Previous research suggests that intense, emotional pictures at fixation elicit an early posterior negativity (EPN) and a late positive potential (LPP) despite manipulations of spatial inattention and perceptual load. However, if high emotional intensity protects against such manipulations, then these manipulations should reduce emotional effects on EPN and LPP more strongly for medium than for intense emotional pictures. To test this prediction, pictures that were high negative, medium negative, or neutral were shown at fixation, and a small letter string was superimposed on the picture center. When participants attended the pictures, there were clear emotional effects on EPN and LPP. When participants attended the letter string, the emotional effects on LPP decreased; this decrease was smaller for medium than for high negative pictures. Thus, opposite of predictions, spatial inattention reduced the emotional effects more strongly for high than for medium negative pictures. As a manipulation of perceptual load, participants performed the letter task with one, three, or six relevant letters. Irrespective of load, EPN and LPP were similar for high and medium negative pictures. Our findings suggest that high negative valence does not protect EPN and LPP more strongly from effects of spatial inattention and perceptual load than does medium negative valence.
Both emotional facial expressions and markers of racial-group belonging are ubiquitous signals in social interaction, but little is known about how these signals together affect future behavior through learning. To address this issue, we investigated how emotional (threatening or friendly) in-group and out-group faces reinforced behavior in a reinforcement-learning task. We asked whether reinforcement learning would be modulated by intergroup attitudes (i.e., racial bias). The results showed that individual differences in racial bias critically modulated reinforcement learning. As predicted, racial bias was associated with more efficiently learned avoidance of threatening out-group individuals. We used computational modeling analysis to quantitatively delimit the underlying processes affected by social reinforcement. These analyses showed that racial bias modulates the rate at which exposure to threatening out-group individuals is transformed into future avoidance behavior. In concert, these results shed new light on the learning processes underlying social interaction with racial-in-group and out-group individuals.
Social groups are organized along dominance hierarchies, which determine how we respond to threats posed by dominant and subordinate others. The persuasive impact of these dominance threats on mental and physical well-being has been well described but it is unknown how dominance rank of others bias our experience and learning in the first place. We introduce a model of conditioned social dominance threat in humans, where the presence of a dominant other is paired with an aversive event. Participants first learned about the dominance rank of others by observing their dyadic confrontations. During subsequent fear learning, the dominant and subordinate others were equally predictive of an aversive consequence (mild electric shock) to the participant. In three separate experiments, we show that participants' eye-blink startle responses and amygdala reactivity adaptively tracked dominance of others during observation of confrontation. Importantly, during fear learning dominant vs subordinate others elicited stronger and more persistent learned threat responses as measured by physiological arousal and amygdala activity. Our results characterize the neural basis of learning through observing conflicts between others, and how this affects subsequent learning through direct, personal experiences.
Associations linking a fearful experience to a member of a social group other than one's own (out-group) are more resistant to change than corresponding associations to a member of one's own (in-group) (Olsson et al., 2005; Kubota et al., 2012), providing a possible link to discriminative behavior. Using a fear conditioning paradigm, we investigated the neural activity underlying aversive learning biases towards in-group (White) and out-group (Black) members, and their predictive value for discriminatory interactive behavior towards novel virtual members of the racial out-group (n = 20). Our results indicate that activity in brain regions previously linked to conditioned fear and perception of individuals belonging to the racial out-groups, or otherwise stigmatized groups, jointly contribute to the expression of race-based biases in learning and behavior. In particular, we found that the amygdala and anterior insula (AI) played key roles in differentiating between in-group and out-group faces both when the faces were paired with an aversive event (acquisition) and when no more shocks were administered (extinction). In addition, functional connectivity between the amygdala and the fusiform gyrus increased during perception of conditioned out-group faces. Moreover, we showed that brain activity in the fear-learning-bias network was related to participants' discriminatory interactions with novel out-group members on a later day. Our findings are the first to identify the neural mechanism of fear learning biases towards out-group members, and its relationship to interactive behavior. Our findings provide important clues towards understanding the mechanisms underlying biases between social groups.
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.
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