Browsing by Author "Casas, Brooks"

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    A 4-year longitudinal neuroimaging study of cognitive control using latent growth modeling: developmental changes and brain-behavior associations
    Kim-Spoon, Jungmeen; Herd, Toria; Brieant, Alexis; Elder, Jacob; Lee, Jacob; Deater-Deckard, Kirby; Casas, Brooks (2021-08-15)
    Despite theoretical models suggesting developmental changes in neural substrates of cognitive control in adolescence, empirical research has rarely examined intraindividual changes in cognitive control-related brain activation using multi-wave multivariate longitudinal data. We used longitudinal repeated measures of brain activation and behavioral performance during the multi-source interference task (MSIT) from 167 adolescents (53% male) who were assessed annually over four years from ages 13 to 17 years. We applied latent growth modeling to delineate the pattern of brain activation changes over time and to examine longitudinal associations between brain activation and behavioral performance. We identified brain regions that showed differential change patterns: (1) the fronto-parietal regions that involved bilateral insula, bilateral middle frontal gyrus, left pre-supplementary motor area, left inferior parietal lobule, and right precuneus; and (2) the rostral anterior cingulate cortex (rACC) region. Longitudinal confirmatory factor analyses of the fronto-parietal regions revealed strong measurement invariance across time implying that multivariate functional magnetic resonance imaging data during cognitive control can be measured reliably over time. Latent basis growth models indicated that fronto-parietal activation decreased over time, whereas rACC activation increased over time. In addition, behavioral performance data, age-related improvement was indicated by a decreasing trajectory of intraindividual variability in response time across four years. Testing longitudinal brain-behavior associations using multivariate growth models revealed that better behavioral cognitive control was associated with lower fronto-parietal activation, but the change in behavioral performance was not related to the change in brain activation. The current findings suggest that reduced effects of cognitive interference indicated by fronto-parietal recruitment may be a marker of a maturing brain that underlies better cognitive control performance during adolescence.
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    A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity
    Lahnakoski, Juha M.; Nolte, Tobias; Solway, Alec; Vilares, Iris; Hula, Andreas; Feigenbaum, Janet; Lohrenz, Terry; Casas, Brooks; Fonagy, Peter; Montague, P. Read; Schilbach, Leonhard (Elsevier, 2024-05-26)
    Background: Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci. Aims: Evaluate discriminative performance and generalizability of functional connectivity markers for BPD. Method: Whole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain—global ROI-based network, seed-based ROI-connectivity, functional consistency, and voxel-to-voxel connectivity—and evaluated the generalizability of the classification in the left-out portion of non-matched data. Results: Full-brain connectivity allowed classification (∼70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (∼61–70 %). Highest seed-based accuracies were in a similar range to global accuracies (∼70–75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed-ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level. Limitations: The accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability. Conclusions: Spatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population.
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    Altered Neural and Behavioral Associability-Based Learning in Posttraumatic Stress Disorder
    Brown, Vanessa (Virginia Tech, 2015-02-26)
    Posttraumatic stress disorder (PTSD) is accompanied by marked alterations in cognition and behavior, particularly when negative, high-value information is present (Aupperle, Melrose, Stein, & Paulus, 2012; Hayes, Vanelzakker, & Shin, 2012) . However, the underlying processes are unclear; such alterations could result from differences in how this high value information is updated or in its effects on processing future information. To untangle the effects of different aspects of behavior, we used a computational psychiatry approach to disambiguate the roles of increased learning from previously surprising outcomes (i.e. associability; Li, Schiller, Schoenbaum, Phelps, & Daw, 2011) and from large value differences (i.e. prediction error; Montague, 1996; Schultz, Dayan, & Montague, 1997) in PTSD. Combat-deployed military veterans with varying levels of PTSD symptoms completed a learning task while undergoing fMRI; behavioral choices and neural activation were modeled using reinforcement learning. We found that associability-based loss learning at a neural and behavioral level increased with PTSD severity, particularly with hyperarousal symptoms, and that the interaction of PTSD severity and neural markers of associability based learning predicted behavior. In contrast, PTSD severity did not modulate prediction error neural signal or behavioral learning rate. These results suggest that increased associability-based learning underlies neurobehavioral alterations in PTSD.
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    Assessing and remediating altered reinforcement learning in depression
    Brown, Vanessa (Virginia Tech, 2018-07-06)
    Major depressive disorder is a common, impairing disease, but current treatments are only moderately effective. Understanding how processes such as reward and punishment learning are disrupted in depression and how these disruptions are remediated through treatment is vital to improving outcomes for people with this disorder. In the present set of studies, computational reinforcement learning models and neuroimaging were used to understand how symptom clusters of depression (anhedonia and negative affect) were related to neural and behavioral measures of learning (Study 1, in Paper 1), how these alterations changed with improvement in symptoms after cognitive behavioral therapy (Study 2, in Paper 1), and how learning parameters could be directly altered in a learning retraining paradigm (Study 3, in Paper 2). Results showed that anhedonia and negative affect were uniquely related to changes in learning and that improvement in these symptoms correlated with changes in learning parameters; these parameters could also be changed through targeted queries based on reinforcement learning theory. These findings add important information to how learning is disrupted in depression and how current and novel treatments can remediate learning and improve symptoms.
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    Associability-modulated loss learning is increased in posttraumatic stress disorder
    Brown, Vanessa M.; Zhu, Lusha; Wang, John M.; Frueh, B. Christopher; Casas, Brooks; Chiu, Pearl H. (eLife Sciences, 2018-01-09)
    Disproportionate reactions to unexpected stimuli in the environment are a cardinal symptom of posttraumatic stress disorder (PTSD). Here, we test whether these heightened responses are associated with disruptions in distinct components of reinforcement learning. Specifically, using functional neuroimaging, a loss-learning task, and a computational model-based approach, we assessed the mechanistic hypothesis that overreactions to stimuli in PTSD arise from anomalous gating of attention during learning (i.e., associability). Behavioral choices of combat- deployed veterans with and without PTSD were fit to a reinforcement learning model, generating trial-by-trial prediction errors (signaling unexpected outcomes) and associability values (signaling attention allocation to the unexpected outcomes). Neural substrates of associability value and behavioral parameter estimates of associability updating, but not prediction error, increased with PTSD during loss learning. Moreover, the interaction of PTSD severity with neural markers of associability value predicted behavioral choices. These results indicate that increased attention- based learning may underlie aspects of PTSD and suggest potential neuromechanistic treatment targets.
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    Association between Reward Sensitivity and Smoking Status in Major Depressive Disorder
    Feng, Shengchuang (Virginia Tech, 2017-05-10)
    Chronic nicotine use has been linked to increased sensitivity to nondrug rewards as well as improvement in mood among individuals with depression, and these effects have been hypothesized to be mediated through alternations in striatal dopamine activity. Similarly, chronic nicotine use is hypothesized to influence the mechanisms by which healthy and depressed individuals learn about rewards in their environment. However, the specific behavioral and neural mechanisms by which nicotine influences the learning process is poorly understood. Here, we use a probabilistic learning task, functional magnetic resonance imaging and neurocomputational analyses, to show that chronic smoking is associated with higher reward sensitivity, along with lower learning rate and striatal prediction error signal. Further, we show that these effects do not differ between individuals with and without major depressive disorder (MDD). In addition, a negative correlation between reward sensitivity and striatal prediction error signal was found among smokers, consistent with the suggestion that enhanced tonic dopamine associated with increased reward sensitivity leads to an attenuation of phasic dopamine activity necessary for updating of reward value during learning.
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    Associations between peer attachment and neural correlates of risk processing across adolescence
    Asscheman, J. Susanne; Deater-Deckard, Kirby; Lauharatanahirun, Nina; van Lier, Pol A. C.; Koot, Susanne; Casas, Brooks; Kim-Spoon, Jungmeen (2020-04)
    Adolescence is a period of increased risk-taking behavior where individual differences in risk taking may relate to both adverse and positive experiences with peers. Yet, knowledge on how risk processing develops in the adolescent brain and whether this development is related to peer attachment is limited. In this longitudinal functional magnetic resonance imaging (fMRI) study, we collected data from 167 adolescents (53% male) followed for four annual assessments across ages 13-17 years. At each assessment, participants completed a lottery choice task to assess neural risk processing and reported on their perceived attachment to peers and parents. Behaviorally, risk-preference on the lottery choice task decreased linearly with age. Neural activation during risk processing was consistently found in the insula and dACC across the four assessments and increased linearly from ages 13-17 years. Furthermore, higher peer attachment was related to greater right insula risk processing for males but not for females, even after controlling for parental attachment. The magnitudes of this association did not change with age. Findings demonstrate that neural risk processing shows maturation across adolescence and high peer attachment may be associated with low risk taking by heightening neural sensitivity to potential risks for male adolescents.
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    Attachment and borderline personality disorder as the dance unfolds: A quantitative analysis of a novel paradigm
    Mancinelli, Federico; Nolte, Tobias; Griem, Julia; Lohrenz, Terry; Feigenbaum, Janet; Casas, Brooks; Montague, P. Read; Fonagy, Peter; Mathys, Christoph (Elsevier, 2024-04-17)
    Current research on personality disorders strives to identify key behavioural and cognitive facets of patient functioning, to unravel the underlying root causes and maintenance mechanisms. This process often involves the application of social paradigms — however, these often only include momentary affective depictions rather than unfolding interactions. This constitutes a limitation in our capacity to probe core symptoms, and leaves potential findings uncovered which could help those who are in close relationships with affected individuals. Here, we deployed a novel task in which subjects interact with four unknown virtual partners in a turn-taking paradigm akin to a dance, and report on their experience with each. The virtual partners embody four combinations of low/high expressivity of positive/negative mood. Higher scores on our symptomatic measures of attachment anxiety, avoidance, and borderline personality disorder (BPD) were all linked to a general negative appraisal of all the interpersonal experiences. Moreover, the negative appraisal of the partner who displayed a high negative/low positive mood was tied with attachment anxiety and BPD symptoms. The extent to which subjects felt responsible for causing partners’ distress was most strongly linked to attachment anxiety. Finally, we provide a fully-fledged exploration of move-by-move action latencies and click distances from partners. This analysis underscored slower movement initiation from anxiously attached individuals throughout all virtual interactions. In summary, we describe a novel paradigm for second-person neuroscience, which allowed both the replication of established results and the capture of new behavioural signatures associated with attachment anxiety, and discuss its limitations.
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    Autonomic Patterns of Emotion across Multiple Contexts
    Mcginley, Jared J. (Virginia Tech, 2015-06-17)
    Research on the autonomic specificity of emotion has spanned several decades. Even though considerable evidence exists for supporting autonomic specificity for discrete emotion states (Kreibig, 2010), there is still an active debate, and conflicting explanations, for these findings (Quigley and Barrett, 2014). There have been several studies employing multivariate pattern classification analytic techniques and calls for those types of studies are still prevalent (Kragel and LaBar, 2014). Although many studies have explored the autonomic specificity of emotions, few have explored what effects the induction methods, themselves, have had in inducing the autonomic change. Autonomic specificity of induction methods might be a meaningful, and confounding, phenomenon in this literature. Based on this unknown variable, the current experiment was designed to see if methods for emotion elicitation could be meaningfully captured by these same pattern classification techniques. This was accomplished using three separate emotion-elicitation methods to elicit five separate emotions. A sample of 64 college-aged students watched film clips, read imagery scripts, and recalled personal memories for five discrete emotions. Using discriminant analysis, the evidence from the current study lent less support for autonomic specificity of emotion than past experiments, and lends some support for providing future exploration into autonomic change that is related to methods for induction. Potential confounds and task fatigue effects are discussed.
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    The Behavioral and Neural Mechanisms of Social and Non-social Risky Decision-Making
    Lauharatanahirun, Nina (Virginia Tech, 2013-05-06)
    Decisions made under risk have been primarily studied within economic contexts (Platt & Huettel, 2008). This has led to the development of sound methods and models for studying risky choice behavior (Rangel, Camerer & Montague, 2008). In particular, these models are helpful for estimating how much risk an individual is willing to tolerate. However, there may be a limit in the extent to which we can generalize these estimations, in that economic models do not take into account the underlying social preferences that often guide decision makers (Fehr & Camerer, 2007; Fehr & Schmidt, 2004). This suggests that an individual's propensity for risk may be different depending on social or non-social information present within the environment (Bohnet, Greig, Herrmann & Zeckhauser, 2008). The present study aimed to: (i) assess how risk preferences may differ across social and non-social contexts; (ii) identify common and distinct neural correlates of social and non-social risk; and (iii) determine neural characteristics associated with individual sensitivities to social and non-social risk. Subjects (N=30) played an adaptation of the Trust Game while their blood-oxygen-level-dependent response was monitored using functional magnetic resonance imaging. Differences in risk preferences across social and non-social conditions as well as neuroimaging correlates of social and non-social risk will be discussed.
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    Behavioral and Neural Substrates of Decision-Making Under Perceptual and Reward Uncertainty: The Role of Task Structure
    Ghane-Ezabadi, Merage (Virginia Tech, 2022-01-18)
    Real world decision-making requires simultaneously determining what we are observing in our environment (perceptual decision-making; PDM) and what the stimuli and actions are worth (reward-based decision-making; RDM). There is evidence of a bi-directional relationship between reward and perceptual information in guiding choice, with some studies suggesting that individuals optimally combine the two. Uncertainty in both reward expectations and perception have been shown to alter choice behavior, however few studies have manipulated both variables simultaneously. Given the distinct theoretical and computational foundations of PDM and RDM, it has also been assumed that the underlying behavioral and neural substrates of perceptual and reward-based choice are separable. However, there is evidence that task structure and subjective value/uncertainty more generally contribute to activity in large-scale networks of the brain, rather than domain specific features (perceptual salience/reward). Variability in task structures and methods of manipulating and modeling sensory and reward uncertainty, make it hard to draw definitive conclusions across these perspectives with currently available data. The current study used behavioral and fMRI techniques to investigate the neurobehavioral substrates of decision-making under simultaneous perceptual and reward uncertainty in a sample of healthy adult volunteers. The primary objectives of this project were to test: a) how simultaneous manipulations in sensory and reward uncertainty influence choice, b) whether task structure alters the influence of sensory and reward information on choice behavior, and c) whether activity in underlying neural substrates reflect domain-specific or domain-general processes. Results showed that choices were best predicted by a combined model of perceptual salience and reward, with an overall bias towards perceptual salience information. Choice percentage was not impacted by task structure, however choices were better predicted by individual features (salience and reward) when they were manipulated stably, than dynamically. Activity in the brain showed greater overlap between dynamic task conditions when compared to both salience and reward conditions. There was also greater overlap between stable task conditions when compared to reward but not salience conditions. Preliminary evidence suggests that activity in decision-relevant regions of the brain varied by uncertainty and value rather than salience and reward per se.
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    Behavioral Training of Reward Learning Increases Reinforcement Learning Parameters and Decreases Depression Symptoms Across Repeated Sessions
    Goyal, Shivani (Virginia Tech, 2023-12)
    Background: Disrupted reward learning has been suggested to contribute to the etiology and maintenance of depression. If deficits in reward learning are core to depression, we would expect that improving reward learning would decrease depression symptoms across time. Whereas previous studies have shown that changing reward learning can be done in a single study session, effecting clinically meaningful change in learning requires this change to endure beyond task completion and transfer to real world environments. With a longitudinal design, we investigate the potential for repeated sessions of behavioral training to create change in reward learning and decrease depression symptoms across time. Methods: 929 online participants (497 depression-present; 432 depression-absent) recruited from Amazon’s Mechanical Turk platform completed a behavioral training paradigm and clinical selfreport measures for up to eight total study visits. Participants were randomly assigned to one of 12 arms of the behavioral training paradigm, in which they completed a probabilistic reward learning task interspersed with queries about a feature of the task environment (11 learning arms) or a control query (1 control arm). Learning queries trained participants on one of four computational-based learning targets known to affect reinforcement learning (probability, average or extreme outcome values, and value comparison processes). A reinforcement learning model previously shown to distinguish depression related differences in learning was fit to behavioral responses using hierarchical Bayesian estimation to provide estimates of reward sensitivity and learning rate for each participant on each visit. Reward sensitivity captured participants’ value dissociation between high versus low outcome values, while learning rate informed how much participants learned from previously experienced outcomes. Mixed linear models assessed relationships between model-agnostic task performance, computational model-derived reinforcement learning parameters, depression symptoms, and study progression. Results: Across time, learning queries increased individuals’ reward sensitivities in depression-absent participants (β = 0.036, p =< 0.001, 95% CI (0.022, 0.049)). In contrast, control queries did not change reward sensitivities in depression-absent participants across time ((β = 0.016, p = 0.303, 95% CI (-0.015, 0.048)). Learning rates were not affected across time for participants receiving learning queries (β = 0.001, p = 0.418, 95% CI (-0.002, 0.004)) or control queries (β = 0.002, p = 0.558, 95% CI (-0.005, 0.009). Of the learning queries, those targeting value comparison processes improved depression symptoms (β = -0.509, p = 0.015, 95% CI (-0.912, - 0.106)) and increased reward sensitivities across time (β = 0.052, p =< 0.001, 95% CI (0.030, 0.075)) in depression-present participants. Increased reward sensitivities related to decreased depression symptoms across time in these participants (β = -2.905, p = 0.002, 95% CI (-4.75, - 1.114)). Conclusions: Multiple sessions of targeted behavioral training improved reward learning for participants with a range of depression symptoms. Improved behavioral reward learning was associated with improved clinical symptoms with time, possibly because learning transferred to real world scenarios. These results support disrupted reward learning as a mechanism contributing to the etiology and maintenance of depression and suggest the potential of repeated behavioral training to target deficits in reward learning.
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    The Betrayal Aversion Elicitation Task: An Individual Level Betrayal Aversion Measure
    Aimone, Jason A.; Ball, Sheryl B.; Casas, Brooks (PLOS, 2015-09-02)
    Research on betrayal aversion shows that individuals’ response to risk depends not only on probabilities and payoffs, but also on whether the risk includes a betrayal of trust. While previous studies focus on measuring aggregate levels of betrayal aversion, the connection between an individual’s own betrayal aversion and other individually varying factors, including risk preferences, are currently unexplored. This paper develops a new task to elicit an individual’s level of betrayal aversion that can then be compared to individual characteristics. We demonstrate the feasibility of our new task and show that our aggregate individual results are consistent with previous studies. We then use this classification to ask whether betrayal aversion is correlated with risk aversion. While we find risk aversion and betrayal aversion have no significant relationship, we do observe that risk aversion is correlated with non-social risk preferences, but not the social, betrayal related, risk component of the new task.
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    Brain Similarity as a Protective Factor in the Longitudinal Pathway Linking Household Chaos, Parenting, and Substance Use
    Kim-Spoon, Jungmeen; Lee, Tae-Ho; Clinchard, Claudia; Lindenmuth, Morgan; Brieant, Alexis; Steinberg, Laurence; Deater-Deckard, Kirby; Casas, Brooks (Elsevier, 2023-04-29)
    Background: Socioecological factors such as family environment and parenting behaviors contribute to the development of substance use. While biobehavioral synchrony has been suggested as the foundation for resilience that can modulate environmental effects on development, the role of brain similarity that attenuates deleterious effects of environmental contexts has not been clearly understood. We tested whether parent-adolescent neural similarity—the level of pattern similarity between parent-adolescent functional brain connectivity representing the level of attunement within each dyad—moderates the longitudinal pathways in which household chaos (a stressor) predicts adolescent substance use directly and indirectly via parental monitoring. Methods: In a sample of 70 parent-adolescent dyads, similarity in resting-state brain activity was identified using multipattern connectivity similarity estimation. Adolescents and parents reported on household chaos and parental monitoring, and adolescent substance use was assessed at a 1-year follow-up. Results: The moderated mediation model indicated that for adolescents with low neural similarity, but not high neural similarity, greater household chaos predicted higher substance use over time directly and indirectly via lower parental monitoring. Our data also indicated differential susceptibility in the overall association between household chaos and substance use: Adolescents with low neural similarity exhibited high substance use under high household chaos but low substance use under low household chaos. Conclusions: Neural similarity acts as a protective factor such that the detrimental effects of suboptimal family environment and parenting behaviors on the development of adolescent health risk behaviors may be attenuated by neural similarity within parent-adolescent bonds.
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    Cocaine Use Modulates Neural Prediction Error During Aversive Learning
    Wang, John Mujia (Virginia Tech, 2015-05-07)
    Cocaine use has contributed to 5 million individuals falling into the cycle of addiction. Prior research in cocaine dependence mainly focused on rewards. Losses also play a critical role in cocaine dependence as dependent individuals fail to avoid social, health, and economic losses even when they acknowledge them. However, dependent individuals are extremely adept at escaping negative states like withdrawal. To further understand whether cocaine use may contribute to dysfunctions in aversive learning, this paper uses fMRI and an aversive learning task to examine cocaine dependent individuals abstinent from cocaine use (C-) and using as usual (C+). Specifically of interest is the neural signal representing actual loss compared to the expected loss, better known as prediction error (δ), which individuals use to update future expectations. When abstinent (C-), dependent individuals exhibited higher positive prediction error (δ+) signal in their striatum than when they were using as usual. Furthermore, their striatal δ+ signal enhancements from drug abstinence were predicted by higher positive learning rate (α+) enhancements. However, no relationships were found between drug abstinence enhancements to negative learning rates (α±-) and negative prediction error (δ-) striatal signals. Abstinent (C-) individuals' striatal δ+ signal was predicted by longer drug use history, signifying possible relief learning adaptations with time. Lastly, craving measures, especially the desire to use cocaine and positive effects of cocaine, also positively correlated with C- individuals' striatal δ+ signal. This suggests possible relief learning adaptations in response to higher craving and withdrawal symptoms. Taken together, enhanced striatal δ+ signal when abstinent and adaptations in relief learning provide evidence in supporting dependent individuals' lack of aversive learning ability while using as usual and enhanced relief learning ability for the purpose of avoiding negative situations such as withdrawal, suggesting a neurocomputational mechanism that pushes the dependent individual to maintains dependence.
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    Computational and Human Learning Models of Generalized Unsafety
    Huskey, Alisa Mae (Virginia Tech, 2020-08-20)
    The Generalized Unsafety Theory of Stress proposes that physiological markers of generalized stress impair learning of safe cues in stressful environments. Based on this model, chronic problems inhibiting physiological arousal lead to a heightened perception of threat, which involves experiencing anxiety symptoms without any obvious precipitating stressful or traumatic event. This investigation aims to determine the impact of stressor- versus context-related emotional learning on generalized unsafety, using a Pavlovian threat-conditioning paradigm. The difference in learning threatening cues ([CS+] paired with an aversive stimulus) compared to safety cues ([CS-] not paired with an aversive stimulus) was used as a proxy measure of generalized unsafety, as conceptualized by the GUTS model. This difference is expected to be moderated by individual differences in tonic cardiac regulation (i.e. heart rate variability). Lastly, a temporal-differences learning model was used to predict skin-conductance learning during stressor, stressor context and general contexts to determine which best predicts Pavlovian learning. TD learning is expected to better predict skin-conductance in individuals with higher fear inhibition in comparison to those with low fear inhibition.
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    The Context-Dependence of the Process of Risky Choice
    Alsharawy, Abdelaziz Mohammed (Virginia Tech, 2021-08-17)
    The evaluation of risk is a fundamental aspect of decision-making and influences important outcomes, such as in the domain of financial and health behavior. In many economic applications, risk attitudes are assumed to be inherently stable. Nonetheless, behavioral tasks that elicit risk preferences have shown temporal inconsistencies. The instability of risk preferences can be attributed to several factors such as the way information is presented (framing effects), personal past experiences, and experienced emotions. We conduct four studies in this dissertation to shed light on the state dependency of risk attitudes and on the decision process of risky choice. Chapter 2 examines, using a laboratory experiment, how high stakes in risky choices influence physiological arousal, as measured via skin conductance, pulse rate and pupil size, and attention, as measured via gaze bias and saccades. We link the changes in arousal and attention accompanying high stakes to changes in risk aversion. Moreover, we develop and test a Sequential Sampling Model (SSM), the arousal-modulated Attentional Drift Diffusion Model (aADDM), linking reaction time and choice while allowing attention and its interaction with arousal to modulate the evaluation process of risky alternatives. High stakes caused changes in attention toward the safe option's attributes, heightened physiological arousal, and increased risk aversion. Results from the aADDM, demonstrate that the values of the high attributes are discounted when participants attend to the low attributes, with arousal amplifying this process further. Chapter 3, using a laboratory experiment, investigates how incentives and emotional experiences influence the adaptation process across high and low volatility contexts in risky choice. Due to the brain's computational capacity limitations, perception is optimized to detect differences within a narrow range of stimuli. We show that this adaptation process is itself context-dependent, with stronger incentives, heightened arousal, or more unpleasant feelings increasing payoff responsivity under high volatility. Chapter 4, using survey data, focuses on fear responses during the COVID-19 pandemic and risk perception of the health- and financial-related consequences of the crisis. We show that women report higher fear of the COVID-19 pandemic compared to men, modulating the gender differences in preventative health behaviors. Women also perceive the health risks of COVID-19, and not financial risks, to be greater than men. Chapter 5, using vignette experiments, demonstrates that betrayal aversion, or hesitancy regarding the risk of being betrayed in an environment involving trust, is an important preference construct in the decision to become vaccinated and is not accounted for by widely used vaccine hesitancy measures. We show that people are significantly less willing to get vaccinated when the associated risk involved the vaccine actively contributing to the cause of death. We also find that betrayal aversion is amplified with an active role of government or scientists. Moreover, we test an exogenous intervention that increases willingness to vaccinate without mitigating betrayal aversion. JEL codes: D81, D83, D87, D91, I12, J16
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    The Development and Validation of a Neural Model of Affective States
    McCurry, Katherine Lorraine (Virginia Tech, 2015-09-23)
    Emotion dysregulation plays a central role in psychopathology (B. Bradley et al., 2011) and has been linked to aberrant activation of neural circuitry involved in emotion regulation (Beauregard, Paquette, & Lévesque, 2006; Etkin & Schatzberg, 2011). In recent years, technological advances in neuroimaging methods coupled with developments in machine learning have allowed for the non-invasive measurement and prediction of brain states in real-time, which can be used to provide feedback to facilitate regulation of brain states (LaConte, 2011). Real-time functional magnetic resonance imaging (rt-fMRI)-guided neurofeedback, has promise as a novel therapeutic method in which individuals are provided with tailored feedback to improve regulation of emotional responses (Stoeckel et al., 2014). However, effective use of this technology for such purposes likely entails the development of (a) a normative model of emotion processing to provide feedback for individuals with emotion processing difficulties; and (b) best practices concerning how these types of group models are designed and translated for use in a rt-fMRI environment (Ruiz, Buyukturkoglu, Rana, Birbaumer, & Sitaram, 2014). To this end, the present study utilized fMRI data from a standard emotion elicitation paradigm to examine the impact of several design decisions made during the development of a whole-brain model of affective processing. Using support vector machine (SVM) learning, we developed a group model that reliably classified brain states associated with passive viewing of positive, negative, and neutral images. After validating the group whole-brain model, we adapted this model for use in an rt-fMRI experiment, and using a second imaging dataset along with our group model, we simulated rt-fMRI predictions and tested options for providing feedback.
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    Development of cognitive control during adolescence: The integrative effects of family socioeconomic status and parenting behaviors
    Li, Mengjiao; Lindenmuth, Morgan; Tarnai, Kathryn; Lee, Jacob; Casas, Brooks; Kim-Spoon, Jungmeen; Deater-Deckard, Kirby (Elsevier, 2022-10)
    Cognitive control is of great interest to researchers and practitioners. The concurrent association between family socioeconomic status (SES) and adolescent cognitive control is well-documented. However, little is known about whether and how SES relates to individual differences in the development of adolescent cognitive control. The current four-year longitudinal investigation (N = 167, 13-14 years at Wave 1) used multi-source interference task performance (reaction time in interference correct trials minus neutral correct trials) and corresponding neural activities (blood oxygen level dependent contrast of interference versus neutral conditions) as measures of cognitive control. SES and parenting behaviors (warmth, monitoring) were measured through surveys. We examined direct and indirect effects of earlier SES on the development of cognitive control via parenting behaviors; the moderating effect of parenting also was explored. Results of latent growth modeling (LGM) revealed significant interactive effects between SES and parenting predicting behavioral and neural measures of cognitive control. Lower family SES was associated with poorer cognitive performance when coupled with low parental warmth. In contrast, higher family SES was associated with greater improvement in performance, as well as a higher intercept and steeper decrease in frontoparietal activation over time, when coupled with high parental monitoring. These findings extend prior cross-sectional evidence to show the moderating effect of the parenting environment on the potential effects of SES on developmental changes in adolescent cognitive control.
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    A Developmental Cascade Model of Maltreatment, Delay Discounting, and Health Behaviors across Adolescence and Young Adulthood
    Peviani, Kristin Marie (Virginia Tech, 2022-06-15)
    Maltreatment is a pervasive global problem known to have cascading consequences that persist long after exposure subsides (Masten and Cicchetti, 2010). Maltreatment is often co-occurring, involving exposure to multiple types. Cumulative maltreatment, or exposure to multiple types of neglect and abuse, is proposed to be of critical importance for developmental psychopathology. However, a cumulative approach to studying maltreatment provides little insight into the developmental processes whereby it exerts its effects on health. Thus, we employed both a cumulative approach and a multidimensional approach to facilitate our comprehensive understanding of maltreatment experiences related to behavioral development. Given the high prevalence of maltreatment, it is important to cultivate a greater understanding of the processes linking maltreatment and health and to identify developmental periods of vulnerability to its deleterious effects. The present study uses a longitudinal design and a multidimensional approach to examine the effects of maltreatment on delay discounting and health-promoting and health-demoting behaviors during adolescence and across the transition from adolescence to young adulthood. The study sample includes 167 adolescents (aged 13–14 at Time 1; 53% male) who participated across 5 time points over 6 years. At Time 5, adolescents provided retrospective reports of their exposure to maltreatment during adolescence across ages 13–18. Delay discounting, substance use, and body mass index (BMI) were assessed at each time point. We used a developmental cascade model with autoregressive, cross-lagged, and cross-sectional associations to examine the longitudinal multivariate change processes and indirect effects from maltreatment exposure during adolescence to delay discounting and health-promoting and health-demoting behaviors during adolescence and across the transition to young adulthood. Our results indicate that cumulative maltreatment affects health-demoting behavior via its effects on delay discounting and that maltreatment of omission but not commission drives this effect. Furthermore, the findings identify adolescents exposed to maltreatment of omission as being especially vulnerable to marijuana use via elevated delay discounting. Identifying mediating processes linking maltreatment exposure to health-promoting and health-demoting behaviors may be instrumental for preventing deleterious developmental cascades and interrupting related health problems during adolescence and across the transition from adolescence to young adulthood.