The influence of emotions on cognitive control: feelings and beliefs—where do they meet?

The influence of emotion on higher-order cognitive functions, such as attention allocation, planning, and decision-making, is a growing area of research with important clinical applications. In this review, we provide a computational framework to conceptualize emotional influences on inhibitory control, an important building block of executive functioning. We first summarize current neurocognitive models of inhibitory control and show how Bayesian ideal observer models can help reframe inhibitory control as a dynamic decision-making process. Finally, we propose a Bayesian framework to study emotional influences on inhibitory control, providing several hypotheses that may be useful to conceptualize inhibitory control biases in mental illness such as depression and anxiety. To do so, we consider the neurocognitive literature pertaining to how affective states can bias inhibitory control, with particular attention to how valence and arousal may independently impact inhibitory control by biasing probabilistic representations of information (i.e., beliefs) and valuation processes (e.g., speed-error tradeoffs)

Bayesian computational markers of relapse in methamphetamine dependence

Methamphetamine use disorder is associated with a high likelihood of relapse. Identifying robust predictors of relapse that have explanatory power is critical to develop secondary prevention based on a mechanistic understanding of relapse. Computational approaches have the potential to identify such predictive markers of psychiatric illness, with the advantage of providing a finer mechanistic explanation of the cognitive processes underlying psychiatric vulnerability.In this study, sixty-two recently sober methamphetamine-dependent individuals were recruited from a 28-day inpatient treatment program, and completed a Stop Signal Task (SST) while undergoing functional magnetic resonance imaging (fMRI). These individuals were prospectively followed for 1 year and assessed for relapse to methamphetamine use. Thirty-three percent of followed participants reported relapse.We found that neural activity associated with two types of Bayesian prediction error, i.e. the difference between actual and expected need to stop on a given trial, significantly differentiated those individuals who remained abstinent and those who relapsed. Specifically, relapsed individuals exhibited smaller neural activations to such Bayesian prediction errors relative to those individuals who remained abstinent in the left temporoparietal junction (Cohen's d = 0.91), the left inferior frontal gyrus (Cohen's d = 0.57), and left anterior insula (Cohen's d = 0.63). In contrast, abstinent and relapsed participants did not differ in neural activation to non-model based task contrasts or on various self-report clinical measures.In conclusion, Bayesian cognitive models may help identify predictive biomarkers of relapse, while providing a computational explanation of belief processing and updating deficits in individuals with methamphetamine use disorder. Keywords: Methamphetamine dependence, Relapse, Bayesian model, Inhibitory control, Stimulan

Data from: The influence of depression on cognitive control: disambiguating approach and avoidance tendencies

Dysfunctions of approach and avoidance motivation play an important role in depression, which in turn may affect cognitive control, i.e., the ability to regulate thoughts and action to achieve internal goals. We use a novel experimental paradigm, i.e. a computer simulated driving-task, to study the impact of depression on cognitive control by measuring approach and avoidance actions in continuous time. In this task, 39 subjects with minimal to severe depression symptoms were instructed to use a joystick to move a virtual car as quickly as possible to a target point without crossing a stop-sign or crashing into a wall. We recorded their continuous actions on a joystick and found that depression 1) leads to further stopping distance to task target; and 2) increases the magnitude of late deceleration (avoidance) but not early acceleration (approach), which was only observed in the stop-sign condition. Taken together, these results are consistent with the hypothesis that depressed individuals have greater avoidance motivation near stopping target, but are minimally affected by approach motivation