Neural signatures of heterogeneity in risk‐taking and strategic consistency

Leota, Josh; Kleinert, Tobias; Tran, Alex; Nash, Kyle

doi:10.1111/ejn.15476

Cited by 13 publications

(11 citation statements)

References 146 publications

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“…The sLORETA (Pascual-Marqui, 2002) solution space, which has been used in many EEG studies (e.g., Leota et al, 2021; Nash et al, 2013; Schiller, Domes, & Heinrichs, 2020; Schiller et al, 2014), consists of 6.239 voxels (voxel size: 5 × 5 × 5 mm) and is restricted to cortical gray matter and hippocampi, as defined by the digitized Montreal Neurological Institute probability atlas. The sLORETA functional images represent the estimated electrical activity at each voxel as squared magnitude (i.e., power) of computed current density (unit: amperes per square meter, or A/m 2 ).…”

Section: Methodsmentioning

confidence: 99%

“…An individual score of risk-taking behavior (RT) was computed as (RT = average pumps × (explosions + 1)/total number of trials). Thus, RT increases with a higher average number of pumps and explosions, making it a more sensitive and valid measure of risk-taking compared with traditional measures that focus on the number of either pumps or explosions (also see Leota et al, 2021). Following a reviewer’s suggestion, we also checked whether our findings remained robust when using an alternative risk-taking score, as originally proposed by Lejuez et al, 2002.…”

Section: Methodsmentioning

confidence: 99%

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A Self-Controlled Mind Is Reflected by Stable Mental Processing

et al. 2022

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Self-control—the ability to inhibit inappropriate impulses—predicts economic, physical, and psychological well-being. However, recent findings demonstrate low correlations among self-control measures, raising the question of what self-control actually is. Here, we examined the idea that people high in self-control show more stable mental processing, characterized by processing steps that are fewer in number but longer lasting because of fewer interruptions by distracting impulses. To test this hypothesis, we relied on resting electroencephalography microstate analysis, a method that provides access to the stream of mental processing by assessing the sequential activation of neural networks. Across two samples (Study 1: N = 58 male adults from Germany; Study 2: N = 101 adults from Canada, 58 females), the temporal stability of resting networks (i.e., longer durations and fewer occurrences) was positively associated with self-reported self-control and a neural index of inhibitory control, and it was negatively associated with risk-taking behavior. These findings suggest that stable mental processing represents a core feature of a self-controlled mind.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Self-Controlled Mind Is Reflected by Stable Mental Processing

et al. 2022

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“…We suggest that the activation of vmPFC was related to risk perception, given its association with valuation and risk assessment (Spaniol et al,2019). The right dorsolateral prefrontal cortex (dlPFC) also showed a significantly greater activation for more favorable probabilities (p<0.05), which might possibly be explained by greater risk-taking behavior that was found in the literature to be associated with an increase in the activity of dlPFC (Leota et al,2021). A role of angular gyrus (AG) in both decision-making, visuospatial attention (Studer et al,2014) and number processing (Seghier,2013) has been recognized.…”

Section: Figure 2-correlations Between Active Coping Score and Age (L...mentioning

confidence: 62%

A multimodal approach to understand and improve cognitive decision-making during firefighting

Dionísio¹,

Duarte²,

Correia³

et al. 2022

Advances in Forest Fire Research 2022

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Day-to-day we have to make choices. Even simple decisions involve complex cognitive processes that are still not clearly understood. When it comes to critical decisions which may lead to impactful and irreversible consequences, the understanding of the decision-making process is highly relevant. Dysfunctional coping responses may lead to stress and ultimately to trauma. In the present project, we aim to understand decision-making in the context of firefighting and to study how neurocognitive control and stress management strategies affect decision-making. We present here a functional brain imaging paradigm coupled with biosensors while firefighters play a decision-making task. In this computerized task, the firefighter faces the dilemma of entering or not in a burning house, with variable risk of house collapsing, variable number of victims in danger and variable probability of the victims saving themselves. We present here the preliminary results from a cohort of 13 firefighters, of which none presented clinically significant symptoms of post-traumatic stress disorder. We found brain regions, including the ventromedial and dorsolateral prefrontal cortices and angular gyrus, showing a parametric pattern of activation during the decision phase, i.e. the lower the risk of collapsing, the higher the neural activity in these areas. This suggests that these regions are processing that risk information and signalling the chances of being successful in the rescue phase. We also found that the power in high frequencies of the pulse rate variability of this cohort of firefighters is higher when they decide to enter for the rescue. It is suggestive that deciding not to enter for rescue causes an increase in arousal, which may be related to the expectation about the victimsâ€™ outcome (whether they survived or not). Concerning coping strategies, we found that active coping used in personal context was significantly correlated with the age and the years of experience in our sample of firefighters. We aim to understand if this pattern of brain activity reflects the coping strategies and if, in turn, it is reflected in the physiological signals we measure. Concurrently, we are recruiting firefighters with post-traumatic stress disorder and non-firefighters to perform the same task. We will investigate whether the decision processes in firefighters are different from non-firefighters, and we will address if post-traumatic stress disorder impairs decision-making in this context. Biosignals will also be used in a second phase of the project to inform training systems about the physiological state of the firefighter during virtual reality simulation. This may help developing optimal neurocognitive control and better coping strategies to deal with stress.

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“…An individual score of risk-taking behavior (RT) was computed as (RT=average pumps * (explosions + 1)/total number of trials). Thus, RT increases with a higher average number of pumps and explosions, making it a more sensitive and valid measure of risk-taking compared to traditional measures which focus on the number of either pumps or explosions (also see Leota et al, 2021). Following a reviewer's suggestion, we also checked whether our findings remain robust when using an alternative risk-taking score, as originally proposed by Lejuez et al, 2002…”

Section: Measurement Of Self-reported Self-control and Risk-taking Be...mentioning

confidence: 85%

“…The sLORETA (Pascual-Marqui, 2002) solution space, which has been used in many EEG studies (e.g., Leota et al, 2021;Nash et al, 2013;Schiller, Domes, et al, 2020;Schiller et al, 2014), consists of 6.239 voxels (voxel size: 5 × 5 × 5 mm) and is restricted to cortical grey matter and hippocampi, as defined by the digitized Montreal Neurological Institute probability atlas. The sLORETA functional images represent the estimated electrical activity at each voxel as squared magnitude (i.e., power) of computed current density (unit: amperes per square meter, A/m 2 ).…”

Section: Eeg Source Localization Analysismentioning

confidence: 99%

A Self-Controlled Mind is Reflected by Stable Mental Processing

Kleinert¹,

Nash²,

Leota³

et al. 2022

Preprint

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Self-control–the ability to inhibit inappropriate impulses–predicts economic, physical, and psychological well-being. However, recent findings demonstrate low correlations among self-control measures, raising the questions what self-control actually is. Here, we examine the idea that people high in self-control show more stable mental processing, characterized by fewer, but longer lasting processing steps due to fewer interruptions by distracting impulses. To test this hypothesis, we relied on resting EEG microstate analysis, a method that provides access to the stream of mental processing by assessing the sequential activation of neural networks. Across two samples (N1=58 male adults from Germany; N2=101 adults from Canada [58 females]), the temporal stability of resting networks (i.e., longer durations and fewer occurrences) was positively associated with self-reported self-control and a neural index of inhibitory control, and negatively associated with risk-taking behavior. These findings suggest that stable mental processing represents a core feature of a self-controlled mind.

show abstract

Neural signatures of heterogeneity in risk‐taking and strategic consistency

Cited by 13 publications

References 146 publications

A Self-Controlled Mind Is Reflected by Stable Mental Processing

A Self-Controlled Mind Is Reflected by Stable Mental Processing

A multimodal approach to understand and improve cognitive decision-making during firefighting

A Self-Controlled Mind is Reflected by Stable Mental Processing

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