Objective: Reinforcement Sensitivity Theory (RST) is a theory of motivation, emotion, and learning, that has been translated into an account of personality. RST proposes neural structures that form the basis of systems responsible for reward (behavioral approach system; BAS), punishment (flight–fight–freeze system; FFFS) and conflict processing (behavioral inhibition system; BIS). This systematic review collated studies examining psychometric measures of RST alongside structural and function Magnetic Resonance Imaging (MRI) data to (a) examine how psychometric RST is associated with the proposed neural topologies of RST, (b) identify any common associations between psychometric RST and other brain regions, and (c) provide recommendations for advancing the current literature base. Method: Initial search terms identified 10,952 papers. After processing, 39 papers that investigated the association between RST scales and neural functioning in healthy adult samples were included in this review. Results: There was general support for associations between the BAS and the structure/activity of the prefrontal cortex and ventral striatum with some additional findings for the ventral pallidum and ventral tegmental area. There was also some support for associations between BIS/FFFS and structure/activity of frontal regions, cingulate cortices and the amygdala. Conclusions: Overall, psychometric correlates of RST were associated with activity in proposed neural circuitry, with the most consistent support being found for the BAS; however, psychometric and experimental limitations still hamper the differentiation of the BIS and FFFS systems in their activation of deeper brain networks. Future studies need to include revised RST scales that separate the BIS and FFFS and implement more rigorous tasks that allow for the examination of each system both independently and codependently.
The extent to which similar capacity limits in visual attention and visual working memory indicate a common shared underlying mechanism is currently still debated. In the spatial domain, the multiple object tracking (MOT) task has been used to assess the relationship between spatial attention and spatial working memory though existing results have been inconclusive. In three dual task experiments, we examined the extent of interference between attention to spatial positions and memory for spatial positions. When the position monitoring task required keeping track of target identities through colour-location binding, we found a moderate detrimental effect of position monitoring on spatial working memory and an ambiguous interaction effect. However, when this task requirement was removed, load increases in neither task were detrimental to the other. The only very moderate interference effect that remained resided in an interaction between load types but was not consistent with shared capacity between tasks—rather it was consistent with content-related crosstalk between spatial representations. Contrary to propositions that spatial attention and spatial working memory may draw on a common shared set of core processes, these findings indicate that for a purely spatial task, perceptual attention and working memory appear to recruit separate core capacity-limited processes.
Virtual reality (VR) is receiving widespread attention as a delivery tool for exposure therapies. The advantage offered by VR over traditional technology is a greater sense of presence and immersion, which magnifies user effects and enhances the effectiveness of exposure-based interventions. The current study systematically examined the basic factors involved in generating presence in VR as compared to standard technology, namely (1) system-driven factors that are exclusive to VR devices while controlling general factors such as field of view and image quality; (2) media-driven factors of the virtual environment eliciting motivational salience through different levels of arousal and valence (relaxing, exciting and fear evoking stimuli); and (3) the effects of presence on magnifying affective response. Participants (N = 14) watched 3 different emotionally salient videos (1 × fear evoking, 1 × relaxing and 1 × exciting) in both viewing modes (VR and Projector). Subjective scores of user experience were collected as well as objective EEG markers of presence (frontal alpha power, theta/beta ratio). Subjective and objective presence was significantly greater in the VR condition. There was no difference in subjective or objective presence for stimulus type, suggesting presence is not moderated by arousal, but may be reliant on activation of motivational systems. Finally, presence did not magnify feelings of relaxation or excitement, but did significantly magnify users’ experience of fear when viewing fear evoking stimuli. This is in line with previous literature showing strong links between presence and generation of fear, which is vital in the efficacy of exposure therapies.
Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety.
Successful psychopathy is characterized by core traits of callousness and interpersonal manipulation associated with clinical definitions of psychopathy, whilst largely being successful or adaptive in daily life. Despite rising interest in the field, the construct is still not well understood, partially due to competing theoretical models and the lack of a well-validated measurement. Using theoretical literature and expert opinion, we developed and validated the Successful Psychopathy Scale (SPS) for use within the general population. The pilot study (N = 208) developed an initial item pool which was subjected to Deductive Rational Strategy, reliability testing and an Exploratory factor analysis on 51-items which indicated a 5-facet structure. Study 1 (N = 403) developed on the pilot structure using an extended item pool based on researchers’ theoretical knowledge and expert ratings conducted in the pilot in an independent sample. The finalized structure of the SPS was 6-facets representing the key domains within successful psychopathy; core psychopathic traits, risk taking, confidence, stress immunity, social potency, and manipulation. Rasch analysis was used to verify the most suitable candidate items for a reliable and valid SPS measure its shorter form. The final 54-item long form and 30-item short form versions were satisfied expectations of unidimensionality with minor modifications resolved by creating super-items. The final versions of the SPS were correlated in expected directions with existing measures of psychopathic traits, professional skills, and success expectancy.
Virtual-reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required.In this work, we have explored a series of machine learning models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of machine learning models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of Virtual Reality Exposure Therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety.
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