The interaction between emotion and economic decision-making

Emotions play an important role in daily life decisions. For example, we are likely to choose, judge, or evaluate things around us in different ways depending on whether we are feeling sad, anxious, or happy. Emotional reactions to life events and outcomes, such as winning an award, or getting a divorce, should also predict individuals’ subsequent decisions. However, the mechanisms by which such interactions between emotions and decisions unfold are still poorly understood. The aim of this thesis was two-fold: first, to characterize a computational model of how emotions are integrated into decisions; second, to provide a better understanding of the cognitive and neural mechanisms by which manipulating emotions can alter decisions. Following the general introduction and methods description, the first experimental chapter shows that integrating emotions (self-report feelings) in a computational model of decision-making could reliably predict people’s gambling choices, indicating a unique contribution of feelings to decisions. The second experimental chapter explores the influence of incidental emotional priming on gambling choice and the underlying neural mechanisms, using functional magnetic resonance imaging (fMRI). The findings suggest that how external emotions impact decisions, at both behavioural and neural levels, varied with individual differences in levels of trait anxiety. The third experimental chapter attempts to extend this finding by testing how risky decisions are altered in patients with clinical anxiety, relative to healthy controls; demonstrating a dissociation between sensitivity to risk, which was enhanced in anxiety, and sensitivity to monetary losses, which was not associated with anxiety. This thesis provides a more complete understanding of the complex interactions between emotions, mood and decision-making. In the final chapter the findings are discussed in light of influential theories in cognitive neuroscience and behavioural economics that posit a central role for emotions in determining the choices we make

Anxiety promotes memory for mood-congruent faces but does not alter loss aversion

Pathological anxiety is associated with disrupted cognitive processing, including working memory and decision-making. In healthy individuals, experimentally-induced state anxiety or high trait anxiety often results in the deployment of adaptive harm-avoidant behaviours. However, how these processes affect cognition is largely unknown. To investigate this question, we implemented a translational within-subjects anxiety induction, threat of shock, in healthy participants reporting a wide range of trait anxiety scores. Participants completed a gambling task, embedded within an emotional working memory task, with some blocks under unpredictable threat and others safe from shock. Relative to the safe condition, threat of shock improved recall of threat-congruent (fearful) face location, especially in highly trait anxious participants. This suggests that threat boosts working memory for mood-congruent stimuli in vulnerable individuals, mirroring memory biases in clinical anxiety. By contrast, Bayesian analysis indicated that gambling decisions were better explained by models that did not include threat or treat anxiety, suggesting that: (i) higher-level executive functions are robust to these anxiety manipulations; and (ii) decreased risk-taking may be specific to pathological anxiety. These findings provide insight into the complex interactions between trait anxiety, acute state anxiety and cognition, and may help understand the cognitive mechanisms underlying adaptive anxiety

Enhanced Risk Aversion, But Not Loss Aversion, in Unmedicated Pathological Anxiety.

BACKGROUND: Anxiety disorders are associated with disruptions in both emotional processing and decision making. As a result, anxious individuals often make decisions that favor harm avoidance. However, this bias could be driven by enhanced aversion to uncertainty about the decision outcome (e.g., risk) or aversion to negative outcomes (e.g., loss). Distinguishing between these possibilities may provide a better cognitive understanding of anxiety disorders and hence inform treatment strategies. METHODS: To address this question, unmedicated individuals with pathological anxiety (n = 25) and matched healthy control subjects (n = 23) completed a gambling task featuring a decision between a gamble and a safe (certain) option on every trial. Choices on one type of gamble-involving weighing a potential win against a potential loss (mixed)-could be driven by both loss and risk aversion, whereas choices on the other type-featuring only wins (gain only)-were exclusively driven by risk aversion. By fitting a computational prospect theory model to participants' choices, we were able to reliably estimate risk and loss aversion and their respective contribution to gambling decisions. RESULTS: Relative to healthy control subjects, pathologically anxious participants exhibited enhanced risk aversion but equivalent levels of loss aversion. CONCLUSIONS: Individuals with pathological anxiety demonstrate clear avoidance biases in their decision making. These findings suggest that this may be driven by a reduced propensity to take risks rather than a stronger aversion to losses. This important clarification suggests that psychological interventions for anxiety should focus on reducing risk sensitivity rather than reducing sensitivity to negative outcomes per se

Emotion-induced loss aversion and striatal-amygdala coupling in low-anxious individuals

Adapting behavior to changes in the environment is a crucial ability for survival but such adaptation varies widely across individuals. Here, we asked how humans alter their economic decision-making in response to emotional cues, and whether this is related to trait anxiety. Developing an emotional decision-making task for functional magnetic resonance imaging, in which gambling decisions were preceded by emotional and non-emotional primes, we assessed emotional influences on loss aversion, the tendency to overweigh potential monetary losses relative to gains. Our behavioral results revealed that only low-anxious individuals exhibited increased loss aversion under emotional conditions. This emotional modulation of decision-making was accompanied by a corresponding emotion-elicited increase in amygdala-striatal functional connectivity, which correlated with the behavioral effect across participants. Consistent with prior reports of 'neural loss aversion', both amygdala and ventral striatum tracked losses more strongly than gains, and amygdala loss aversion signals were exaggerated by emotion, suggesting a potential role for this structure in integrating value and emotion cues. Increased loss aversion and striatal-amygdala coupling induced by emotional cues may reflect the engagement of adaptive harm-avoidance mechanisms in low-anxious individuals, possibly promoting resilience to psychopathology

Harnessing electric potential: DLPFC tDCS induces widespread brain perfusion changes.

A commentary on widespread modulation of cerebral perfusion induced during and after transcranial direct current stimulation applied to the left dorsolateral prefrontal corte

Unreliability of putative fMRI biomarkers during emotional face processing

There is considerable need to develop tailored approaches to psychiatric treatment. Numerous researchers have proposed using functional magnetic resonance imaging (fMRI) biomarkers to predict therapeutic response, in particular by measuring task-evoked subgenual anterior cingulate (sgACC) and amygdala activation in mood and anxiety disorders. Translating this to the clinic relies on the assumption that blood-oxygen-level dependent (BOLD) responses in these regions are stable within individuals. To test this assumption, we scanned a group of 29 volunteers twice (mean test-retest interval=14.3 days) and calculated the within-subject reliability of the amplitude of the amygdalae and sgACC BOLD responses to emotional faces using three paradigms: emotion identification; emotion matching; and gender classification. We also calculated the reliability of activation in a control region, the right fusiform face area (FFA). All three tasks elicited robust group activations in the amygdalae and sgACC (which changed little on average over scanning sessions), but within-subject reliability was surprisingly low, despite excellent reliability in the control right FFA region. Our findings demonstrate low statistical reliability of two important putative treatment biomarkers in mood and anxiety disorders

Computational Modeling of Silicate Glasses: A Quantitative Structure-Property Relationship Perspective

This article reviews the present state of Quantitative Structure-Property Relationships (QSPR) in glass design and gives an outlook into future developments. First an overview is given of the statistical methodology, with particular emphasis to the integration of QSPR with molecular dynamics simulations to derive informative structural descriptors. Then, the potentiality of this approach as a tool for interpretative and predictive purposes is highlighted by a number of recent inspiring applications