We propose a Survival Optimization System (SOS) to account for the strategies that humans and other animals use to defend against recurring and novel threats. The SOS attempts to merge ecological models that define a repertoire of contextually relevant threat induced survival behaviors with contemporary approaches to human affective science. We first propose that the goal of the nervous system is to reduce surprise and optimize actions by (i) predicting the sensory landscape by simulating possible encounters with threat and selecting the appropriate pre-encounter action and (ii) prevention strategies in which the organism manufactures safe environments. When a potential threat is encountered the (iii) threat orienting system is engaged to determine whether the organism ignores the stimulus or switches into a process of (iv) threat assessment, where the organism monitors the stimulus, weighs the threat value, predicts the actions of the threat, searches for safety, and guides behavioral actions crucial to directed escape. When under imminent attack, (v) defensive systems evoke fast reflexive indirect escape behaviors (i.e., fight or flight). This cascade of responses to threat of increasing magnitude are underwritten by an interconnected neural architecture that extends from cortical and hippocampal circuits, to attention, action and threat systems including the amygdala, striatum, and hard-wired defensive systems in the midbrain. The SOS also includes a modulatory feature consisting of cognitive appraisal systems that flexibly guide perception, risk and action. Moreover, personal and vicarious threat encounters fine-tune avoidance behaviors via model-based learning, with higher organisms bridging data to reduce face-to-face encounters with predators. Our model attempts to unify the divergent field of human affective science, proposing a highly integrated nervous system that has evolved to increase the organism's chances of survival.

The social environment presents the human brain with the most complex of information processing demands. The computations that the brain must perform occur in parallel, combine social and nonsocial cues, produce verbal and non-verbal signals, and involve multiple cognitive systems; including memory, attention, emotion, 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 (1) social perception; (2) social inferences, such as mentalizing; (3) social learning; (4) social signaling through verbal and non-verbal cues; (5) social drives (e.g., how to increase one’s status); (6) determining the social identity of agents, including oneself; and (7) 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.

During social interaction, the brain has the enormous task of interpreting signals that are fleeting, subtle, contextual, abstract, and often ambiguous. Despite the signal complexity, the human brain has evolved to be highly successful in the social landscape. Here, we propose that the human brain makes sense of noisy dynamic signals through accumulation, integration, and prediction, resulting in a coherent representation of the social world. We propose that successful social interaction is critically dependent on a core set of highly connected hubs that dynamically accumulate and integrate complex social information and, in doing so, facilitate social tuning during moment-to-moment social discourse. Successful interactions, therefore, require adaptive flexibility generated by neural circuits composed of highly integrated hubs that coordinate context-appropriate responses. Adaptive properties of the neural substrate, including predictive and adaptive coding, and neural reuse, along with perceptual, inferential, and motivational inputs, provide the ingredients for pliable, hierarchical predictive models that guide our social interactions.

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.

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