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A fundamental challenge for humans—and other animals—is to survive in response to a natural diversity of dangers. Here, we provide a new perspective called Adaptive Threat Coding (ATC), where human defensive behavior is constructed in response to sensory, inferential, internal and subjective states that facilitate survival in a multidimensional landscape of danger. In humans, these states are guided by culture and social norms that influence the defensive response (e.g., avoidance of shame). The ATC model proposes that two contiguous survival strategies emerge: generalization, which is associated with representations that have high dimensionality and which furnish flexible cognitive heuristics (including efficient perceptual and conceptual representations, relations between threats, higher-order imagination, memory) and which converge through the construction of defensive states and via transfer learning. These representations support generalization and ‘explorative’ behaviors including information seeking and curiosity, where the goal is to increase situational awareness that can be used to mitigate current and future threat. Conversely, specialization entails lower dimensional representations, which underpin specialized, ‘exploitative’ behaviors. This specialization facilitates the economic execution of defensive behaviors, that are generated through a combination of learning and innate configuration. We finally propose that systems implicated in specialization and generalization coalesce through active inference and learning—and that this captures a central adaptive feature of human defensive systems; namely, self-preservation in response to a mélange of threats across diverse ecologies.

Social and behavioral science research proliferated during the COVID-19 pandemic, reflecting the substantial increase in influence of behavioral science in public health and public policy more broadly. This review presents a comprehensive assessment of 742 scientific articles on human behavior during COVID-19. Two independent teams evaluated 19 substantive policy recommendations (“claims”) on potentially critical aspects of behaviors during the pandemic drawn from the most widely cited behavioral science papers on COVID-19. Teams were made up of original authors and an independent team, all of whom were blinded to other team member reviews throughout. Both teams found evidence in support of 16 of the claims; for two claims, teams found only null evidence; and for no claims did the teams find evidence of effects in the opposite direction. One claim had no evidence available to assess. Seemingly due to the risks of the pandemic, most studies were limited to surveys, highlighting a need for more investment in field research and behavioral validation studies. The strongest findings indicate interventions that combat misinformation and polarization, and to utilize effective forms of messaging that engage trusted leaders and emphasize positive social norms.

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) 2022 APA, all rights reserved).

Fear and anxiety play a central role in the lives of humans and other animals, and there is considerable interest in clarifying their nature, identifying their biological underpinnings, and determining their consequences for health and disease. Although important strides have been made over the past half-century, it has become clear that our understanding remains far from complete. Here we provide a roundtable discussion on the nature and biological bases of fear- and anxiety-related states, traits, and psychiatric disorders. The discussants include scientists familiar with a wide variety of human and animal populations and a broad spectrum of basic, translational, and clinical science methods. The goal of the roundtable was to take stock of the state of the science—both basic and clinical—and provide a general roadmap to the next generation of fear and anxiety research. Much of the discussion centered on the major gaps and challenges facing the field, the most important avenues for future theoretical and empirical work, and emerging opportunities for accelerating discovery, with implications for scientists, trainees, funders, and other stakeholders. Understanding fear and anxiety is a matter of practical as well as theoretical importance. Anxiety disorders are a leading burden on global public health and existing treatments are far from consistently curative, underscoring the urgency of developing a deeper understanding of the factors governing threat-related emotions, and the myriad ways in which they influence the way we think, feel, and behave.

Successful avoidance of recurrent threats depends on inferring threatening agents’ preferences and predicting their movement patterns accordingly. However, it remains largely unknown how the human brain achieves this, despite the fact that many natural threats are posed by complex, dynamic agents that act according to their own goals. Here, we propose that humans exploit an interactive cognitive map of the social environment to infer threatening agents’ preferences and also to simulate their future behavior, providing for flexible, generalizable avoidance strategies. We tested this proposal across three preregistered experiments (total n=510) using a task in which participants collected rewards while avoiding one of several possible virtual threatening agents. A novel, model-based, hypothesis-testing inverse reinforcement learning computational model best explained participants’ inferences about threatening agents’ latent preferences, with participants using this inferred knowledge to enact generalizable, model-based avoidance strategies across different environments. Using tree-search planning models, we found that participants’ behavior was best explained by a planning algorithm that incorporated simulations of the threat’s goal-directed behavior, and that prior expectations about the threat’s predictability were linked to individual differences in avoidance. Together, our results indicate that humans use a cognitive map to determine threatening agents’ preferences, in turn facilitating generalized predictions of the threatening agent’s behavior and enabling flexible and effective avoidance.

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.

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).

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 networks. To identify the relevant neural patterns and build an interpretable model to predict dishonesty, we scanned 8-min rsfMRI before an information-passing task. In the task, we employed monetary rewards to induce dishonesty. We applied both connectome-based predictive modeling (CPM) and region-of-interest (ROI) analysis to examine the association between FC and dishonesty. CPM indicated that the stronger FC between fronto-parietal and default mode networks can predict a higher dishonesty rate. The ROIs were set in the regions involving four cognitive processes (self-reference, cognitive control, reward valuation, and moral regulation). The ROI analyses showed that a stronger FC between these regions and the prefrontal cortex can predict a higher dishonesty rate. Our study offers an integrated model to predict dishonesty with rsfMRI, and the results suggest that the frequent motivated dishonest behavior may require a higher engagement of social brain regions.

Humans are often faced with social dilemmas that pit self-interest against honesty, yet questions arise about whether this trade-off reflects spontaneous default response or active suppression of the alternative response. Here, we created two message-sending tasks where decisions involved honesty as truth-telling or dishonesty as untruthful-message-sending. In our pre-registered study, participants joined the message task as the sender, who can send truthful or untruthful messages to earn more money for themselves, thus manipulating the conflict between self-interest and honesty. In experiment 1, we used mouse tracking (MT) to gain insight from movement trajectories and reaction time (RTs) of a forced-choice task with one fair default option. We found that being honest under conflict situations took longer for individuals who had higher self-interest concerns. In experiment 2 with a two-alternative forced-choice design without a default fair option on another sample to test whether self-interest-seeking people would show a similar tendency to lie and how these behavioral patterns correlated with utility and drift-diffusion model (DDM) modeling parameters. We found lower RTs for untruthful responses when self-interest concern was higher. In both experiments, the mixed model of RTs indicated the interest-temptation (i.e., conflict) condition led to a longer RT for truth-telling. These results were also supported by a DDM showing a larger drift rate for untruthful, compared to truthful, responses in conflict trials. Overall, our findings further support the individual difference in moral default, which can be manifested in behavioral indices in decisions.

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., Humans adapt decision strategies in response to environmental demands. Here the authors show that decisions in a virtual foraging task can be modelled based on perceived patch value, which includes reward, competition and threat, and is associated with activity in ventromedial prefrontal and medial cingulate cortices.

Primates have developed a unique set of complex drives for successful group living, yet theorists rarely contemplate their taxonomy and how such drives relate to affective dynamics fundamental for group success. Affective dynamics and drive fulfillment exert mutual influence on one another, ultimately collectively promoting or undermining survival. We first identify six core benefits of group living common among both humans and other animals, and from this foundation we propose three broad social drives that have evolved to preserve or enhance group living benefits: (i) Mutualism comprises cooperation, reciprocity, trust, and fairness; (ii) Affiliation comprises assimilation and belonging, whereby one aims to fit into the group through adherence to group norms and ideologies; (iii) Status-Seeking is represented by a drive to build one’s value in the group and acquire differential access to mates and other resources. We identify affective dynamics that facilitate each social drive: (i) Reactive flexibility involves qualitative shifts in affect in response to shifting goals, which facilitates mutualism; (ii) Affective synchrony is the reproduction of another individual’s emotions in oneself and facilitates social affiliation; (iii) Regulatory flexibility facilitates status-seeking through a broad repertoire of regulatory approaches during strategic behavioral pursuits. Finally, we posit that fulfilling Mutualism, Affiliation, and Status-Seeking (MASS) drives enhances the benefits of social living and supports development of fundamental affective dynamics.

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.

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 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 were beyond experimental investigation so far. Inspired by nonhuman computational ethology, we explore how these new tools can be used to test important questions in human neuroscience. We argue that the application of this methodology will help human neuroscience and psychology extend on 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.

There is disagreement on how best to define and investigate fear. Nature Neuroscience asked Dean Mobbs to lead experts from the fields of human and animal affective neuroscience to discuss their viewpoints on how to define fear and how to move forward with the study of fear.

Theoretical models distinguish between neural responses elicited by distal threats and those evoked by more immediate threats1-3. Specifically, slower 'cognitive' fear responses towards distal threats involve a network of brain regions including the ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC), while immediate 'reactive' fear responses rely on regions such as the periaqueductal grey4,5. However, it is unclear how anxiety and its neural substrates relate to these distinct defensive survival circuits. We tested whether individual differences in trait anxiety would impact escape behaviour and neural responses to slow and fast attacking predators: conditions designed to evoke cognitive and reactive fear, respectively. Behaviourally, we found that trait anxiety was not related to escape decisions for fast threats, but individuals with higher trait anxiety escaped earlier during slow threats. Functional magnetic resonance imaging showed that when subjects faced slow threats, trait anxiety positively correlated with activity in the vHPC, mPFC, amygdala and insula. Furthermore, the strength of functional coupling between two components of the cognitive circuit-the vHPC and mPFC-was correlated with the degree of trait anxiety. This suggests that anxiety predominantly affects cognitive fear circuits that are involved in volitional strategic escape.

Dean Mobbs is an assistant professor in the Department of Humanities and Social Sciences and a member of the Computation and Neural Systems Program at the Californian Institute of Technology. He received his PhD from University College, London in 2008 and was a post-doctoral fellow at the Medical Research Council’s Cognition and Brain Science Unit, University of Cambridge. He is interested in the intersection of behavioral ecology, economics, emotion, and social psychology. By understanding the neural, computational and behavioral dynamics of human social and emotional experiences, his aims are to develop theoretical models that merge these fields. His awards include the APS Janet Spence Award for Transformative Early Career Contributions and a NARSAD Young Investigator Award. He is a life fellow of Clare Hall at the University of Cambridge. His research is supported by NIMH, the Brain & Behavior Research Foundation and the Tianqiao and Chrissy Chen Foundation. More details can be found here: www.deanmobbslab.com.

The natural world presents a myriad of dangers that can threaten an organism’s survival. This diversity of threats is matched by a set of universal and species specific defensive behaviors which are often subsumed under the emotions of fear and anxiety. A major issue in the field of affective science, however, is that these emotions are often conflated and scientists fail to reflect the ecological conditions that gave rise to them. I attempt to clarify these semantic issues by describing the link between ethologically defined defensive strategies and fear. This in turn, provides a clearer differentiation between fears, the contexts that evoke them and how they are organized within defensive survival circuits.

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.

Humans, like many other animals, pre-empt danger by moving to locations that maximize their success at escaping future threats. Here, we test the idea that spatial margin of safety (MOS) decisions, a form of prospective avoidance, result in participants placing themselves closer to safer locations when facing more unpredictable threats. Using multivariate pattern analysis on fMRI data collected while subjects engaged in MOS decisions with varying attack location predictability, we show that while the hippocampus encodes MOS decisions across all types of threat, a vmPFC anterior-posterior gradient tracked threat predictability. The posterior vmPFC encoded for more unpredictable threat and showed functional coupling with the amygdala and hippocampus. Conversely, the anterior vmPFC was more active for the more predictable attacks and showed coupling with the striatum. Our findings suggest that when pre-empting danger, the anterior vmPFC may provide a safety signal, possibly via predictable positive outcomes, while the posterior vmPFC drives prospective danger signals.

In group foraging organisms, optimizing the conflicting demands of competitive food loss and safety is critical. We demonstrate that humans select competition avoidant and risk diluting strategies during foraging depending on socially adjusted value. We formulate a mathematically grounded quantification of socially adjusted value in foraging environments and show using multivariate fMRI analyses that socially adjusted value is encoded by mid-cingulate and ventromedial prefrontal cortices, regions that integrate value and action signals.

Efforts to change behaviour are critical in minimizing the spread of highly transmissible pandemics such as COVID-19. However, it is unclear whether individuals are aware of disease risk and alter their behaviour early in the pandemic. We investigated risk perception and self-reported engagement in protective behaviours in 1591 United States-based individuals cross-sectionally and longitudinally over the first week of the pandemic. Subjects demonstrated growing awareness of risk and reported engaging in protective behaviours with increasing frequency but underestimated their risk of infection relative to the average person in the country. Social distancing and hand washing were most strongly predicted by the perceived probability of personally being infected. However, a subgroup of individuals perceived low risk and did not engage in these behaviours. Our results highlight the importance of risk perception in early interventions during large-scale pandemics.

Mentalizing, conventionally defined as the process in which we infer the inner thoughts and intentions of others, is a fundamental component of human social cognition. Yet its role, and the nuanced layers involved, in real world social interaction are rarely discussed. To account for this lack of theory, we propose the interactive mentalizing theory (IMT) which emphasize the role of the metacognition in different mentalizing components. We discuss the connection between mentalizing, metacognition, and social interaction in the context of four elements of mentalizing: (i) Metacognition - inference of our own thought processes and social cognitions and which is central to all other components of mentalizing including: (ii) first-order mentalizing – inferring the thoughts and intentions of an agent’s mind; (iii) personal second-order mentalizing - inference of other’s mentalizing of one’s own mind; (iv) Collective mentalizing: which takes at least two forms (a) vicarious mentalizing: adopting another’s mentalizing of an agent (i.e. what we think others think of an agent) and (b) co-mentalizing: mentalizing about an agent in conjunction with others’ mentalizing of that agent (i.e. conforming to others beliefs about another agent’s internal states). The weights of these four elements is determined by metacognitive insight and confidence in one’s own or another’s mentalizing ability, yielding a dynamic interaction between these circuits. To advance our knowledge on mentalizing during live social interaction, we identify how these subprocesses can be organized by different target agents and facilitated by combining computational modeling and interactive brain approaches.

A large proportion of human cognitive processes may operate outside of conscious awareness. Subliminally presented visual stimuli that are not consciously perceived have a pervasive effect on behavioral and autonomic responses. Recent studies have claimed that placebo/nocebo effects, which are previously thought to require conscious expectancies, can be elicited to comparable levels regardless of whether the stimuli were consciously perceived or not. We systematically explored the role of consciousness in conditioned analgesic and hyperalgesic pain responses using both classical delay conditioning procedure and trace conditioning procedure. In 2 experiments (total N = 247), we found that analgesic and hyperalgesic responses were differentially dependent on the conscious awareness of the relevant stimuli. Specifically, the analgesic response was only significant when stimuli were supraliminal in both conditioning/acquisition phase and test/activation phases. While the hyperalgesic responses were acquired and activated irrespective of stimulus exposure (supraliminal/subliminal), the magnitude of this response was larger when stimuli were supraliminal in the test stage. Our results indicate that analgesic responses require both conscious conditioning and conscious activation, challenging the view that classical conditioning of analgesic pain responses operates without conscious awareness. Hyperalgesic responses are generally not dependent on the consciousness of stimuli, suggesting the presence of a valence-specific rapid regulatory mechanism to enable adaptive responses in threatening circumstances. Our study demonstrates a nascent role of consciousness in the learning of complex cognitive processes. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The defining pathological features of social anxiety disorder primarily concern the social landscape, yet few empirical studies have examined the potentially aberrant behavioral and neural patterns in this population using socially interactive paradigms. We addressed this issue by investigating the behavioral and neural patterns associated with social conformity in patients with social anxiety disorder. We recorded event-related potentials when healthy subjects (n = 19), and patients with social anxiety disorder (n = 20) made attractiveness judgements of unfamiliar others, while at the same time, being exposed to congruent/incongruent peer ratings. Afterwards, participants were asked to rerate the same faces without the presence of peer ratings. When compared with healthy controls, social anxiety disorder patients exhibited more positive attitudes to unfamiliar others and conformed more with peers-higher feedback. These behavioral effects were in parallel with neural responses associated with social conflict in the N400 signal, showing higher conformity to peers-higher feedback compared with peers-lower or peers-agree feedback among social anxiety disorder patients. Our findings provide evidence on the behavioral and neural patterns of social anxiety disorder during social interactions, and support the hypothesis that individuals with social anxiety disorder are more motivated to pursue social acceptance and possibly avoid social rejection.

Flight initiation distance (FID), the distance at which an organism flees from an approaching threat, is an ecological metric of cost-benefit functions of escape decisions. We adapted the FID paradigm to investigate how fast or slow attacking ‘virtual predators’ constrain escape decisions. We show that rapid escape decisions rely on ‘reactive fear’ circuits in the periaqueductal gray and midcingulate cortex (MCC), while protracted escape decisions, defined by larger buffer zones, were associated with ‘cognitive fear’ circuits which include posterior cingulate cortex, hippocampus and the ventromedial prefrontal cortex, circuits implicated in strategic avoidance and behavioral flexibility. Using a Bayesian Decision Model, we further show that optimization of escape decisions under rapid flight were localized to the MCC, a region involved in adaptive motor control, while the hippocampus is implicated in optimizing decisions that update and control slower escape initiation. These results demonstrate an unexplored link between defensive survival circuits and their role in adaptive escape decisions.SignificanceHumans, like other animals, have evolved a set of circuits whose primary function is survival. In the case of predation, these circuits include ‘reactive fear’ circuits involved in fast and immediate escape decisions and ‘cognitive fear’ circuits that are involved in the conscious feeling of threat as well as slow strategic escape. Using neuroimaging combined with computational modeling, we support this differentiation of fear circuits by showing that fast escape decisions are elicited by the periaqueductal gray and MCC, regions involved in reactive flight. Conversely, slower escape decisions rely on the hippocampus, posterior cingulate cortex and prefrontal cortex, a circuit implicated in behavioral flexibility. These results support the role of the defensive survival circuitry in escape decisions and a separation of fear into reactive and cognitive circuits.

Organisms that evolved nervous systems that predict, detect, and assess ecological dangers are at a distinct survival advantage compared to those with less sophisticated neural machinery. The strategies generated and honed by adaptive survival circuits in humans, which we call the survival optimization system (SOS), are controlled by both conscious and implicit systems.

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.

Real behaviors are binding consequential commitments to a course of action, such as harming another person, buying an Apple watch, or fleeing from danger. Cognitive scientists are generally interested in the psychological and neural processes that cause such real behavior. However, for practical reasons, many scientific studies measure behavior using only hypothetical or imagined stimuli. Generalizing from such studies to real behavior implicitly assumes that the processes underlying the two types of behavior are similar. We review evidence of similarity and differences in hypothetical and real mental processes. In many cases, hypothetical choice tasks give an incomplete picture of brain circuitry that is active during real choice.

Decisions to avoid or escape predators occur at different spatiotemporal scales, resulting in different computations and neural circuits. At their extremes, surprising or proximal threats will reduce decision and state space and utilize model-free architectures, while distant threats allow increased information processing supported by model-based operations. Model-free and model-based computations, however, are often intertwined. Furthermore, under conditions of safety the foundations for effective reactive execution in the future can be laid through model-based instruction of model-free control. Prospective planning can also be enabled. Together, these computations reflect distinct population codes embedded within a distributed defensive circuitry whose goal is to determine and realize the best policy. Naturalistic observations show that decisions to avoid or escape predators occur at different spatiotemporal scales and that they are supported by different computations and neural circuits. At their extremes, proximal threats are addressed by a limited repertoire of reflexive and myopic actions, reflecting reduced decision and state spaces and model-free (MF) architectures. Conversely, distal threats allow increased information processing supported by model-based (MB) operations, including affective prospection, replay, and planning. However, MF and MB computations are often intertwined, and under conditions of safety the foundations for future effective reactive execution can be laid through MB instruction of MF control. Together, these computations are associated with distinct population codes embedded within a distributed defensive circuitry whose goal is to determine and realize the best policy.