De-automatization of attentional bias in high trait anxiety : effects of hypnotic suggestion on the Stroop interference task

2010 ◽  
Author(s):  
Hai-yen Huang
Keyword(s):  
Attentional Bias ◽  
Trait Anxiety ◽  
Stroop Interference ◽  
Interference Task ◽  
Hypnotic Suggestion ◽  
High Trait ◽  
High Trait Anxiety

scholarly journals Gender Difference of Unconscious Attentional Bias in High Trait Anxiety Individuals

PLoS ONE ◽  
2011 ◽  
Vol 6 (5) ◽  
pp. e20305 ◽  
Author(s):  
Jieqing Tan ◽  
Zheng Ma ◽  
Xiaochao Gao ◽  
Yanhong Wu ◽  
Fang Fang
Keyword(s):  
Gender Difference ◽  
Attentional Bias ◽  
Trait Anxiety ◽  
High Trait ◽  
High Trait Anxiety

Mechanisms of cross-modal selective attentional bias for negative faces of anger and disgust in high-trait anxiety individuals

Neuroreport ◽  
2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Ningning Mao ◽  
Luyao Xia ◽  
Qin Zhang ◽  
Chieh Li ◽  
Lixia Cui
Keyword(s):  
Attentional Bias ◽  
Trait Anxiety ◽  
High Trait ◽  
High Trait Anxiety

scholarly journals EEG biofeedback improves attentional bias in high trait anxiety individuals

2013 ◽  
Vol 14 (1) ◽  
Author(s):  
Sheng Wang ◽  
Yan Zhao ◽  
Sijuan Chen ◽  
Guiping Lin ◽  
Peng Sun ◽  
...  
Keyword(s):  
Attentional Bias ◽  
Trait Anxiety ◽  
Eeg Biofeedback ◽  
High Trait ◽  
High Trait Anxiety

Components of attentional bias to threat in high trait anxiety: Facilitated engagement, impaired disengagement, and attentional avoidance

2006 ◽  
Vol 44 (12) ◽  
pp. 1757-1771 ◽  
Author(s):  
Ernst H.W. Koster ◽  
Geert Crombez ◽  
Bruno Verschuere ◽  
Stefaan Van Damme ◽  
Jan Roelf Wiersema
Keyword(s):  
Attentional Bias ◽  
Trait Anxiety ◽  
Attentional Avoidance ◽  
High Trait ◽  
High Trait Anxiety

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

2019 ◽  
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.

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

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.

High trait anxiety enhances optimal integration of auditory and visual threat cues

2022 ◽  
Vol 74 ◽  
pp. 101693
Author(s):  
Naomi Heffer ◽  
Molly Gradidge ◽  
Anke Karl ◽  
Chris Ashwin ◽  
Karin Petrini
Keyword(s):  
Trait Anxiety ◽  
Optimal Integration ◽  
High Trait ◽  
Threat Cues ◽  
High Trait Anxiety
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