Dean Mobbs PhD

Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper 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 …

Unethical actions and decisions may distort human memory in two aspects: memory accuracy and metacognition. However, the neural and computational mechanisms underlying the metacognition distortion caused by repeated dishonesty remain largely unknown. Here, we performed two fMRI studies, including one replication study, with an information-sending task in the scanner. The main moral decision task in the scanner involves consistency and reward as two main factors, combined with a pre-scan and post-scan memory test together with mouse tracking. With multiple dimensions of metrics to measure metacognition, we test whether the inter-subject metacognition change correlates with how participants trade off consistency and reward. We find that the compression of representational geometry of reward in the orbitofrontal cortex (OFC) is correlated with both immediate and delayed metacognition changes. Also, the functional connectivity between the dorsolateral prefrontal cortex (DLPFC) and the left temporoparietal junction (lTPJ) under dishonest responses can predict both immediate and delayed metacognition changes in memory. These results suggest that decision-making, emotion, and memory-related brain regions together play a key role in metacognition change after immoral action, shedding light on the neural mechanism of the complex interplay between moral decisions, cognitive processes, and memory distortion.

Predators attack at different spatiotemporal scales, spurring prey to elicit escape responses that range from simple motor reactions and strategic planning that involve more complex cognitive processes. Recent work in humans suggests that escape relies on two distinct circuits: the reactive and cognitive fear circuits. However, the specific involvement of these two circuits in different stages of human escaping remains poorly characterized. In this study, we recorded intracranial electroencephalography (iEEG) from epilepsy patients while they performed a modified flight initiation distance (FID) task. We found brain regions in the cognitive fear circuit, including the ventromedial prefrontal cortex and hippocampus, encoded the threat level during the information processing stage. The actual escaping stage, especially under rapid attack, prominently activated areas within the reactive fear circuit, including the midcingulate cortex and amygdala. Furthermore, we observed a negative correlation between the high gamma activity (HGA) of the amygdala and the HGA of the vmPFC and HPC under rapid attacks. This indicates that the amygdala may suppress the activity of the cognitive fear circuit under rapid attacks, enabling the organism to react quickly to ensure survival under the imminent threat. These findings highlight the distinct roles of the reactive and cognitive fear circuits in human escaping, and provide a foundation for further research and potential applications.

BackgroundComparative research suggests that the hypothalamus is critical in switching between survival behaviors such as avoidance and hunting and it has been recently known as an “anxiety center”. However, the role of hypothalamus in humans is unclear due to the lack of naturalistic experimental paradigms and the difficulty of investigating human hypothalamic signals given its small size and low tissue contrast.MethodsWe introduce a gamified experimental paradigm where participants switch between hunting virtual prey and escaping from virtual predators. Deep learning-based segmentation enabled identifying individual-specific hypothalamus and the imaging sequence was optimized for hypothalamic signal acquisition. We investigated how hypothalamus and its connected regions encode switching using Multi-voxel pattern analysis (MVPA) and multi-voxel connectivity analysis. Computational …

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 …

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 …

BackgroundThe ability to plan for the future has been linked to transdiagnostic dimensions of psychopathology. However, these abilities are typically assessed using artificial cognitive tasks that only require short-term planning, unlike many real-world decisions. It remains unclear how planning is linked to transdiagnostic psychopathology in naturalistic environments where long-term planning is required.MethodsA gamified avoidance planning task was completed by subjects online (n= 200), alongside a battery of questionnaires measuring facets of psychopathology. Subjects navigated a character around virtual environments, collecting rewards while avoiding a predator. Importantly, subjects were required to make multiple moves at a time, requiring extended planning. Computational mechanisms supporting planning were assessed using tree-search planning models.ResultsModel fitting revealed that subjects …

BackgroundComputational accounts fail to explain why humans with anxiety have difficulty distinguishing threat from safety. Existing focus centers on external threat detection, but overlooks self-related safety computations that mediate threat estimates. Theoretical models propose the vmPFC integrates self-oriented safety with external-threat information to adaptively code threat contingencies.MethodsIn two preregistered studies (behavioral, N= 100, 50 females; fMRI, N= 30, 15 females), subjects performed a novel two-alternative forced choice task predicting whether they would receive electric shock when encountering attacking animals (external “threat”) while armed with protective weapons (self-oriented “safety”). Subjects were scanned intensively (∼ 4 hours) to increase signal-to-noise. Neuroimaging analyses:(1) parametric modulation tracking shock probability (FWE-corrected Z> 3.1, p<. 05);(2) MVPA …

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 …

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 …

The journal Personality Neuroscience has been in existence since 2018. It continues in its aim of providing an outlet for high-quality research at the interface of personality and neuroscience–especially research that might otherwise struggle to be published in either of these fields. We have already made considerable progress. There is potential to do more, though. As we enter our fifth year, now is an appropriate time to reflect on the past, examine the present, and look to the future.

Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsistic" approaches, which do not consider interactions between agents. Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios. Each scenario pairs a physical environment (a "substrate") with a reference set of co-players (a "background population"), to create a social situation with substantial interdependence between the individuals involved. For instance, some scenarios were inspired by institutional-economics-based accounts of natural resource management and public-good-provision dilemmas. Others were inspired by considerations from evolutionary biology, game theory, and artificial life. Melting Pot aims to cover a maximally diverse set of interdependencies and incentives. It includes the commonly-studied extreme cases of perfectly-competitive (zero-sum) motivations and perfectly-cooperative (shared-reward) motivations, but does not stop with them. As in real-life, a clear majority of scenarios in Melting Pot have mixed incentives. They are neither purely competitive nor purely cooperative and thus demand successful agents be able to navigate the resulting ambiguity. Here we describe Melting Pot 2.0, which revises and expands on Melting Pot. We also introduce support for scenarios with asymmetric roles, and explain how to integrate them into the evaluation protocol. This report also contains: (1 …

University teaching, whether by professors, lecturers, or instructors, draws on a number of skills including communicating and simplifying complex ideas, to inspiring students and teaching critical thinking. The social sciences, from psychology to behavioral economics, and anthropology, are often a joy to teach as the fields inherently attempt to understand ourselves by scientifically interrogating all aspects of human nature. What is not often explicitly considered is that the way we communicate science can come with implicit biases and narrow cultural references. This includes the data we present and how we interpret the data, as well as the identity of the scientists themselves. Attempts to increase unbiased teaching are particularly important for members of majority-status groups. This article draws on the recent increase in adoption of learner-centered teaching approaches, which aim to give students more power over learning. In supporting this goal, we offer suggestions for educators to reduce biased teaching practices and create more inclusive and positive learning environments. These are a selection of important biases to be combatted but are not intended to be all-inclusive. The overarching goal is to encourage teachers to continue on the path toward improving diversity and inclusion and to provide concrete action steps to do so. The aim of this article is to identify the unintended biases that educators can introduce into their classrooms. We discuss a range of seemingly small missteps that have substantial influence on the learning environment and students’ motivation and self-worth. The authors note that these Rules apply to teaching at all …

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).

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 …

Successful avoidance of recurrent threats depends on inferring threatening agents’ preferences and predicting their movement

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.

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 …

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.

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 …