Abstract:Value-based decision making (VBDM) is a principle that states that humans and other species adapt their behavior according to the dynamic subjective values of the chosen or unchosen options. The neural bases of this process have been extensively investigated using task-based fMRI and lesion studies. However, the growing field of resting-state functional connectivity (RSFC) may shed light on the organization and function of brain connections across different decision-making domains. With this aim, we used indep… Show more
“…The latter further split into a risk taking for gains and mixed gambles cluster and a sampling bias cluster. The factor structure, as well as the clustering, are broadly consistent with theoretical predictions of a cognitive control dimension separate from a decision-making dimension that segregates gain and loss contexts (Deza Araujo et al, 2018).…”
Section: Split-half Reliabilitysupporting
confidence: 74%
“…In a recent historical review, Nigg (2017) concluded that most theories include working memory and inhibition in cognitive control (see also Botvinik & Braver, 2015), but separate them from decision-making such as risk taking. Furthermore, empirical evidence shows a distinction in neural circuits underlying risk taking to avoid losses and risk taking for gains (Deza Araujo et al, 2018). Risk taking for gains decreases with age, putatively reflecting decreases in dopamine (Rutledge et al, 2016), and risk taking for losses has been linked to circadian rhythms (Bedder et al, 2020).…”
Self-regulation, the ability to guide behavior according to one’s goals, plays an integral role in understanding loss of control behaviors a pertinent example being substance use disorders (SUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors often occur, namely in real life situations outside the laboratory. Moreover, lab-based experimental tasks are criticized for poor test–retest reliability and a lack of construct validity. These concerns might in part explain why ecological validity of experimental measures—their ability to predict real-life behavior—is low. To address these shortcomings, we assessed the reliability and construct validity of four smartphone-based experimental tasks designed to measure cognitive control and decision-making. To facilitate future clinical applicability we recruited a large (N=488) sample of individuals with SUD. Joint modeling of measurement sessions increased the reliability of task measures from moderate to good and often excellent levels. In line with theories of cognitive control and motivation, three latent factors reflecting cognitive control and decision-making in the context of gains and losses best described the data. As proof of concept, we show that a latent cognitive control score based on joint modeling, yielded stronger correlations with drinking behavior than single task scores based on separate modeling. These findings indicate that in individuals with SUD, smartphone-based ambulatory experimental assessments can reliably index functions of cognitive control and decision-making, with plausible construct validity. Our findings provide evidence for rich possibilities arising from longitudinal experimental studies in SUD as well as in psychiatry, neuroscience, and psychology more generally.
“…The latter further split into a risk taking for gains and mixed gambles cluster and a sampling bias cluster. The factor structure, as well as the clustering, are broadly consistent with theoretical predictions of a cognitive control dimension separate from a decision-making dimension that segregates gain and loss contexts (Deza Araujo et al, 2018).…”
Section: Split-half Reliabilitysupporting
confidence: 74%
“…In a recent historical review, Nigg (2017) concluded that most theories include working memory and inhibition in cognitive control (see also Botvinik & Braver, 2015), but separate them from decision-making such as risk taking. Furthermore, empirical evidence shows a distinction in neural circuits underlying risk taking to avoid losses and risk taking for gains (Deza Araujo et al, 2018). Risk taking for gains decreases with age, putatively reflecting decreases in dopamine (Rutledge et al, 2016), and risk taking for losses has been linked to circadian rhythms (Bedder et al, 2020).…”
Self-regulation, the ability to guide behavior according to one’s goals, plays an integral role in understanding loss of control behaviors a pertinent example being substance use disorders (SUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors often occur, namely in real life situations outside the laboratory. Moreover, lab-based experimental tasks are criticized for poor test–retest reliability and a lack of construct validity. These concerns might in part explain why ecological validity of experimental measures—their ability to predict real-life behavior—is low. To address these shortcomings, we assessed the reliability and construct validity of four smartphone-based experimental tasks designed to measure cognitive control and decision-making. To facilitate future clinical applicability we recruited a large (N=488) sample of individuals with SUD. Joint modeling of measurement sessions increased the reliability of task measures from moderate to good and often excellent levels. In line with theories of cognitive control and motivation, three latent factors reflecting cognitive control and decision-making in the context of gains and losses best described the data. As proof of concept, we show that a latent cognitive control score based on joint modeling, yielded stronger correlations with drinking behavior than single task scores based on separate modeling. These findings indicate that in individuals with SUD, smartphone-based ambulatory experimental assessments can reliably index functions of cognitive control and decision-making, with plausible construct validity. Our findings provide evidence for rich possibilities arising from longitudinal experimental studies in SUD as well as in psychiatry, neuroscience, and psychology more generally.
“…Comparing risky with nonrisky adolescents, DeWitt, Aslan, and Filbey (2014) observed that the former group displayed increased connectivity between the amygdala and the right MFG, left cingulate gyrus, left precuneus, and right inferior parietal cortex and between the nucleus accumbens and the right MFG. In a similar vein, Deza Araujo et al (2018) demonstrated hyperconnectivity between the frontoparietal network and the occipital cortex and between the DMN and medial temporal and frontal regions in high risk-seeking behavior in losses (observed in people who prefer delayed potential high losses rather than immediate but sure small losses).…”
Impulsivity and sensation seeking are considered to be among the most important personality traits involved in risk-taking behavior. This study is focused on whether the association of these personality traits and brain functional connectivity depends on individuals' risk proneness. Risk proneness was assessed with the DOSPERT-30 scale and corroborated with performance in a motorcycle simulator. The associations of impulsivity-and sensation seeking-related traits with the between and within coupling of seven major brain functional networks, estimated from electroencefalograma (EEG) recordings, differ according to whether an individual is risk prone or not. In risk-prone individuals, (lack of) premeditation enhanced the coupling of the ventral attention and limbic networks. At the same time, emotion seeking increased the coupling of the frontoparietal network and the default mode networks (DMNs). Finally, (lack of) perseverance had a positive impact on the coupling of anterior temporal nodes of the limbic network whilst having a negative impact on some frontal nodes of the frontoparietal network and the DMNs. In general, the results suggest that the predisposition to behave riskily modulates the way in which impulsivity traits are linked to brain functionality, seemingly making the brain networks prepare for an immediate, automatic, and maladaptive response.K E Y W O R D S brain functional coupling, impulsivity, personality traits, risk taking, sensation seeking
“…Other research work conducted by Araujo et al (2018) discussed the distinct connectivity patterns of large-scale brain networks that may underlie individual differences in decision-making in healthy populations. Results showed that higher risk seeking for losses was associated with increased connectivity between medial temporal regions, frontal regions, and the default mode network.…”
Due to the ambiguity between risk attitudes, this study aims at ranking the different risk attitudes considering the factors that affect the behaviour of the decision-makers. Both the technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) are employed to address the characteristics of risk attitudes aiming to highlight the criteria significance and finally to rank the most impactful risk attitude. It was found that regret aversion and risk aversion attitudes have higher impact in real life decision-making problems. In contrast, the maximin and maximax risk attitudes have the lowest importance. Risk seeking and regret aversion attitudes demonstrated the highest importance using TOPSIS of equal-weights while the importance of loss aversion and regret aversion have the highest for the AHP-TOPSIS approach. The results of this study can be beneficial for decision-makers who encounter a variety of risk attitudes in their decision problems.
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