Emotion and decision-making: affect-driven belief systems in anxiety and depression

The truth is in there: Belief processes in the human brain

Belief, defined by William James as the mental state or function of cognizing reality, is a core psychological function with strong influence on emotion and behavior. Furthermore, strong and aberrant beliefs about the world and oneself play important roles in mental disorders. The underlying processes of belief have been the matter of a long debate in philosophy and psychology, and modern neuroimaging techniques can provide insight into the underlying neural processes. Here, we conducted a functional magnetic resonance imaging study with N = 30 healthy participants in which we presented statements about facts, politics, religion, conspiracy theories, and superstition. Participants judged whether they considered them as true (belief) or not (disbelief) and reported their certainty in the decision. We found belief-associated activations in bilateral dorsolateral prefrontal cortex, left superior parietal cortex, and left lateral frontopolar cortex. Disbelief-associated activations were found in an anterior temporal cluster extending into the amygdala. We found a larger deactivation for disbelief than belief in the ventromedial prefrontal cortex that was most pronounced during decisions, suggesting a role of the vmPFC in belief-related decision-making. As a category-specific effect, we found disbelief-associated activation in retrosplenial cortex and parahippocampal gyrus for conspiracy theory statements. Exploratory analyses identified networks centered at anterior cingulate cortex for certainty, and dorsomedial prefrontal cortex for uncertainty. The uncertainty effect identifies a neural substrate for Alexander Bain's notion from 1859 of uncertainty as the real opposite of belief. Taken together, our results suggest a two-factor neural process model of belief with falsehood/veracity and uncertainty/certainty factors.

Exploring Factors Affecting Acceptance of Fecal Microbiota Transplantation for Patients with Recurrent Urinary Tract Infections: a Descriptive Qualitative Study

Purpose

Patients with recurrent urinary tract infections face complex management challenges. Fecal microbiota transplantation is a superior treatment for chronic infectious diseases, but limited patient knowledge affects treatment decisions. This study aims to identify factors associated with hesitancy towards fecal microbiota transplantation among patients with recurrent urinary tract infections, to help physicians and nurses in providing accurate and useful information to patients.

Patients and Methods

A descriptive qualitative approach was employed, utilizing semi-structured interviews conducted with patients experiencing recurrent urinary tract infections who expressed hesitancy towards fecal microbiota transplantation. The interviews took place between September 2021 and December 2022. Thematic analysis was conducted on the semi-structured interviews to identify perceived facilitators and barriers associated with fecal microbiota transplantation.

Results

The analysis included interviews with thirty adult female patients with recurrent urinary tract infections. Four facilitators influencing patients' decision-making regarding fecal microbiota transplantation were identified: (1) the motivating role of hope and expectations for active patient participation; (2) the influence of healthcare providers, as well as family members and friends on patients' decisions to pursue fecal microbiota transplantation; (3) the patients' perception of fecal microbiota transplantation as a low-risk treatment option; and (4) the dedication to the advancement of medical treatments. In contrast, two primary barriers to accepting fecal microbiota transplantation were identified: (1) that conventional treatment controls disease activity, while fecal microbiota transplantation effects remain uncertain; and (2) that safety concerns surrounding fecal microbiota transplantation.

Conclusion

Comprehensive information about fecal microbiota transplantation, including donor selection, sample processing, the procedure, and potential discomfort, is essential for patients and families to make informed treatment decisions.

Registration

CHiCTR2100048970.

The computational structure of consummatory anhedonia

Anhedonia is a reduction in enjoyment, motivation, or interest. It is common across mental health disorders and a harbinger of poor treatment outcomes. The enjoyment aspect, termed 'consummatory anhedonia', in particular poses fundamental questions about how the brain constructs rewards: what processes determine how intensely a reward is experienced? Here, we outline limitations of existing computational conceptualisations of consummatory anhedonia. We then suggest a richer reinforcement learning (RL) account of consummatory anhedonia with a reconceptualisation of subjective hedonic experience in terms of goal progress. This accounts qualitatively for the impact of stress, dysfunctional cognitions, and maladaptive beliefs on hedonic experience. The model also offers new views on the treatments for anhedonia.

Pulling the lever in a hurry: the influence of impulsivity and sensitivity to reward on moral decision-making under time pressure

BACKGROUND

Making timely moral decisions can save a life. However, literature on how moral decisions are made under time pressure reports conflicting results. Moreover, it is unclear whether and how moral choices under time pressure may be influenced by personality traits like impulsivity and sensitivity to reward and punishment.

METHODS

To address these gaps, in this study we employed a moral dilemma task, manipulating decision time between participants: one group (N = 25) was subjected to time pressure (TP), with 8 s maximum time for response (including the reading time), the other (N = 28) was left free to take all the time to respond (noTP). We measured type of choice (utilitarian vs. non-utilitarian), decision times, self-reported unpleasantness and arousal during decision-making, and participants' impulsivity and BIS-BAS sensitivity.

RESULTS

We found no group effect on the type of choice, suggesting that time pressure per se did not influence moral decisions. However, impulsivity affected the impact of time pressure, in that individuals with higher cognitive instability showed slower response times under no time constraint. In addition, higher sensitivity to reward predicted a higher proportion of utilitarian choices regardless of the time available for decision.

CONCLUSIONS

Results are discussed within the dual-process theory of moral judgement, revealing that the impact of time pressure on moral decision-making might be more complex and multifaceted than expected, potentially interacting with a specific facet of attentional impulsivity.

Characterizing major depressive disorder and substance use disorder using heatmaps and variable interactions: The utility of operant behavior and brain structure relationships

BACKGROUND

Rates of depression and addiction have risen drastically over the past decade, but the lack of integrative techniques remains a barrier to accurate diagnoses of these mental illnesses. Changes in reward/aversion behavior and corresponding brain structures have been identified in those with major depressive disorder (MDD) and cocaine-dependence polysubstance abuse disorder (CD). Assessment of statistical interactions between computational behavior and brain structure may quantitatively segregate MDD and CD.

METHODS

Here, 111 participants [40 controls (CTRL), 25 MDD, 46 CD] underwent structural brain MRI and completed an operant keypress task to produce computational judgment metrics. Three analyses were performed: (1) linear regression to evaluate groupwise (CTRL v. MDD v. CD) differences in structure-behavior associations, (2) qualitative and quantitative heatmap assessment of structure-behavior association patterns, and (3) the k-nearest neighbor machine learning approach using brain structure and keypress variable inputs to discriminate groups.

RESULTS

This study yielded three primary findings. First, CTRL, MDD, and CD participants had distinct structure-behavior linear relationships, with only 7.8% of associations overlapping between any two groups. Second, the three groups had statistically distinct slopes and qualitatively distinct association patterns. Third, a machine learning approach could discriminate between CTRL and CD, but not MDD participants.

CONCLUSIONS

These findings demonstrate that variable interactions between computational behavior and brain structure, and the patterns of these interactions, segregate MDD and CD. This work raises the hypothesis that analysis of interactions between operant tasks and structural neuroimaging might aide in the objective classification of MDD, CD and other mental health conditions.

Identifying and modulating neural signatures of stress susceptibility and resilience enables control of anhedonia

Anhedonia is a core aspect of major depressive disorder. Traditionally viewed as a blunted emotional state in which individuals are unable to experience joy, anhedonia also diminishes the drive to seek rewards and the ability to value and learn about them 1-4.The neural underpinnings of anhedonia and how this emotional state drives related behavioral changes remain unclear. Here, we investigated these questions by taking advantage of the fact that when mice are exposed to traumatic social stress, susceptible animals become socially withdrawn and anhedonic, where they cease to seek high-value rewards, while others remain resilient. By performing high density electrophysiological recordings and comparing neural activity patterns of these groups in the basolateral amygdala (BLA) and ventral CA1 (vCA1) of awake behaving animals, we identified neural signatures of susceptibility and resilience to anhedonia. When animals actively sought rewards, BLA activity in resilient mice showed stronger discrimination between upcoming reward choices. In contrast, susceptible mice displayed a rumination-like signature, where BLA neurons encoded the intention to switch or stay on a previously chosen reward. When animals were at rest, the spontaneous BLA activity of susceptible mice was higher dimensional than in controls, reflecting a greater number of distinct neural population states. Notably, this spontaneous activity allowed us to decode group identity and to infer if a mouse had a history of stress better than behavioral outcomes alone. Finally, targeted manipulation of vCA1 inputs to the BLA in susceptible mice rescued dysfunctional neural dynamics, amplified dynamics associated with resilience, and reversed their anhedonic behavior. This work reveals population-level neural signatures that explain individual differences in responses to traumatic stress, and suggests that modulating vCA1-BLA inputs can enhance resilience by regulating these dynamics.

COVID-19 Pandemic and Food Insecurity Fuel the Mental Health Crisis in Africa

Objective: Providing country-level estimates for prevalence rates of Generalized Anxiety Disorder (GAD), COVID-19 exposure and food insecurity (FI) and assessing the role of persistent threats to survival-exemplified by exposure to COVID-19 and FI-for the mental health crisis in Africa. Methods: Original phone-based survey data from Mozambique, Sierra Leone, Tanzania and Uganda (12 consecutive cross-sections in 2021; n = 23,943) were analyzed to estimate prevalence rates of GAD. Logistic regression models and mediation analysis using structural equation models identify risk and protective factors. Results: The overall prevalence of GAD in 2021 was 23.3%; 40.2% in Mozambique, 17.0% in Sierra Leone, 18.0% in Tanzania, and 19.1% in Uganda. Both COVID-19 exposure (ORadj. 1.4; CI 1.3-1.6) and FI (ORadj 3.2; CI 2.7-3.8) are independent and significant predictors of GAD. Thus, the impact of FI on GAD was considerably stronger than that of COVID-19 exposure. Conclusion: Persistent threats to survival play a substantial role for mental health, specifically GAD. High anxiety prevalence in the population requires programs to reduce violence and enhance social support. Even during a pandemic, addressing FI as a key driver of GAD should be prioritized by policymakers.

Anxiety and Depression in Metastatic Cancer: A Critical Review of Negative Impacts on Advance Care Planning and End-of-Life Decision Making With Practical Recommendations

PURPOSE

Providers treating adults with advanced cancer increasingly seek to engage patients and surrogates in advance care planning (ACP) and end-of-life (EOL) decision making; however, anxiety and depression may interfere with engagement. The intersection of these two key phenomena is examined among patients with metastatic cancer and their surrogates: the need to prepare for and engage in ACP and EOL decision making and the high prevalence of anxiety and depression.

METHODS

Using a critical review framework, we examine the specific ways that anxiety and depression are likely to affect both ACP and EOL decision making.

RESULTS

The review indicates that depression is associated with reduced compliance with treatment recommendations, and high anxiety may result in avoidance of difficult discussions involved in ACP and EOL decision making. Depression and anxiety are associated with increased decisional regret in the context of cancer treatment decision making, as well as a preference for passive (not active) decision making in an intensive care unit setting. Anxiety about death in patients with advanced cancer is associated with lower rates of completion of an advance directive or discussion of EOL wishes with the oncologist. Patients with advanced cancer and elevated anxiety report higher discordance between wanted versus received life-sustaining treatments, less trust in their physicians, and less comprehension of the information communicated by their physicians.

CONCLUSION

Anxiety and depression are commonly elevated among adults with advanced cancer and health care surrogates, and can result in less engagement and satisfaction with ACP, cancer treatment, and EOL decisions. We offer practical strategies and sample scripts for oncology care providers to use to reduce the effects of anxiety and depression in these contexts.

Decision making and its correlates in recurrent depressive disorder

Objective

Decision-making (DM) is simply choosing among alternatives or defining one's course of action. A depressed individual does not perceive himself as a decision-maker as ruminations reinforce dysfunctional metacognitive beliefs and poor executive functioning. The aim was to study and compare the relationship among DM, metacognition, and executive functioning in those with recurrent depressive disorder (RDD) and in healthy controls (HCs).

Methods

A cross-sectional comparative group study design was used with a sample size of 40, with 20 participants in each group. The tools used were Mini International Neuropsychiatric Interview, General Health Questionnaire, Melbourne Decision Making Questionnaire, Metacognitive Questionnaire, Wisconsin Card Sorting Test, and Controlled Oral Word Association Test.

Results

The RDD group had significantly higher scores on buck-passing (BP), procrastination (PR), hypervigilance, and dysfunctional metacognitive beliefs, and poor performance on executive functioning than HC. PR was inversely correlated with executive functioning and dysfunctional metacognitive beliefs in the RDD group, whereas in the HC group, BP was positively correlated with executive functioning and dysfunctional metacognitive beliefs.

Conclusion

DM has a significant relationship with executive functions and dysfunctional metacognitive beliefs; therefore, changes in any one variable contribute to changes in the other two. The altered attentional and executive control due to dysfunctional metacognitive beliefs leads to poor DM, resulting in psychosocial dysfunction. The underlying metacognitive beliefs and executive functioning play a crucial role in DM, the process determining psychosocial functioning.

Relationship between caregiver adverse childhood events and age of autism spectrum diagnosis

BACKGROUND

The age at which children are diagnosed with autism spectrum disorder (ASD) has not significantly decreased in the past 20 years. Adverse childhood events (ACEs) experienced by caregivers of autistic children may predict delays in caregivers attending ASD diagnostic evaluations, negatively impacting the age at which the child is diagnosed with ASD. The purpose of this study was to further explore the caregiver delay response by analyzing recurring events common in caregivers of children at risk for ASD.

METHODS

We used a quantitative research design with convenience sampling to categorize caregivers of children referred for an ASD diagnostic evaluation into three groups based on their ACEs score and medical history.

FINDINGS

A higher percentage of caregivers with four or more ACEs compared to the national average was noted. Parental separation or divorce, verbal aggression, emotional abuse, and parental alcohol or substance abuse occurred most frequently, and the latter predicted a prolonged time in attending the diagnostic evaluation.

DISCUSSION

ACEs experienced by caregivers of children with ASD may delay the age of ASD diagnosis. Further investigation into the effect of ACEs on caregivers' mental health status and executive functioning is warranted to develop best practice for assisting caregivers in ASD recognition, diagnosis, and care management.

APPLICATION TO PRACTICE

Clinicians should consider caregiver ACEs score when referring a child for a diagnostic developmental evaluation. Resources to address caregiver mental health needs should be provided at the onset of the referral process to increase the likelihood of caregiver adherence.

Neural signatures of stress susceptibility and resilience in the amygdala-hippocampal network

The neural dynamics that underlie divergent anhedonic responses to stress remain unclear. Here, we identified neuronal dynamics in an amygdala-hippocampal circuit that distinguish stress resilience and susceptibility. In a reward-choice task, basolateral amygdala (BLA) activity in resilient mice showed enhanced discrimination of upcoming reward choices. In contrast, a rumination-like signature emerged in the BLA of susceptible mice; a linear decoder could classify the intention to switch or stay on a previously chosen reward. Spontaneous activity in the BLA of susceptible mice was higher dimensional than controls, reflecting the exploration of a larger number of distinct neural states. Manipulation of vCA1-BLA inputs rescued dysfunctional neural dynamics and anhedonia in susceptible mice, suggesting that targeting this pathway can enhance BLA circuit function and ameliorate of depression-related behaviors.

Explaining reversal learning deficits in anxiety with electrophysiological evidence

Reversal learning is a crucial aspect of behavioral flexibility that plays a significant role in environmental adaptation and development. While previous studies have established a link between anxiety and impaired reversal learning ability, the underlying mechanisms behind this association remain unclear. This study employed a probabilistic reversal learning task with electroencephalographic recording to investigate these mechanisms. Participants were divided into two groups based on their scores on Spielberger's State-Trait Anxiety Inventory: high trait-anxiety (HTA) and low trait-anxiety (LTA), consisting of 50 individuals in each group. The results showed that the HTA group had poorer reversal learning performance than the LTA group, including a lower tendency to shift to the new optimal option after rule reversals (reversal-shift). The study also examined event-related potentials elicited by reversals and found that although the N1 (related to attention allocation), feedback-related negativity (FRN: related to belief updating), and P3 (related to response inhibition) were all sensitive to the grouping factor, only the FRN elicited by reversal-shift mediated the relationship between anxiety and the number/reaction time of reversal-shift. From these findings, we suggest that abnormalities in belief updating may contribute to the impaired reversal learning performance observed in anxious individuals. In our opinion, this study sheds light on potential targets for interventions aimed at improving behavioral flexibility in anxious individuals.

A Comparison of Social Exclusion Towards People with Depression or Chronic Back Pain

Objectives

Research comparing mental and physical health stigma is scarce. The aim of this study was to compare social exclusion towards hypothetical males and females with depression or chronic back pain. Furthermore, the study investigated whether social exclusion is associated with participant's empathy and personality traits, while controlling for their sex, age and personal exposure to mental/physical chronic health conditions.

Design

This study employed a cross-sectional questionnaire design.

Methods

Participants (N = 253) completed an online vignette-based questionnaire and were randomly allocated to either a depression or chronic back pain study condition. Measures of social exclusion through respondents' willingness to interact with hypothetical individuals, empathy and the Big Five personality traits were completed.

Results

Willingness to interact scores did not significantly differ depending on the diagnosis or sex of the hypothetical person in the vignette. For depression, higher levels of conscientiousness significantly predicted less willingness to interact. Whilst being a female participant and having higher empathy significantly predicted greater willingness to interact. For chronic back pain, higher empathy significantly predicted greater willingness to interact, with no significant predictors found from the Big Five personality traits.

Conclusion

Findings indicate that females and males with depression or chronic back pain face similar levels of social exclusion, with empathy being a core variable driving social exclusion behaviours. These findings enhance our understanding of potential variables driving social exclusion, in-turn informing campaign development to reduce public stigma towards depression and chronic back pain.

Decision-making under ambiguity after frontal lobe resection for epilepsy

PURPOSE

Decision-making is crucial to daily life and can impact our society as well as economic conditions. Although the frontal lobes have been identified as important for decision-making, this capacity has only been studied to a limited extent in frontal lobe epilepsy and not at all after frontal lobe resection (FLR) for epilepsy. This study aimed to explore decision-making under ambiguity after FLR for epilepsy.

METHODS

Fourteen patients having undergone FLR for epilepsy completed the Iowa Gambling Task (IGT) which is a widely used tool to measure decision-making under ambiguity. Iowa Gambling Task scores included in the analysis were: total net score, separate scores from five blocks across the test, and a change score (last block of IGT minus first block). A group of healthy controls (n = 30) was used as a comparison. Associations between IGT and standardized neuropsychological methods for assessment of executive functions, self-rating questionnaires of mental health, fatigue, and behavior linked to frontal lobe dysfunction were also investigated.

RESULTS

The patient group performed inferior to controls at the final block of the IGT (p =.001).A group difference in IGT change scores was found (p =.005), reflectingthe absence of a positive change in performance over time for the FLR group compared to the control group. Correlations with tests of executive functions as well as self-rating scales were mainly statistically nonsignificant.

CONCLUSIONS

This study shows that patients having undergone FLR for epilepsy have difficulties with decision-making under ambiguity. The performance illustrated a failure to learn throughout the task. Executive as well as emotional deficits may impact decision-making processes in this patient group and need to be considered in further studies. Prospective studies with larger cohorts are needed.

Neural valuation of rewards and punishments in posttraumatic stress disorder: a computational approach

Posttraumatic stress disorder (PTSD) is associated with changes in fear learning and decision-making, suggesting involvement of the brain's valuation system. Here we investigate the neural mechanisms of subjective valuation of rewards and punishments in combat veterans. In a functional MRI study, male combat veterans with a wide range of posttrauma symptoms (N = 48, Clinician Administered PTSD Scale, CAPS-IV) made a series of choices between sure and uncertain monetary gains and losses. Activity in the ventromedial prefrontal cortex (vmPFC) during valuation of uncertain options was associated with PTSD symptoms, an effect which was consistent for gains and losses, and specifically driven by numbing symptoms. In an exploratory analysis, computational modeling of choice behavior was used to estimate the subjective value of each option. The neural encoding of subjective value varied as a function of symptoms. Most notably, veterans with PTSD exhibited enhanced representations of the saliency of gains and losses in the neural valuation system, especially in ventral striatum. These results suggest a link between the valuation system and the development and maintenance of PTSD, and demonstrate the significance of studying reward and punishment processing within subject.

Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans

Anxiety has been linked to altered belief formation and uncertainty estimation, impacting learning. Identifying the neural processes underlying these changes is important for understanding brain pathology. Here, we show that oscillatory activity in the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC) explains anxiety-related learning alterations. In a magnetoencephalography experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a probabilistic reward-based learning task. HTA undermined learning through an overestimation of volatility, leading to faster belief updating, more stochastic decisions and pronounced lose-shift tendencies. On a neural level, we observed increased gamma activity in the ACC, dmPFC, and OFC during encoding of precision-weighted prediction errors in HTA, accompanied by suppressed ACC alpha/beta activity. Our findings support the association between altered learning and belief updating in anxiety and changes in gamma and alpha/beta activity in the ACC, dmPFC, and OFC.

Extending the use of the Belief Action Outcome model during COVID-19 pandemic: Technology access review on locational disparities and inequalities for knowledge workers

Remote working has played an increasingly important role in accelerating alternative workplaces. In the wake of the COVID-19 pandemic emergency demands, this paper seek to demonstrate the resilience of knowledge workers and their ability to work remotely, despite the uneven distribution of enabling infrastructure during the COVID-19 lockdown restrictions. The Belief Action Outcome (BAO) model for information systems was used to support the study as this underexplored theory was found to be worthy of further testing in real-world situations. This qualitative study used a range of sources consisting largely of search data from major online journal databases. The findings highlight that knowledge workers can successfully work from alternative workplaces and still deliver the required outputs, despite socio-economic problems such as locational disparities and inequalities in access to technology. The same technologies that empowered knowledge workers to transform their work locations during the COVID-19 crisis, however, are the same to enable certain sectors of society whilst hindering other cohorts residing in under resourced locations. Therefore, the benefits of working remotely cannot favour everyone because of the existing inequalities and disparities. Applying the BAO model in this context implies environmental issues are likely to play a growing important role in future when decisions are made around alternative workplace and adoption of IS/IT systems. Although the COVID-19 pandemic has disrupted working patterns and accelerated the trend towards working in alternative workplaces than the traditional office/factory premises, there are notable implications around this shift. The study confirmed the related behaviours, opportunities, and barriers (social systems and organisations), as well as the structures (both societal and organisational) of the BAO model. In addition, certain aspects of both the remote workers’ and organisations’ adoption behaviour were changed to a greater extent and more rapidly because of the COVID-19 pandemic. As a contribution, this qualitative study reveals in more detail the yet uncharted remote workers' beliefs.

Cognitive, Emotional, Behavioral and Physiological Evaluation of the Relationship Between Brain and Gut Microbiota

The aim of this study is to examine the effect of gut microbiota on brain functions, mood and psychiatric disorders such as depression, anxiety and behavioral addictions, neurotransmitter levels, cognitive processes such as self-control, decision making and delayed gratification. In this context, the relevant literature was reviewed and the findings were evaluated. The relationships of the bidirectional communication between the brain-gut axis with cognitive, emotional, behavioral and physiological processes were explained with a diagram. As a result, although more research is needed on this subject, it has been observed that the brain-gut axis is bidirectionally established through neural, stress, endocrine and immune systems. In this bidirectional communication process, there are interactions in the context of cognitive, emotional, behavioral and other physiological factors. These factors both individually enter into bidirectional relationships with the brain and gut microbiota and are affected by the bidirectional communication between the brain and gut.

Baseline emotional state influences on the response to animated short films: A randomized online experiment

Introduction

Considering the relevance of the emotional state, it is necessary to understand how daily stimuli can modulate the emotions. Animated short films are common stimuli, but it is unknown how they can modulate the emotional state. The study aimed to evaluate: how participants' emotional state changed after watching animated short films with positive or negative emotional valence in an online experiment; the relationship between participants' baseline score on an Emotional Intensity Scale and their potential change in the main emotion after watching the films; and the association between the initial main emotion valence and the potential change in this emotion with participants' sociodemographic information.

Methods

A sample of 2,269 participants recruited during COVID-19 pandemic were randomly assigned to either watch a negative or positive animated short film.

Results

The results showed that, after watching a film with negative valence, participants were in a more negative emotional state than at baseline and compared with those who watched the film with positive valence. Also, individuals who had a negative baseline emotion and maintained the same emotion after the film had presented higher baseline emotional state scores (more negative emotion) than those who changed their emotions. In addition, the individuals who kept the baseline emotion had an association with age, marital status, level of education and psychiatric disorders, use of medication, and emotional awareness, while the individuals who changed the baseline emotion had an association with age, gender, and following or not social distancing recommendations.

Conclusion

Baseline emotional state may influence the response to animated short films and sociodemographic characteristics are associated with the initial main emotion valence and its potential change in this emotion.

Taking gambles at face value: Effects of emotional expressions on risky decisions

Emotional facial expressions are ubiquitous and potent social stimuli that can signal favorable and unfavorable conditions. Previous research demonstrates that emotional expressions influence preference judgments, basic approach-avoidance behaviors, and reward learning. We examined whether emotional expressions can influence decisions such as choices between gambles. Based on theories of affective cue processing, we predicted greater risk taking after positive than negative expressions. This hypothesis was tested in four experiments across tasks that varied in implementation of risks, payoffs, probabilities, and temporal decision requirements. Facial expressions were presented unobtrusively and were uninformative about the choice. In all experiments, the likelihood of a risky choice was greater after exposure to positive versus neutral or negative expressions. Similar effects on risky choice occurred after presentation of different negative expressions (e.g., anger, fear, sadness, and disgust), suggesting involvement of general positive and negative affect systems. These results suggest that incidental emotional cues exert a valence-specific influence of on decisions, which could shape risk-taking behavior in social situations.

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

The Role of the Cerebellar and Vestibular Networks in Anxiety Disorders and Depression: the Internal Model Hypothesis

Clinical data and animal studies confirmed that the cerebellum and the vestibular system are involved in emotions. Nowadays, no real consensus has really emerged to explain the clinical symptoms in humans and behavioral deficits in the animal models. We envisage here that the cerebellum and the vestibular system play complementary roles in emotional reactivity. The cerebellum integrates a large variety of exteroceptive and proprioceptive information necessary to elaborate and to update the internal model: in emotion, as in motor processes, it helps our body and self to adapt to the environment, and to anticipate any changes in such environment in order to produce a time-adapted response. The vestibular system provides relevant environmental stimuli (i.e., gravity, self-position, and movement) and is involved in self-perception. Consequently, cerebellar or vestibular disorders could generate « internal fake news» (due to lack or false sensory information and/or integration) that could, in turn, generate potential internal model deficiencies. In this case, the alterations provoke false anticipation of motor command and external sensory feedback, associated with unsuited behaviors. As a result, the individual becomes progressively unable to cope with the environmental solicitation. We postulate that chronically unsuited, and potentially inefficient, behavioral and visceral responses to environmental solicitations lead to stressful situations. Furthermore, this inability to adapt to the context of the situation generates chronic anxiety which could precede depressive states.

Striatal reactivity during emotion and reward relates to approach-avoidance conflict behaviour and is altered in adults with anxiety or depression

BACKGROUND

We have previously reported activation in reward, salience and executive control regions during functional MRI (fMRI) using an approach-avoidance conflict (AAC) decision-making task with healthy adults. Further investigations into how anxiety and depressive disorders relate to differences in neural responses during AAC can inform their understanding and treatment. We tested the hypothesis that people with anxiety or depression have altered neural activation during AAC.

METHODS

We compared 118 treatment-seeking adults with anxiety or depression and 58 healthy adults using linear mixed-effects models to examine group-level differences in neural activation (fMRI) during AAC decision-making. Correlational analyses examined relationships between behavioural and neural measures.

RESULTS

Adults with anxiety or depression had greater striatal engagement when reacting to affective stimuli (p = 0.008, d = 0.31) regardless of valence, and weaker striatal engagement during reward feedback (p = 0.046, d = -0.27) regardless of the presence of monetary reward. They also had blunted amygdala activity during decision-making (p = 0.023, d = -0.32) regardless of the presence of conflict. Across groups, approach behaviour during conflict decision-making was inversely correlated with striatal activation during affective stimuli (p < 0.001, r = -0.28) and positively related to striatal activation during reward feedback (p < 0.001, r = 0.27).

LIMITATIONS

Our transdiagnostic approach did not allow for comparisons between specific anxiety disorders, and our cross-sectional approach did not allow for causal inference.

CONCLUSION

Anxiety and depression were associated with altered neural responses to AAC. Findings were consistent with the role of the striatum in action selection and reward responsivity, and they point toward striatal reactivity as a future treatment target. Blunting of amygdala activity in anxiety or depression may indicate a compensatory response to inhibit affective salience and maintain approach.

Enhancing the Psychometric Properties of the Iowa Gambling Task Using Full Generative Modeling

Poor psychometrics, particularly low test-retest reliability, pose a major challenge for using behavioral tasks in individual differences research. Here, we demonstrate that full generative modeling of the Iowa Gambling Task (IGT) substantially improves test-retest reliability and may also enhance the IGT's validity for use in characterizing internalizing pathology, compared to the traditional analytic approach. IGT data ( n = 50 ) was collected across two sessions, one month apart. Our full generative model incorporated (1) the Outcome Representation Learning (ORL) computational model at the person-level and (2) a group-level model that explicitly modeled test-retest reliability, along with other group-level effects. Compared to the traditional 'summary score' (proportion good decks selected), the ORL model provides a theoretically rich set of performance metrics (Reward Learning Rate ( A + ) , Punishment Learning Rate ( A - ) , Win Frequency Sensitivity ( β f ) , Perseveration Tendency ( β p ) , Memory Decay ( K ) ), capturing distinct psychological processes. While test-retest reliability for the traditional summary score was only moderate (r = . 37 , BCa 95% CI [.04, .63]), test-retest reliabilities for ORL performance metrics produced by the full generative model were substantially improved, with test-retest correlations ranging between r = . 64 - . 82 for the five ORL parameters. Further, while summary scores showed no substantial associations with internalizing symptoms, ORL parameters were significantly associated with internalizing symptoms. Specifically, Punishment Learning Rate was associated with higher self-reported depression and Perseveration Tendency was associated with lower self-reported anhedonia. Generative modeling offers promise for advancing individual differences research using the IGT, and behavioral tasks more generally, through enhancing task psychometrics.

Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability

In this article, we present a deep learning model of human psychology that can predict one’s current age and future well-being. We used the model to demonstrate that one’s baseline well-being is not the determining factor of future well-being, as posited by hedonic treadmill theory. Further, we have created a 2D map of human psychotypes and identified the regions that are most vulnerable to depression. This map may be used to provide personalized recommendations for maximizing one’s future well-being.

A neural and behavioral trade-off between value and uncertainty underlies exploratory decisions in normative anxiety

Exploration reduces uncertainty about the environment and improves the quality of future decisions, but at the cost of provisional uncertain and suboptimal outcomes. Although anxiety promotes intolerance to uncertainty, it remains unclear whether and by which mechanisms anxiety relates to exploratory decision-making. We use a dynamic three-armed-bandit task and find that higher trait-anxiety is associated with increased exploration, which in turn harms overall performance. We identify two distinct behavioral sources: first, decisions made by anxious individuals are guided toward reduction of uncertainty; and second, decisions are less guided by immediate value gains. These findings are similar in both loss and gain domains, and further demonstrate that an affective trait relates to exploration and results in an inverse-U-shaped relationship between anxiety and overall performance. Additional imaging data (fMRI) suggests that normative anxiety correlates negatively with the representation of expected-value in the dorsal-anterior-cingulate-cortex, and in contrast, positively with the representation of uncertainty in the anterior-insula. We conclude that a trade-off between value-gains and uncertainty-reduction entails maladaptive decision-making in individuals with higher normal-range anxiety.

COVID-19 related distress in the Swedish population: Validation of the Swedish version of the COVID Stress Scales (CSS)

BACKGROUND

The COVID Stress Scales (CSS) assess health- and contamination-related distress in the face of a medical outbreak like the ongoing COVID-19 pandemic. Though the CSS is translated into 21 languages, it has not been validated in a Swedish national sample.

AIM

Our general objective is to provide a translation, replication, and validation of the CSS and test its convergent- and discriminant validity in relation to anxiety, health anxiety, depression, and stress in the general Swedish population. We also present latent psychometric properties by modelling based on item response theory.

METHODS

Participants consisted of 3044 Swedish adults (> 18 years) from a pre-stratified (gender, age, and education) sample from The Swedish Citizen Panel. Mental health status was assessed by validated instruments, including the CSS, PHQ-4, SHAI-14, and PSS-10.

RESULTS

Results indicate that our Swedish translation of CSS has good psychometric properties and consists of 5 correlated factors.

DISCUSSION

The CSS is useful either as a unidimensional or multidimensional construct using the CSS scales to measure key facets of pandemic-related stress.

CONCLUSIONS

The findings support the cross-cultural validity of the CSS and its potential utility in understanding many of the emotional challenges posed by the current and future pandemics.

State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning

Anxiety influences how the brain estimates and responds to uncertainty. The consequences of these processes on behaviour have been described in theoretical and empirical studies, yet the associated neural correlates remain unclear. Rhythm-based accounts of Bayesian predictive coding propose that predictions in generative models of perception are represented in alpha (8-12 Hz) and beta oscillations (13-30 Hz). Updates to predictions are driven by prediction errors weighted by precision (inverse variance) encoded in gamma oscillations (>30 Hz) and associated with the suppression of beta activity. We tested whether state anxiety alters the neural oscillatory activity associated with predictions and precision-weighted prediction errors (pwPE) during learning. Healthy human participants performed a probabilistic reward-based learning task in a volatile environment. In our previous work, we described learning behaviour in this task using a hierarchical Bayesian model, revealing more precise (biased) beliefs about the tendency of the reward contingency in state anxiety, consistent with reduced learning in this group. The model provided trajectories of predictions and pwPEs for the current study, allowing us to assess their parametric effects on the time-frequency representations of EEG data. Using convolution modelling for oscillatory responses, we found that, relative to a control group, state anxiety increased beta activity in frontal and sensorimotor regions during processing of pwPE, and in fronto-parietal regions during encoding of predictions. No effects of state anxiety on gamma modulation were found. Our findings expand prior evidence on the oscillatory representations of predictions and pwPEs into the reward-based learning domain. The results suggest that state anxiety modulates beta-band oscillatory correlates of pwPE and predictions in generative models, providing insights into the neural processes associated with biased belief updating and poorer learning.

Subjective optimality in finite sequential decision-making

Many decisions in life are sequential and constrained by a time window. Although mathematically derived optimal solutions exist, it has been reported that humans often deviate from making optimal choices. Here, we used a secretary problem, a classic example of finite sequential decision-making, and investigated the mechanisms underlying individuals' suboptimal choices. Across three independent experiments, we found that a dynamic programming model comprising subjective value function explains individuals' deviations from optimality and predicts the choice behaviors under fewer and more opportunities. We further identified that pupil dilation reflected the levels of decision difficulty and subsequent choices to accept or reject the stimulus at each opportunity. The value sensitivity, a model-based estimate that characterizes each individual's subjective valuation, correlated with the extent to which individuals' physiological responses tracked stimuli information. Our results provide model-based and physiological evidence for subjective valuation in finite sequential decision-making, rediscovering human suboptimality in subjectively optimal decision-making processes.

A model for learning based on the joint estimation of stochasticity and volatility

Previous research has stressed the importance of uncertainty for controlling the speed of learning, and how such control depends on the learner inferring the noise properties of the environment, especially volatility: the speed of change. However, learning rates are jointly determined by the comparison between volatility and a second factor, moment-to-moment stochasticity. Yet much previous research has focused on simplified cases corresponding to estimation of either factor alone. Here, we introduce a learning model, in which both factors are learned simultaneously from experience, and use the model to simulate human and animal data across many seemingly disparate neuroscientific and behavioral phenomena. By considering the full problem of joint estimation, we highlight a set of previously unappreciated issues, arising from the mutual interdependence of inference about volatility and stochasticity. This interdependence complicates and enriches the interpretation of previous results, such as pathological learning in individuals with anxiety and following amygdala damage.

Decision Making: a Theoretical Review

Decision-making is a crucial skill that has a central role in everyday life and is necessary for adaptation to the environment and autonomy. It is the ability to choose between two or more options, and it has been studied through several theoretical approaches and by different disciplines. In this overview article, we contend a theoretical review regarding most theorizing and research on decision-making. Specifically, we focused on different levels of analyses, including different theoretical approaches and neuropsychological aspects. Moreover, common methodological measures adopted to study decision-making were reported. This theoretical review emphasizes multiple levels of analysis and aims to summarize evidence regarding this fundamental human process. Although several aspects of the field are reported, more features of decision-making process remain uncertain and need to be clarified. Further experimental studies are necessary for understanding this process better and for integrating and refining the existing theories.

Increased inflammation predicts nine-year change in major depressive disorder diagnostic status

Cytokine theory of depression proposes that increased baseline inflammatory activity may accumulate over time and lead to future major depressive disorder (MDD). However, most research conducted on this topic has been cross-sectional and examined between- (vs. within-) persons and symptom severity (vs. diagnosis). Therefore, we tested if elevated inflammatory activity at Time 1 (T1) would predict future within-person 9-year change in MDD diagnosis. Community-dwelling adults (n = 945) participated in the Midlife Development in the United States (MIDUS) study. T1 and Time 2 (T2) MDD status was assessed using the Composite International Diagnostic Interview-Short Form, and markers of inflammatory activity at T1 were measured (e.g., levels of serum interleukin-6 [IL-6], C-reactive protein [CRP], fibrinogen). Latent change score modeling was conducted. Higher T1 IL-6, CRP, and fibrinogen levels of inflammatory activity predicted T1-T2 development/relapse of MDD within persons. This effect occurred more strongly among women (vs. men; d = .149 vs. .042), younger (vs. older) adults (d = .137 vs. .119), persons with more (vs. less) chronic health issues (d = .133 vs. .065), low- (vs. middle- or high-) income earners (d = .161 vs. .050), and persons with more (vs. less) frequent childhood trauma (d = .156 vs. .017). Findings aligned with expanded cytokine theories, which posit that the impact of increased T1 inflammatory activity on future change in MDD status will be larger for subgroups vulnerable to increased stress exposure. Cognitive-behavioral or pharmacological approaches to reduce markers of inflammatory activity may prevent development/relapse of MDD. General Scientific Summary: Increased C-reactive protein (CRP), fibrinogen, and interleukin-6 (IL-6) levels predicted 9-year major depressive disorder (MDD) diagnostic status change more strongly in younger than older adults, women but not men, those with low (vs. high) income, as well as persons with high (vs. low) childhood trauma frequency and number of chronic illnesses. Findings aligned with expanded cytokine theories (e.g., social signal transduction theory of depression), which posit that markers of inflammatory activity predict future change in MDD status especially for populations vulnerable to heightened, chronic, and long-term exposure to environmental stressors. Continued efforts to empirically test expanded cytokine theories of depression may improve delineation of patterns of health disparities and facilitate effective measures to prevent the onset or recurrence of MDD. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The relationship of caffeinated beverages with depressive symptoms and decision-making

BACKGROUND: Caffeine can affect depressive symptoms and decision-making. OBJECTIVE: This study aims to examine the relationship between caffeinated beverages consumption with depressive symptoms and decision-making styles. METHODS: This cross-sectional study was conducted with 432 adults working in office environment. The questionnaire consisting of individuals’ socio-demographic attributes, the frequency and the amount of caffeinated beverages consumption, the “Epidemiological Research Center-Depression (CES-D) Scale” and the “Decision-Making Styles Scale” were used. The frequency and amount of caffeinated beverages were determined using the food frequency questionnaire (FFQ). The participants were asked to choose which cup/mug they prefer to drink their caffeinated beverages and what amount they consume that beverage at a time. All the data were collected using online platforms. RESULTS: In the study, 76.7%of the participants were female and the mean age was 31.5±8.0 years. The average daily total caffeine intake of the participants was 425.8±461.4 mg and the total CES-D scale score was 17.7±11.2 points. It was found that as the amount of caffeine consumed increased, intuitive decision-making decreased and depressive symptoms increased (p <  0.05). In linear regression analysis, total caffeine consumption was found to be a significant predictor for the intuitive decision-making score (B: –0.151; p:0.002). When caffeine consumption is controlled, intuitive and rational decision making decreases with increasing depressive symptoms while addiction and avoidance decision making increased (p <  0.05). CONCLUSIONS: As a result, the amount of caffeine consumed daily was related to intuitive decision-making but did not effect depression. It has been observed that depressive symptoms affect decision-making styles in different ways. To our knowledge, our study is the first to examine the effects of caffeine consumption on depression and decision-making styles. Accordingly, future studies may focus on the link between caffeine consumption, depression, and decision-making styles in larger populations and the mechanisms that influence this relationship.

Mobile applications for prematurity: a systematic review protocol

Background

Premature birth is a global epidemic of significant public health concern. Counselling and education of pregnant women at risk of preterm birth or mothers with premature infants are essential to improve mother and infant health. Mobile applications are an increasingly popular tool among parents to receive health information and education. This study aims to evaluate the usages and the effects of a mobile application designed for premature births in order to improve health outcomes.

Methods

This review will include all studies of different designs which evaluated the use and impact of interventions provided via mobile applications on pregnant women at risk of preterm birth or mothers with premature infants in order to address all health outcomes. A combination of keywords and MeSH(Medical Subject Headings) terms is used in the search strategy. Literature databases including Scopus, PubMed, ISI Web of Science, ProQuest, CINAHL and Cochrane Library will be searched to May 2021. Furthermore, eligible studies will be chosen from the reference list of retrieved papers. Two researchers will independently review the retrieved citations to decide whether they meet the inclusion criteria. Mixed Methods Appraisal Tool (MMAT) V.2018 will be used to assess the quality of studies. Relevant data are collected in a data extraction form and analysed. Results are reported under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Discussion

This systematic review will recognize and combine evidence about the usages and impact of mobile application interventions on the health improvement of pregnant women at risk of preterm birth or mothers with premature infants.

The Psychological Impact of the COVID-19 Pandemic in Remote Learning in Higher Education

Stressful events can cause a significant impact on education; however, it is not yet clear how the interplay between anxiety, work, and social dysfunction relates to learning impairments. In this study, we investigated the impact of the COVID-19 pandemic on students’ learning and mental health. This study was conducted during four modules of a remote Psychopharmacology course between 5 October and 20 December 2020. We collected data from 28 Psychology undergraduate students at the University of São Paulo, Brazil. We used pre- and post-test multiple-choice questions to obtain a quantitative measure of learning. Students completed an online survey to report demographic information, functional impairment (Work and Social Adjustment scale; WSAS), generalized anxiety (Generalized Anxiety Disorder scale; GAD-7), coronavirus anxiety (Brazilian adapted version of the Coronavirus Anxiety Scale; CAS-BR), and self-perception with the remote lectures’ methodology. In our sample, 42.9% of respondents experienced symptoms of generalized anxiety disorders (GAD-7 ≥ 15), and 53.6% had moderate to severe functional impairment (WSAS > 20). We also observed an overlapping profile of highly anxious and dysfunctional students. A chi-square test of independence revealed a relation between pairs of multiple-choice questions answers and GAD-7 scores, indicating that less anxious students were more likely to perform better in pairs of pre- and post-tests. Intriguingly, the correlational analysis suggested that students with moderate to severe functional impairment (WSAS scores > 20) were less likely to change from an incorrect to a correct answer to pairs of pre- and post-tests. This data suggests that psychological distress and anxiety states might influence students’ ability to coordinate social and work activities and performance during remote learning. Although this study evaluated a small sample of students, our data highlights the importance of investigating anxiety and functional impairment measures as part of the remote-learning curriculum.

Impaired probabilistic reversal learning in anxiety: Evidence from behavioral and ERP findings

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High and low anxious participants finish a probabilistic reversal learning task.

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High anxious participants showed a worse performance and less likely to lose-shift.

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Feedback-related negativity (FRN) was correlated with the frequency of lose-shift.

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High anxious participants showed a smaller FRN in response to lose-shift.

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Anxious people’s reversal learning is affected by impaired sensitivity to losses.

Background

Reversal learning reflects an individual’s capacity to adapt to a dynamic environment with changing stimulus–reward contingencies. This study focuses on the potential influence of anxiety on reversal learning skills.

Methods

We asked 40 participants with a high level of trait anxiety (HTA) and 40 counterparts with a low anxiety level (LTA) to finish a probabilistic reversal learning task with event-related potential (ERP) recording, during which stimulus–reward contingencies are reversed after players have learned the optimal choice.

Results

We found that compared to their LTA counterparts, the HTA participants showed worse learning performance and were less likely to make lose-shift choices. The FRN amplitude might help interpret these behavioral results, which is suggested to be associated with punishment sensitivity and was positively correlated with the number of lose-shift in this study. Seeing that anxiety level predicted the FRN amplitude for lose-shift, we explain that anxious individuals’ inflexible behavioral responses to losses are due to their impaired sensitivity to negative feedback.

Conclusions

A higher level of anxiety is associated with weaker reversal learning performance, possibly because of abnormal sensitivity to negative outcomes. These findings have implications for the understanding of behavioral symptoms in anxiety.

Experimentally induced and real-world anxiety have no demonstrable effect on goal-directed behaviour

BACKGROUND

Goal-directed control guides optimal decision-making and it is an important cognitive faculty that protects against developing habits. Previous studies have found some evidence of goal-directed deficits when healthy individuals are stressed, and in psychiatric conditions characterised by compulsive behaviours and anxiety. Here, we tested if goal-directed control is affected by state anxiety, which might explain the former results.

METHODS

We carried out a causal test of this hypothesis in two experiments (between-subject N = 88; within-subject N = 50) that used the inhalation of hypercapnic gas (7.5% CO2) to induce an acute state of anxiety in healthy volunteers. In a third experiment (N = 1413), we used a correlational design to test if real-life anxiety-provoking events (panic attacks, stressful events) are associated with impaired goal-directed control.

RESULTS

In the former two causal experiments, we induced a profoundly anxious state, both physiologically and psychologically, but this did not affect goal-directed performance. In the third, correlational, study, we found no evidence for an association between goal-directed control, panic attacks or stressful life eventsover and above variance accounted for by trait differences in compulsivity.

CONCLUSIONS

In sum, three complementary experiments found no evidence that anxiety impairs goal-directed control in human subjects.

Meta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms

Reward learning is a ubiquitous cognitive mechanism guiding adaptive choices and behaviors, and when impaired, can lead to considerable mental health consequences. Reward-related functional neuroimaging studies have begun to implicate networks of brain regions essential for processing various peripheral influences (e.g., risk, subjective preference, delay, social context) involved in the multifaceted reward processing construct. To provide a more complete neurocognitive perspective on reward processing that synthesizes findings across the literature while also appreciating these peripheral influences, we used emerging meta-analytic techniques to elucidate brain regions, and in turn networks, consistently engaged in distinct aspects of reward processing. Using a data-driven, meta-analytic, k-means clustering approach, we dissociated seven meta-analytic groupings (MAGs) of neuroimaging results (i.e., brain activity maps) from 749 experimental contrasts across 176 reward processing studies involving 13,358 healthy participants. We then performed an exploratory functional decoding approach to gain insight into the putative functions associated with each MAG. We identified a seven-MAG clustering solution that represented dissociable patterns of convergent brain activity across reward processing tasks. Additionally, our functional decoding analyses revealed that each of these MAGs mapped onto discrete behavior profiles that suggested specialized roles in predicting value (MAG-1 & MAG-2) and processing a variety of emotional (MAG-3), external (MAG-4 & MAG-5), and internal (MAG-6 & MAG-7) influences across reward processing paradigms. These findings support and extend aspects of well-accepted reward learning theories and highlight large-scale brain network activity associated with distinct aspects of reward processing.

A Decision Architecture for Safety Computations

Accurately estimating safety is critical to pursuing nondefensive survival behaviors. However, little attention has been paid to how the human brain computes safety. We conceptualize a model that consists of two components: (i) threat-oriented evaluations that focus on threat value, imminence, and predictability; and (ii) self-oriented evaluations that focus on the agent's experience, strategies, and ability to control the situation. Our model points to the dynamic interaction between these two components as a mechanism of safety estimation. Based on a growing body of human literature, we hypothesize that distinct regions of the ventromedial prefrontal cortex (vmPFC) respond to threat and safety to facilitate survival decisions. We suggest safety is not an inverse of danger, but reflects independent computations that mediate defensive circuits and behaviors.

Predicting decisional conflict: Anxiety and depression in shared decision making

OBJECTIVES

Emotional distress can be a potential barrier to shared decision making (SDM), yet affect is typically not systematically assessed in medical consultation. We examined whether urological patients report anxiety or depression prior to a consultation and if emotional distress predicts decisional conflict thereafter.

METHODS

We recruited a large sample of urological outpatients (N = 397) with a range of different diagnoses (42 % oncological). Prior to a medical consultation, patients filled in questionnaires, including the Hospital Anxiety and Depression Scale. After the consultation, patients completed the Decisional Conflict Scale. We scored the rate of anxiety and depression in our sample and conducted multiple regression analysis to examine if emotional distress before the consultation predicts decisional conflict thereafter.

RESULTS

About a quarter of patients reported values at or above cut-off for clinically relevant emotional distress. Emotional distress significantly predicted a higher degree of decisional conflict. There were no differences in emotional distress between patients with and without uro-oncological diagnosis.

CONCLUSIONS

Emotional distress is common in urology patients - oncological as well as non-oncological. It predicts decisional conflict after physician consultation.

PRACTICE IMPLICATIONS

Emotional distress should be systematically assessed in clinical consultations. This may improve the process and outcome of SDM.

Psychiatric Neural Networks and Precision Therapeutics by Machine Learning

Learning and environmental adaptation increase the likelihood of survival and improve the quality of life. However, it is often difficult to judge optimal behaviors in real life due to highly complex social dynamics and environment. Consequentially, many different brain regions and neuronal circuits are involved in decision-making. Many neurobiological studies on decision-making show that behaviors are chosen through coordination among multiple neural network systems, each implementing a distinct set of computational algorithms. Although these processes are commonly abnormal in neurological and psychiatric disorders, the underlying causes remain incompletely elucidated. Machine learning approaches with multidimensional data sets have the potential to not only pathologically redefine mental illnesses but also better improve therapeutic outcomes than DSM/ICD diagnoses. Furthermore, measurable endophenotypes could allow for early disease detection, prognosis, and optimal treatment regime for individuals. In this review, decision-making in real life and psychiatric disorders and the applications of machine learning in brain imaging studies on psychiatric disorders are summarized, and considerations for the future clinical translation are outlined. This review also aims to introduce clinicians, scientists, and engineers to the opportunities and challenges in bringing artificial intelligence into psychiatric practice.

Shared Decision Making during the COVID-19 Pandemic

OBJECTIVE

The COVID-19 pandemic pushed some of the most well-developed health care systems to their limits. In many cases, this has challenged patient-centered care. We set out to examine individuals' attitudes toward shared decision making (SDM) and to identify predictors of participation preference during the pandemic.

METHODS

We conducted an online survey with a large convenience sample (N = 1061). Our main measures of interest were participants' generic and COVID-19-related participation preference as well as their acceptance and distress regarding a triage vignette. We also assessed anxiety, e-health literacy, and aspects of participants' health. We conducted group comparisons and multiple linear regression analyses on participation preference as well as triage acceptance.

RESULTS

In generic decision making, most participants expressed a strong need for information and a moderate participation preference. In the hypothetical case of COVID-19 infection, most preferred physician-led decisions. Generic participation preference was the strongest predictor of COVID-19-related participation preference, followed by age, education, and anxiety. Furthermore, both higher generic and COVID-19-related participation preferences predicted lower triage acceptance.

CONCLUSION

Our findings demonstrate potential health care recipients' attitudes toward SDM during a severe health care crisis and emphasize that participation preference varies according to the context.

Decreased sensitivity to risk levels in ventral stratum in major depressive disorder during risky decision-making

BACKGROUND

Reduced decision-making ability in depressive people has been observed both in daily life and experimental behavioral studies. However, the neurobiology of dysfunction in decision-making among depressive people is still unclear.

METHODS

The study included 63 patients with major depressive disorder (MDD) and 49 healthy controls (HCs). The balloon analog risk task (BART), a risky decision-making paradigm, was used in a functional magnetic resonance imaging experiment to evaluate how brain activation was modulated by different levels of risk.

RESULTS

No significant difference in behavioral performance was found. In prespecified brain regions, the activation of the left ventral stratum (VS) in MDD patients showed reduced modulation by risk levels compared with HCs. No significant group difference was found in prespecified dorsal anterior cingulate cortex (dACC) and right dorsal lateral prefrontal cortex (DLPFC).

LIMITATIONS

BART did not isolate stages of making a choice and experiencing the outcome of the choice.

CONCLUSION

The left VS was less sensitive to risk levels in MDD patients compared with HCs, indicating inefficient reward processing in risky decision-making in MDD.

Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach

BACKGROUND

Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict).

METHODS

A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence.

RESULTS

The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002).

LIMITATIONS

This study was limited by heterogeneity of the clinical sample and an inability to examine learning.

CONCLUSION

These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach

BACKGROUND

Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict).

METHODS

A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence.

RESULTS

The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002).

LIMITATIONS

This study was limited by heterogeneity of the clinical sample and an inability to examine learning.

CONCLUSION

These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

Mental Capacity, Decision-Making and Emotion Dysregulation in Severe Enduring Anorexia Nervosa

Severe and Enduring Anorexia Nervosa (SE-AN) is a chronic eating disorder characterized by long-term starvation and its physical and psychological sequelae, and severe loss of quality of life. Interactions between neurobiological changes caused by starvation, vulnerability (personality) traits, and eating behaviors play a role. Several other factors, such as increased fear and decreased social cognition, have also been found in relation to SE-AN. With this in mind, we aim to add to the understanding of SE-AN by introducing the concept of mental capacity (MC), which refers to the ability to understand and process information-both on a cognitive and an emotional level-and then make a well-informed choice. MC may be an important construct within the context of SE-AN. Furthermore, we will argue how impaired decision-making processes may underlie, fuel, or contribute to limited MC in SE-AN. We will speculate on the importance of dysfunctional emotion processing and anxiety-related processes (e.g., a high intolerance of uncertainty) and their potential interaction with decision-making. Lastly, we will propose how these aspects, which to our knowledge have previously received little attention, may advise research and treatment or help in dealing with the "want but cannot" situation of life-threatening AN.

Co-Designing a Web-Based Decision Aid Tool for Employees Disclosure of Mental Health Conditions: A Participatory Study Design Using Employee and Organizational Preferences

BACKGROUND

Decisions of whether to disclose mental health conditions are extremely personal and require the consideration of multiple factors associated with the disclosure process (eg, weighing the risks and benefits). Decision aid tools help people make these complex decisions. Such an aid needs to be confidential, easily accessible, and easy to use with the potential to access the tool on multiple occasions. Web programs are well suited to meet these requirements and, if properly developed, can provide feasible, accessible, affordable, and effective workplace interventions.

OBJECTIVE

This study aims to gain insights from potential end users, in this case both employees and organizations, into what type of components including language, style, and content would avoid potential stigma and ensure that elements of clear value for users would be built into a web-based decision aid tool that aims to assist employees in making decisions about the disclosure of their mental health condition at work.

METHODS

A participatory design approach was used to allow developers, researchers, experts, and end users to collaborate in co-designing the tool. During the user research phase of the development of the web-based tool, a participatory design workshop approach was selected as a part of a larger study of focus groups. Australian employees and managers in rural, suburban, and urban locations participated in an exploratory qualitative study involving participatory workshops designed to elicit their perspectives and preferences for a decision aid tool.

RESULTS

A total of 2 workshops were conducted with 13 participants. The majority were from a transport company (9/13, 69%), male (8/13, 62%), and employed full time (11/13, 85%). Six employees had previous experience disclosing their own mental health condition, and 7 were in a supervisory role and had previously been disclosed to. In any co-design development, there are certain trade-offs that need to be made between the views of experts, developers, end users, and the available budget. In this specific instance of a very delicate, personal decision, the end users provided valuable design insights into key areas such as language, and they were very antipathetic to a key feature, the avatar, which was thought to be desirable by experts and developers. Findings including aspects of the tool where all stakeholders were in agreement, aspects where some stakeholders disagreed and adaptations were implemented, where disagreements could not be implemented because of financial constraints, and misalignment between stakeholders and how to decide on a balance were shared.

CONCLUSIONS

The co-design with a lived experience approach is useful for contributing much to the design, language, and features. The key in this study was balancing the needs of the workers and the potential impact for the managers and organizations, while ensuring legislation and regulation requirements were upheld.

State anxiety biases estimates of uncertainty and impairs reward learning in volatile environments

Clinical and subclinical (trait) anxiety impairs decision making and interferes with learning. Less understood are the effects of temporary anxious states on learning and decision making in healthy populations, and whether these can serve as a model for clinical anxiety. Here we test whether anxious states in healthy individuals elicit a pattern of aberrant behavioural, neural, and physiological responses comparable with those found in anxiety disorders-particularly when processing uncertainty in unstable environments. In our study, both a state anxious and a control group learned probabilistic stimulus-outcome mappings in a volatile task environment while we recorded their electrophysiological (EEG) signals. By using a hierarchical Bayesian model of inference and learning, we assessed the effect of state anxiety on Bayesian belief updating with a focus on uncertainty estimates. State anxiety was associated with an underestimation of environmental uncertainty, and informational uncertainty about the reward tendency. Anxious individuals' beliefs about reward contingencies were more precise (had smaller uncertainty) and thus more resistant to updating, ultimately leading to impaired reward-based learning. State anxiety was also associated with greater uncertainty about volatility. We interpret this pattern as evidence that state anxious individuals are less tolerant to informational uncertainty about the contingencies governing their environment and more willing to be uncertain about the level of stability of the world itself. Further, we tracked the neural representation of belief update signals in the trial-by-trial EEG amplitudes. In control participants, lower-level precision-weighted prediction errors (pwPEs) about reward tendencies were represented in the ERP signals across central and parietal electrodes peaking at 496 ms, overlapping with the late P300 in classical ERP analysis. The state anxiety group did not exhibit a significant representation of low-level pwPEs, and there were no significant differences between the groups. Smaller variance in low-level pwPE about reward tendencies in state anxiety could partially account for the null results. Expanding previous computational work on trait anxiety, our findings establish that temporary anxious states in healthy individuals impair reward-based learning in volatile environments, primarily through changes in uncertainty estimates, which play a central role in current Bayesian accounts of perceptual inference and learning.

Association of Loss Aversion, Personality Traits, Depressive, Anxious, and Suicidal Symptoms: Systematic Review

OBJECTIVE

Loss aversion is defined as the individual perception of losses with a more significant impact than the gains of the same proportion, where people would be more sensitive to the possibility of losing objects or money than to the possibility of winning, even the same quantities. However, studies relating to loss aversion and psychological factors are still incipient. The aim of the present literature review was to identify and analyze the results of studies that investigated loss aversion regarding personality traits and symptoms of depression, anxiety, and suicidal tendencies.

METHOD

A systematic review was done through PUBMED and Scopus databases. Descriptors were defined according to each database specificities.

RESULTS

At first, 103 articles were encountered. After evaluation of the inclusion and exclusion criteria, there were a total of 14 remaining articles that were group together into six categories related to loss aversion, depression, anxiety, suicidal tendencies, and personality.

CONCLUSIONS

The present study contributes to the literature mapping in the Behavioural Economics field. However, discrepancies were found among the studies, which made it difficult to acquire more conclusive findings.