From Impulse to Action? Cognitive Mechanisms of Impulsivity-Related Risk for Externalizing Behavior

Revill, Ali S.; Patton, Kiri; Connor, Jason P.; Sheffield, Jeanie; Wood, Andrew; Castellanos‐Ryan, Natalie; Gullo, Matthew J.

doi:10.1007/s10802-020-00642-7

Cited by 8 publications

(3 citation statements)

References 75 publications

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“…The results are congruent with previous cross‐sectional studies. The previous studies indicated that children and adolescents who have higher levels of hostile automatic thoughts are prone to engage in cyber‐aggression (Revill et al, 2020; Yu et al, 2017). Moreover, anger rumination, as a kind of intrusive hostile thought has been found to be associated with aggression in college students (Zhu et al, 2020), and cyber‐aggression in adolescents (Camacho et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the repetitive invasion of hostile thoughts may consume the limited cognitive resources and reduce self-control, which lead individuals to be more likely to carry out aggressive behaviors against provokers or innocent others (Denson et al, 2012). Previous studies showed hostile automatic thoughts could predict the externalizing problems (Revill et al, 2020;Yu et al, 2017). Moreover, a recent study showed negative automatic thoughts could increase the likelihood of cyber-aggression (Lu et al, 2019).…”

Section: Hostile Automatic Thoughts and Cyber-aggressionmentioning

confidence: 99%

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The longitudinal relation between violence exposure in daily life, hostile automatic thoughts, and cyber‐aggression

Zhu

Sun

et al. 2022

Aggressive Behavior

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Cyber‐aggression is a serious social problem worldwide. Its risks have been frequently explored, and violence exposure in daily life has been regarded as an important risk factor of cyber‐aggression. However, the longitudinal association between violence exposure in daily life and cyber‐aggression has not yet been examined, and the mechanisms underlying the link between violence exposure and cyber‐aggression remain largely unclear. Based on the General Aggression Model and Script Theory, we explored the circular relation between violence exposure in daily life, hostile automatic thoughts, and cyber‐aggression. The current study adopted a longitudinal design to address these issues among 941 college students. The results indicated violence exposure in daily life predicted hostile automatic thoughts and cyber‐aggression 6 months later; hostile automatic thoughts predicted violence exposure and cyber‐aggression 6 months later; and cyber‐aggression predicted hostile automatic thoughts and violence exposure 6 months later. Moreover, each of them plays a mediating role in the association between the other two variables. These results support and expand the General Aggression Model and Script Theory that violence exposure, aggressive cognition, and aggression facilitate each other. This also provides theoretical guidance on reducing cyber‐aggression in daily life.

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Section: Discussionmentioning

confidence: 99%

Section: Hostile Automatic Thoughts and Cyber-aggressionmentioning

confidence: 99%

The longitudinal relation between violence exposure in daily life, hostile automatic thoughts, and cyber‐aggression

Zhu

Sun

et al. 2022

Aggressive Behavior

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“…Relapses are a common complication that may occur in one's recovery journey when triggered by specific events and occurrences [7]. Though a stronger network of support (albeit in-person or technology-enabled) that helps address biopsychosocial mechanisms can positively impact behavior changes, when unanticipated events or situations occur, they can trigger relapses that diminish recovery capital across its three domains and lead to poor health outcomes [8,9]. One element that may disrupt this process is that of incentivization.…”

Section: Problem Definitionmentioning

confidence: 99%

Closing the Gap on Addiction Recovery Engagement with an AI-infused Convolutional Neural Network Technology Application—A Design Vision

Jacob,

McDonald,

Bohn

2024

AJNNA

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Currently, real-time detection networks elaborate the technical details of the Faster Regional Convolution Neural Network (R-CNN) recognition pipeline. Within existing R-CNN literature, the evolution exhibited by R-CNN is most profound in terms of computational efficiency integrating each training stage to reduce test time and improvement in mean average precision (mAP), which can be infused into an artificially intelligent (AI), machine learning (ML), real-time, interactive, recovery capital application (app). This article introduces a Region Proposal Network (RPN) that shares full-image convolutional features with a real-time detection AI-ML infused network in an interactive, continuously self-learning wrist-wearable real-time recovery capital app for enabling cost-free region proposals (e.g., instantaneous body physiological responses, mapped connections to emergency services, sponsor, counselor, peer support, links to local and specific recovery capital assets, etc.). A fully merged RPN and Faster R-CNN deep convolutional unified network in the app can simultaneously train to aggregate and predict object bounds and objectness scores for implementing recovery capital real-time solutions (e.g., baseball card scoring dashboards, token-based incentive programs, etc.) A continuous training scheme alternates between fine-tuning RPN tasks (e.g., logging and updating personal client information, gamification orientation) and fine-tuning the detection (e.g., real-time biometric monitoring client’s behavior for self-awareness of when to connect with an addiction specialist or family member, quick response (QR) code registration for a 12-step program, advanced security encryption, etc.) in the interactive app. The very deep VGG-16 model detection system has a frame rate of 5fps within a graphic processing unit (GPU) while accomplishing sophisticated object detection accuracy on PASCAL Visual Object Classification Challenge (PASCAL VOC) and Microsoft Common Objects in Context (MS COCO) datasets. This is achieved with only 300 proposals per real-time retrieved data capture point, information bit or image. The app has real-time, infused cartographic and statistical tracking tools to generate Python Codes, which can enable a gamified addiction recovery-oriented digital conscience. Faster R-CNN and RPN can be the foundations of an interactive real-time recovery capital app that can be adaptable to multiple recovery pathways based on participant recovery plans and actions. This paper discusses some of the critical attributes and features to include in the design of a future app to support and close current gaps in needed recovery capital to help those who are dealing with many different forms of addiction recovery.

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Longitudinal relations between bullying victimization and aggression: The multiple mediation effects of anger rumination and hostile automatic thoughts

Quan,

Huang,

et al. 2024

PsyCh Journal

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Bullying victimization is widely accepted to be associated with aggression. However, the mechanisms underlying this relationship remain unknown. To examine the long‐term impact of bullying victimization on aggression, the present study tested the potential mediating effects of both anger rumination and hostile automatic thoughts. A total of 809 undergraduates from four universities across China (74.80% female; Mage = 19.63 years, SD = 0.82 years) completed the survey on three occasions, with a 6‐month delay between Time 1 and Time 2 and a 1‐year interval between Time 2 and Time 3. A cross‐lagged model of anger rumination and hostile automatic thoughts was developed to test whether they predicted one another, and two structural models were constructed to test their mediating roles in bullying victimization and aggression. Findings indicated that anger rumination and hostile automatic thoughts are mutually predictable; the correlation between bullying victimization and aggression is mediated independently by anger rumination and hostile automatic thoughts, and the chain mediation of bullying victimization predicting aggression first through anger rumination and then through hostile automatic thinking was established. In addition, an alternative mediation model is also significant, with hostile automatic thoughts as the primary mediator and anger rumination as the secondary mediator. These results highlight the significance of anger rumination and hostile automatic thoughts in the long‐term effects of bullying victimization on aggression. Interventions designed to reduce undergraduate students' anger rumination and hostile automatic thoughts may help reduce their aggression.

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From Impulse to Action? Cognitive Mechanisms of Impulsivity-Related Risk for Externalizing Behavior

Cited by 8 publications

References 75 publications

The longitudinal relation between violence exposure in daily life, hostile automatic thoughts, and cyber‐aggression

The longitudinal relation between violence exposure in daily life, hostile automatic thoughts, and cyber‐aggression

Closing the Gap on Addiction Recovery Engagement with an AI-infused Convolutional Neural Network Technology Application—A Design Vision

Longitudinal relations between bullying victimization and aggression: The multiple mediation effects of anger rumination and hostile automatic thoughts

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