On second thought… The influence of a second stage in the ultimatum game on decision behavior, electro-cortical correlates and their trait interrelation

In this study, we tested the influence of a second bargaining stage in an ultimatum game (UG) concerning behavioral responses, electro-cortical correlates and their moderations by the trait’s altruism, anger, anxiety and greed in 92 participants. We found that an additional stage led to more rejection in the 2-stage UG (2SUG) and that an increase in offer in the second stage led to more acceptance. The FRN during a trial was linked to expectance evaluation concerning the fairness of the offers, while midfrontal theta was a marker for the needed cognitive control to overcome the respective default behavioral pattern. The FRN responses to unfair offers were more negative for either low or high altruism in the UG, while high trait anxiety led to more negative FRN responses in the first stage of 2SUG, indicating a higher sensitivity to unfairness. Accordingly, the mean FRN response, representing the trait-like general electrocortical reactivity to unfairness, predicted rejection in the first stage of 2SUG. Additionally, we found that high trait anger led to more rejections for unfair offer in 2SUG in general, while trait altruism led to more rejection of unfair offers that were not improved in the second stage of 2SUG. In contrast, trait anxiety led to more acceptance in the second stage of 2SUG, while trait greed even led to more acceptance if the offer was worse than in the stage before. These findings suggest, that 2SUG creates a trait activation situation compared to the UG.

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Trait Anger, Locus of Control, Religiosity, and Authoritarianism as Related to Trait Anxiety

Psychological Reports ◽

10.2466/pr0.1982.51.3.941 ◽

1982 ◽

Vol 51 (3) ◽

pp. 941-942 ◽

Cited By ~ 7

Author(s):

Sung-Mook Hong ◽

Christelle M. Withers

Keyword(s):

High School Students ◽

Locus Of Control ◽

Trait Anxiety ◽

Trait Anger ◽

School Students ◽

Anxiety Group ◽

High Trait ◽

High Trait Anxiety ◽

Relationship Of ◽

The Relationship

To clarify the relationship of trait anxiety to trait anger, religiosity, locus of control and authoritarianism, high and low trait-anxiety groups, identified from 121 high school students, were compared on the four variables. Only trait anger yielded a significant t ratio, indicating that the high trait-anxiety group appeared to have a higher level of trait anger than low trait-anxiety group.

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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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