This study revealed the existed interpretation bias in military personnel with high trait anxiety using a new paradigm and highlighted the need for further researches to improve the measurement of interpretation bias. Moreover, the picture sentence association paradigm could provide plausible methods for cognitive bias modification to decrease the soldiers’ anxiety.

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The role of state and trait anxiety in the processing of facial expressions of emotion

Royal Society Open Science ◽

10.1098/rsos.210056 ◽

2022 ◽

Vol 9 (1) ◽

Author(s):

Maddy L. Dyer ◽

Angela S. Attwood ◽

Ian S. Penton-Voak ◽

Marcus R. Munafò

Keyword(s):

Emotion Recognition ◽

Trait Anxiety ◽

State Anxiety ◽

Recognition Accuracy ◽

Emotion Processing ◽

Interpretation Bias ◽

Facial Emotion ◽

High Trait ◽

High Trait Anxiety

State anxiety appears to influence facial emotion processing (Attwood et al . 2017 R. Soc. Open Sci. 4 , 160855). We aimed to (i) replicate these findings and (ii) investigate the role of trait anxiety, in an experiment with healthy UK participants ( N = 48, 50% male, 50% high trait anxiety). High and low state anxiety were induced via inhalations of 7.5% carbon dioxide enriched air and medical air, respectively. High state anxiety reduced global emotion recognition accuracy ( p = 0.01, η p 2 = 0.14 ), but it did not affect interpretation bias towards perceiving anger in ambiguous angry–happy facial morphs ( p = 0.18, η p 2 = 0.04 ). We found no clear evidence of a relationship between trait anxiety and global emotion recognition accuracy ( p = 0.60, η p 2 = 0.01 ) or interpretation bias towards perceiving anger ( p = 0.83, η p 2 = 0.01 ). However, there was greater interpretation bias towards perceiving anger (i.e. away from happiness) during heightened state anxiety, among individuals with high trait anxiety ( p = 0.03, d z = 0.33). State anxiety appears to impair emotion recognition accuracy, and among individuals with high trait anxiety, it appears to increase biases towards perceiving anger (away from happiness). Trait anxiety alone does not appear to be associated with facial emotion processing.

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Strategy bias in the emotion regulation of high trait anxiety individuals: An investigation of underlying neural signatures using ERPs.

Neuropsychology ◽

10.1037/neu0000471 ◽

2019 ◽

Vol 33 (1) ◽

pp. 111-122 ◽

Cited By ~ 3

Author(s):

Dong-ni Pan ◽

Yi Wang ◽

Xuebing Li

Keyword(s):

Emotion Regulation ◽

Trait Anxiety ◽

High Trait ◽

High Trait Anxiety ◽

Neural Signatures

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A machine learning approach to identifying objective biomarkers of anxiety and stress

10.1101/745315 ◽

2019 ◽

Cited By ~ 1

Author(s):

Arjun Ramakrishnan ◽

Adam Pardes ◽

William Lynch ◽

Christopher Molaro ◽

Michael Louis Platt

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Trait Anxiety ◽

Video Game ◽

Social Stress ◽

Internal State ◽

Self Report ◽

Support Vector ◽

High Trait ◽

High Trait Anxiety

AbstractAnxiety and stress-related disorders are highly prevalent and debilitating conditions that impose an enormous burden on society. Sensitive measurements that can enable early diagnosis could mitigate suffering and potentially prevent onset of these conditions. Self-reports, however, are intrusive and vulnerable to biases that can conceal the true internal state. Physiological responses, on the other hand, manifest spontaneously and can be monitored continuously, providing potential objective biomarkers for anxiety and stress. Recent studies have shown that algorithms trained on physiological measurements can predict stress states with high accuracy. Whether these predictive algorithms generalize to untested situations and participants, however, remains unclear. Further, whether biomarkers of momentary stress indicate trait anxiety – a vulnerability foreshadowing development of anxiety and mood disorders – remains unknown. To address these gaps, we monitored skin conductance, heart rate, heart rate variability and EEG in 39 participants experiencing physical and social stress and compared these measures to non-stressful periods of talking, rest, and playing a simple video game. Self-report measures were obtained periodically throughout the experiment. A support vector machine trained on physiological measurements identified stress conditions with ~96% accuracy. A decision tree that optimally combined physiological and self-report measures identified individuals with high trait anxiety with ~84% accuracy. Individuals with high trait anxiety also displayed high baseline state anxiety but a muted physiological response to acute stressors. Overall, these results demonstrate the potential for using machine learning tools to identify objective biomarkers useful for diagnosing and monitoring mental health conditions like anxiety and depression.

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During Vigilance to Painful Stimuli: Slower Response Rate Related to High Trait Anxiety, whereas a Faster Response Rate is related to High State Anxiety

Journal of Neurophysiology ◽

10.1152/jn.00492.2020 ◽

2020 ◽

Author(s):

Timothy J Meeker ◽

Nichole M. Emerson ◽

Jui-Hong Chien ◽

Mark I. Saffer ◽

Oscar Joseph Bienvenu ◽

...

Keyword(s):

Response Bias ◽

Trait Anxiety ◽

State Anxiety ◽

Response Rate ◽

Time On Task ◽

Lower Response Rate ◽

State And Trait Anxiety ◽

High Trait ◽

High Trait Anxiety ◽

Over Time

A pathological increase in vigilance, or hypervigilance, may be related to pain intensity in some clinical pain syndromes and may result from attention bias to salient stimuli mediated by anxiety. During a continuous performance task where subjects discriminated painful target stimuli from painful nontargets, we measured detected targets (hits), nondetected targets (misses), nondetected nontargets (correct rejections), and detected nontargets (false alarms). Using signal detection theory, we calculated response bias, the tendency to endorse a stimulus as a target, and discriminability, the ability to discriminate a target from nontarget. Due to the relatively slow rate of stimulus presentation our primary hypothesis was that sustained performance would result in a more conservative response bias reflecting a lower response rate over time on task. We found a more conservative response bias with time on task and no change in discriminability. We predicted that greater state and trait anxiety would lead to a more liberal response bias. A multivariable model provided partial support for our prediction; high trait anxiety related to a more conservative response bias (lower response rate), while high state anxiety related to a more liberal bias. This inverse relationship of state and trait anxiety is consistent with reports of effects of state and trait anxiety on reaction times to threatening stimuli. In sum, we report that sustained attention to painful stimuli was associated with a decrease in the tendency of the subject to respond to any stimulus over time on task, while the ability to discriminate target from nontarget is unchanged.

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