A synthesis of evidence for policy from behavioural science during COVID-19.

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations ('claims') detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms 'physical distancing' and 'social distancing'. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

Interactive cognitive maps support flexible behavior under threat.

In social environments, survival can depend upon inferring and adapting to other agents' goal-directed behavior. However, it remains unclear how humans achieve this, despite the fact that many decisions must account for complex, dynamic agents acting according to their own goals. Here, we use a predator-prey task (total n = 510) to demonstrate that humans exploit an interactive cognitive map of the social environment to infer other agents' preferences and simulate their future behavior, providing for flexible, generalizable responses. A model-based inverse reinforcement learning model explained participants' inferences about threatening agents' preferences, with participants using this inferred knowledge to enact generalizable, model-based behavioral responses. Using tree-search planning models, we then found that behavior was best explained by a planning algorithm that incorporated simulations of the threat's goal-directed behavior. Our results indicate that humans use a cognitive map to determine other agents' preferences, facilitating generalized predictions of their behavior and effective responses.

The nature and neurobiology of fear and anxiety: State of the science and opportunities for accelerating discovery.

Fear and anxiety play a central role in mammalian life, and there is considerable interest in clarifying their nature, identifying their biological underpinnings, and determining their consequences for health and disease. Here we provide a roundtable discussion on the nature and biological bases of fear- and anxiety-related states, traits, and disorders. The discussants include scientists familiar with a wide variety of populations and a broad spectrum of techniques. The goal of the roundtable was to take stock of the state of the science and provide a roadmap to the next generation of fear and anxiety research. Much of the discussion centered on the key challenges facing the field, the most fruitful avenues for future research, and emerging opportunities for accelerating discovery, with implications for scientists, funders, and other stakeholders. Understanding fear and anxiety is a matter of practical importance. Anxiety disorders are a leading burden on public health and existing treatments are far from curative, underscoring the urgency of developing a deeper understanding of the factors governing threat-related emotions.

Orchestration of innate and conditioned defensive actions by the periaqueductal gray.

The midbrain periaqueductal gray (PAG) has been recognized for decades as having a central role in the control of a wide variety of defensive responses. Initial discoveries relied primarily on lesions, electrical stimulation and pharmacology. Recent developments in neural activity imaging and in methods to control activity with anatomical and genetic specificity have revealed additional streams of data informing our understanding of PAG function. Here, we discuss both classic and modern studies reporting on how PAG-centered circuits influence innate as well as learned defensive actions in rodents and humans. Though early discoveries emphasized the PAG's role in rapid induction of innate defensive actions, emerging new data indicate a prominent role for the PAG in more complex processes, including representing behavioral states and influencing fear learning and memory. This article is part of the Special Issue on "Fear, Anxiety and PTSD".

Computationally-defined markers of uncertainty aversion predict emotional responses during a global pandemic.

Exposure to stressful life events involving threat and uncertainty often results in the development of anxiety. However, the factors that confer risk and resilience for anxiety following real world stress at a computational level remain unclear. We identified core components of uncertainty aversion moderating response to stress posed by the COVID-19 pandemic derived from computational modeling of decision making. Using both cross-sectional and longitudinal analyses, we investigated both immediate effects at the onset of the stressor, as well as medium-term changes in response to persistent stress. 479 subjects based in the United States completed a decision-making task measuring risk aversion, loss aversion, and ambiguity aversion in the early stages of the pandemic (March 2020). Self-report measures targeting threat perception, anxiety, and avoidant behavior in response to the pandemic were collected at the same time point and 8 weeks later (May 2020). Cross-sectional analyses indicated that higher risk aversion predicted higher perceived threat from the pandemic, and ambiguity aversion for guaranteed gains predicted perceived threat and pandemic-related anxiety. In longitudinal analyses, ambiguity aversion for guaranteed gains predicted greater increases in perceived infection likelihood. Together, these results suggest that individuals who have a low-level aversion toward uncertainty show stronger negative emotional reactions to both the onset and persistence of real-life stress. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Model-based prioritization for acquiring protection.

Protection often involves the capacity to prospectively plan the actions needed to mitigate harm. The computational architecture of decisions involving protection remains unclear, as well as whether these decisions differ from other beneficial prospective actions such as reward acquisition. Here we compare protection acquisition to reward acquisition and punishment avoidance to examine overlapping and distinct features across the three action types. Protection acquisition is positively valenced similar to reward. For both protection and reward, the more the actor gains, the more benefit. However, reward and protection occur in different contexts, with protection existing in aversive contexts. Punishment avoidance also occurs in aversive contexts, but differs from protection because punishment is negatively valenced and motivates avoidance. Across three independent studies (Total N = 600) we applied computational modeling to examine model-based reinforcement learning for protection, reward, and punishment in humans. Decisions motivated by acquiring protection evoked a higher degree of model-based control than acquiring reward or avoiding punishment, with no significant differences in learning rate. The context-valence asymmetry characteristic of protection increased deployment of flexible decision strategies, suggesting model-based control depends on the context in which outcomes are encountered as well as the valence of the outcome.

Ambiguity drives higher-order Pavlovian learning.

In the natural world, stimulus-outcome associations are often ambiguous, and most associations are highly complex and situation-dependent. Learning to disambiguate these complex associations to identify which specific outcomes will occur in which situations is critical for survival. Pavlovian occasion setters are stimuli that determine whether other stimuli will result in a specific outcome. Occasion setting is a well-established phenomenon, but very little investigation has been conducted on how occasion setters are disambiguated when they themselves are ambiguous (i.e., when they do not consistently signal whether another stimulus will be reinforced). In two preregistered studies, we investigated the role of higher-order Pavlovian occasion setting in humans. We developed and tested the first computational model predicting direct associative learning, traditional occasion setting (i.e., 1st-order occasion setting), and 2nd-order occasion setting. This model operationalizes stimulus ambiguity as a mechanism to engage in higher-order Pavlovian learning. Both behavioral and computational modeling results suggest that 2nd-order occasion setting was learned, as evidenced by lack and presence of transfer of occasion setting properties when expected and the superior fit of our 2nd-order occasion setting model compared to the 1st-order occasion setting or direct associations models. These results provide a controlled investigation into highly complex associative learning and may ultimately lead to improvements in the treatment of Pavlovian-based mental health disorders (e.g., anxiety disorders, substance use).

Resting-state functional connectivity of social brain regions predicts motivated dishonesty.

Motivated dishonesty is a typical social behavior varying from person to person. Resting-state fMRI (rsfMRI) is capable of identifying unique patterns from functional connectivity (FC) between brain regions. Recent work has built a link between brain networks in resting state to dishonesty in Western participants. To determine and reproduce the relevant neural patterns and build an interpretable model to predict dishonesty, we analyzed two conceptually similar datasets containing rsfMRI data with different dishonesty tasks. Both tasks implemented the information-passing paradigm, in which monetary rewards were employed to induce dishonesty. We applied connectome-based predictive modeling (CPM) to build a model among FC within and between four social brain networks (reward, self-referential, moral, and cognitive control). The CPM analysis indicated that FCs of social brain networks are predictive of dishonesty rate, especially FCs within reward network, and between self-referential and cognitive control networks. Our study offers an conceptual replication with integrated model to predict dishonesty with rsfMRI, and the results suggest that frequent motivated dishonest decisions may require the higher engagement of social brain regions.

Physiological Responses to a Haunted-House Threat Experience: Distinct Tonic and Phasic Effects.

Threats elicit physiological responses, the frequency and intensity of which have implications for survival. Ethical and practical limitations on human laboratory manipulations present barriers to studying immersive threat. Furthermore, few investigations have examined group effects and concordance with subjective emotional experiences to threat. The current preregistered study measured electrodermal activity in 156 adults while they participated in small groups in a 30-min haunted-house experience involving various immersive threats. Results revealed positive associations between (a) friends and tonic arousal, (b) unexpected attacks and phasic activity (frequency and amplitude), (c) subjective fear and phasic frequency, and (d) dissociable sensitization effects linked to baseline orienting response. Findings demonstrate the relevance of (a) social dynamics (friends vs. strangers) for tonic arousal and (b) subjective fear and threat predictability for phasic arousal.

Fear in groups: Increasing group size reduces perceptions of danger.

When we face danger or stress, the presence of others can provide a powerful signal of safety and support. However, despite a large literature on group living benefits in animals, few studies have been conducted on how group size alters subjective emotional responses and threat perception in humans. We conducted 5 experiments (N = 3,652) to investigate whether the presence of others decreases fear in response to threat under a variety of conditions. In Studies 1, 2 and 3, we experimentally manipulated group size in hypothetical and real-world situations and found that fear responses decreased as group size increased. In Studies 4 and 5 we again used a combination of hypothetical and real-world decisions to test whether increased anxiety in response to a potential threat would lead participants to choose larger groups for themselves. Participants consistently chose larger groups when threat and anxiety were high. Overall, our findings show that group size provides a salient signal of protection and safety in humans. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Pavlovian occasion setting in human fear and appetitive conditioning: Effects of trait anxiety and trait depression.

Contexts and discrete stimuli often hierarchically influence the association between a stimulus and outcome. This phenomenon, called occasion setting, is central to modulation-based Pavlovian learning. We conducted two experiments with humans in fear and appetitive conditioning paradigms, training stimuli in differential conditioning, feature-positive discriminations, and feature-negative discriminations. We also investigated the effects of trait anxiety and trait depression on these forms of learning. Results from both experiments showed that participants were able to successfully learn which stimuli predicted the electric shock and monetary reward outcomes. Additionally, as hypothesized, the stimuli trained as occasion setters had little-to-no effect on simple reinforced or non-reinforced stimuli, suggesting the former were indeed occasion setters. Lastly, in fear conditioning, trait anxiety was associated with increases in fear of occasion setter/conditional stimulus compounds; in appetitive conditioning, trait depression was associated with lower expectations of monetary reward for the trained negative occasion setting compound and transfer of the negative occasion setter to the simple reinforced stimulus. These results suggest that clinically anxious individuals may have enhanced fear of occasion setting compounds, and clinically depressed individuals may expect less reward with compounds involving the negative occasion setter.

Neural encoding of perceived patch value during competitive and hazardous virtual foraging.

Natural observations suggest that in safe environments, organisms avoid competition to maximize gain, while in hazardous environments the most effective survival strategy is to congregate with competition to reduce the likelihood of predatory attack. We probed the extent to which survival decisions in humans follow these patterns, and examined the factors that determined individual-level decision-making. In a virtual foraging task containing changing levels of competition in safe and hazardous patches with virtual predators, we demonstrate that human participants inversely select competition avoidant and risk diluting strategies depending on perceived patch value (PPV), a computation dependent on reward, threat, and competition. We formulate a mathematically grounded quantification of PPV in social foraging environments and show using multivariate fMRI analyses that PPV is encoded by mid-cingulate cortex (MCC) and ventromedial prefrontal cortices (vMPFC), regions that integrate action and value signals. Together, these results suggest humans utilize and integrate multidimensional information to adaptively select patches highest in PPV, and that MCC and vMPFC play a role in adapting to both competitive and predatory threats in a virtual foraging setting.

Promises and challenges of human computational ethology.

The movements an organism makes provide insights into its internal states and motives. This principle is the foundation of the new field of computational ethology, which links rich automatic measurements of natural behaviors to motivational states and neural activity. Computational ethology has proven transformative for animal behavioral neuroscience. This success raises the question of whether rich automatic measurements of behavior can similarly drive progress in human neuroscience and psychology. New technologies for capturing and analyzing complex behaviors in real and virtual environments enable us to probe the human brain during naturalistic dynamic interactions with the environment that so far were beyond experimental investigation. Inspired by nonhuman computational ethology, we explore how these new tools can be used to test important questions in human neuroscience. We argue that application of this methodology will help human neuroscience and psychology extend limited behavioral measurements such as reaction time and accuracy, permit novel insights into how the human brain produces behavior, and ultimately reduce the growing measurement gap between human and animal neuroscience.

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.

How biopsychosocial depressive risk shapes behavioral and neural responses to social evaluation in adolescence.

INTRODUCTION: Understanding the emotional responsivity style and neurocognitive profiles of depression-related processes in at-risk youth may be helpful in revealing those most likely to develop affective disorders. However, the multiplicity of biopsychosocial risk factors makes it difficult to disentangle unique and combined effects at a neurobiological level. METHODS: In a population-derived sample of 56 older adolescents (aged 17-20), we adopted partial least squares regression and correlation models to explore the relationships between multivariate biopsychosocial risks for later depression, emotional response style, and fMRI activity, to rejecting and inclusive social feedback. RESULTS: Behaviorally, higher depressive risk was associated with both reduced negative affect following negative social feedback and reduced positive affect following positive social feedback. In response to both cues of rejection and inclusion, we observed a general neural pattern of increased cingulate, temporal, and striatal activity in the brain. Secondly, in response to rejection only, we observed a pattern of activity in ostensibly executive control- and emotion regulation-related brain regions encompassing fronto-parietal brain networks including the angular gyrus. CONCLUSION: The results suggest that risk for depression is associated with a pervasive emotional insensitivity in the face of positive and negative social feedback.

Seven computations of the social brain.

The social environment presents the human brain with the most complex information processing demands. The computations that the brain must perform occur in parallel, combine social and nonsocial cues, produce verbal and nonverbal signals and involve multiple cognitive systems, including memory, attention, emotion and learning. This occurs dynamically and at timescales ranging from milliseconds to years. Here, we propose that during social interactions, seven core operations interact to underwrite coherent social functioning; these operations accumulate evidence efficiently-from multiple modalities-when inferring what to do next. We deconstruct the social brain and outline the key components entailed for successful human-social interaction. These include (i) social perception; (ii) social inferences, such as mentalizing; (iii) social learning; (iv) social signaling through verbal and nonverbal cues; (v) social drives (e.g. how to increase one's status); (vi) determining the social identity of agents, including oneself and (vii) minimizing uncertainty within the current social context by integrating sensory signals and inferences. We argue that while it is important to examine these distinct aspects of social inference, to understand the true nature of the human social brain, we must also explain how the brain integrates information from the social world.

Mentalizing During Social Interaction: The Development and Validation of the Interactive Mentalizing Questionnaire.

Studies have shown that during social interaction a shared system underlies inferring one's own mental state, and the mental states of others - processes often referred to as mentalization. However, no validated assessment has been developed to measure second order mentalization (one's beliefs about how transparent one's thoughts are to others), or whether this capacity plays a significant role in social interaction. The current work presents a interactive mentalization theory, which divides these directional and second order aspects of mentalization, and investigates whether these constructs are measurable, stable, and meaningful in social interactions. We developed a 20-item, self-report interactive mentalization questionnaire (IMQ) in order to assess the different sub-components of mentalization: self-self, self-other, and other-self mentalization (Study 1). We then tested this scale on a large, online sample, and report convergent and discriminant validity in the form of correlations with other measures (Study 2), as well as correlations with social deception behaviors in real online interaction with Mturk studies (Study 3 and Study 4). These results validate the IMQ, and support the idea that these three factors can predict mentalization in social interaction.

Economic Decisions with Ambiguous Outcome Magnitudes Vary with Low and High Stakes but Not Trait Anxiety or Depression.

Most of life's decisions involve risk and uncertainty regarding whether reward or loss will follow. Decision makers often face uncertainty not only about the likelihood of outcomes (what are the chances that I will get a raise if I ask my supervisor? What are the chances that my supervisor will be upset with me for asking?) but also the magnitude of outcomes (if I do get a raise, how large will it be? If my supervisor gets upset, how bad will the consequences be for me?). Only a few studies have investigated economic decision making with ambiguous likelihoods, and even fewer have investigated ambiguous outcome magnitudes. In the present report, we investigated the effects of ambiguous outcome magnitude, risk, and gains/losses in an economic decision-making task with low stakes (Study 1; $3.60-$5.70; N = 367) and high stakes (Study 2; $6-$48; N = 210) using a within-subjects design. We conducted computational modeling to determine individuals' preferences/aversions for ambiguous outcome magnitudes, risk, and gains/losses. We additionally investigated the association between trait anxiety and trait depression and decision-making parameters. Our results show that increasing stakes increased ambiguous gain aversion and unambiguous risk aversion but increased ambiguous sure loss preference; participants also became more averse to ambiguous sure gains relative to unambiguous risky gains. There were no significant effects of trait anxiety or trait depression on economic decision making. Our results suggest that as stakes increase, people tend to avoid uncertainty in the gain domain (especially ambiguous gains) but prefer ambiguous vs unambiguous sure losses.