Explore the vast range of titles available.

Many fearful expectations are shaped by observation of aversive outcomes to others. Yet, the neurochemistry regulating social learning is unknown. Previous research has shown that during direct (Pavlovian) threat learning, information about personally experienced outcomes is regulated by the release of endogenous opioids, and activity within the amygdala and periaqueductal gray (PAG). Here we report that blockade of this opioidergic circuit enhances social threat learning through observation in humans involving activity within the amygdala, midline thalamus and the PAG. In particular, anticipatory responses to learned threat cues (CS) were associated with temporal dynamics in the PAG, coding the observed aversive outcomes to other (observational US). In addition, pharmacological challenge of the opioid receptor function is classified by distinct brain activity patterns during the expression of conditioned threats. Our results reveal an opioidergic circuit that codes the observed aversive outcomes to others into threat responses and long-term memory in the observer. Though humans often learn about negative outcomes from observing the response of others, the neurochemistry underlying this learning is unknown. Here, authors show that blocking opioid receptors enhances social threat learning and describe the brain regions underlying this effect.

The neural mechanisms that support naturalistic learning via effective pedagogical approaches remain elusive. Here we used functional near-infrared spectroscopy to measure brain activity from instructor-learner dyads simultaneously during dynamic conceptual learning. Results revealed that brain-to-brain coupling was correlated with learning outcomes, and, crucially, appeared to be driven by specific scaffolding behaviors on the part of the instructors (e.g., asking guiding questions or providing hints). Brain-to-brain coupling enhancement was absent when instructors used an explanation approach (e.g., providing definitions or clarifications). Finally, we found that machine-learning techniques were more successful when decoding instructional approaches (scaffolding vs. explanation) from brain-to-brain coupling data than when using a single-brain method. These findings suggest that brain-to-brain coupling as a pedagogically relevant measure tracks the naturalistic instructional process during instructor-learner interaction throughout constructive engagement, but not information clarification. •We investigated instruction-based naturalistic learning using fNIRS hyperscanning.•Instructor-learner brain coupling was driven by specific scaffolding behaviors.•Instructor-learner brain coupling predicted learning outcomes.•Instructor-learner brain coupling was successfully used to decode instructional approaches.

Learning the value of stimuli and actions from others — social learning — adaptively contributes to individual survival and plays a key role in cultural evolution. We review research across species targeting the neural and computational systems of social learning in both the aversive and appetitive domains. Social learning generally follows the same principles as self-experienced value-based learning, including computations of prediction errors and is implemented in brain circuits activated across task domains together with regions processing social information. We integrate neural and computational perspectives of social learning with an understanding of behaviour of varying complexity, from basic threat avoidance to complex social learning strategies and cultural phenomena.Learning the value of stimuli and actions from others — social learning — is crucial for survival. In this review, Olsson, Knapska and Lindström discuss the neural and computational systems underlying social and self-experienced learning, and integrate this knowledge with behavioural phenomena of varying complexity.

To survive in a complex social group, one needs to know who to approach and, more importantly, who to avoid. In mice, a single defeat causes the losing mouse to stay away from the winner for weeks 1 . Here through a series of functional manipulation and recording experiments, we identify oxytocin neurons in the retrochiasmatic supraoptic nucleus (SOR OXT ) and oxytocin-receptor-expressing cells in the anterior subdivision of the ventromedial hypothalamus, ventrolateral part (aVMHvl OXTR ) as a key circuit motif for defeat-induced social avoidance. Before defeat, aVMHvl OXTR cells minimally respond to aggressor cues. During defeat, aVMHvl OXTR cells are highly activated and, with the help of an exclusive oxytocin supply from the SOR, potentiate their responses to aggressor cues. After defeat, strong aggressor-induced aVMHvl OXTR cell activation drives the animal to avoid the aggressor and minimizes future defeat. Our study uncovers a neural process that supports rapid social learning caused by defeat and highlights the importance of the brain oxytocin system in social plasticity. In mice, the neural mechanisms underlying aversive social learning, specifically avoidance and fear after defeat, involve oxytocin signalling in the anterior subdivision of the ventromedial hypothalamus, ventrolateral part.

The posterior cerebellum has a critical role in human social and emotional learning. Three systems and related neural networks support this cerebellar function: a biological action observation system as part of an extended sensorimotor integration network, a mentalizing system for understanding a person’s mental and emotional state subserved by a mentalizing network, and a limbic network supporting core emotional (dis)pleasure and arousal processes. In this Review, I describe how these systems and networks support social and emotional learning via functional reciprocal connections initiating and terminating in the posterior cerebellum and cerebral neocortex. It is hypothesized that a major function of the posterior cerebellum is to identify and encode temporal sequences of events, which might help to fine-tune and automatize social and emotional learning. I discuss research using neuroimaging and non-invasive stimulation that provides converging evidence for this hypothesized function of cerebellar sequencing, but also other potential functional accounts of the posterior cerebellum’s role in these social and emotional processes.The cerebellum’s canonical role in learning is expanding beyond movement coordination. In this Review, Van Overwalle details the systems and networks facilitating the cerebellum’s role in human social and emotional learning and discusses whether cerebellar temporal sequencing might account for this functionality.

Animals can learn about sources of danger while minimizing their own risk by observing how others respond to threats. However, the distinct neural mechanisms by which threats are learned through social observation (known as observational fear learning 1 , 2 , 3 – 4 (OFL)) to generate behavioural responses specific to such threats remain poorly understood. The dorsomedial prefrontal cortex (dmPFC) performs several key functions that may underlie OFL, including processing of social information and disambiguation of threat cues 5 , 6 , 7 , 8 , 9 , 10 – 11 . Here we show that dmPFC is recruited and required for OFL in mice. Using cellular-resolution microendoscopic calcium imaging, we demonstrate that dmPFC neurons code for observational fear and do so in a manner that is distinct from direct experience. We find that dmPFC neuronal activity predicts upcoming switches between freezing and moving state elicited by threat. By combining neuronal circuit mapping, calcium imaging, electrophysiological recordings and optogenetics, we show that dmPFC projections to the midbrain periaqueductal grey (PAG) constrain observer freezing, and that amygdalar and hippocampal inputs to dmPFC opposingly modulate observer freezing. Together our findings reveal that dmPFC neurons compute a distinct code for observational fear and coordinate long-range neural circuits to select behavioural responses. Studies in mice show that observational fear learning is encoded by neurons in the dorsomedial prefrontal cortex in a manner that is distinct from the encoding of fear learned by direct experience.

Social interactions represent a ubiquitous aspect of our everyday life that we acquire by interpreting and responding to visual cues from conspecifics 1 . However, despite the general acceptance of this view, how visual information is used to guide the decision to cooperate is unknown. Here, we wirelessly recorded the spiking activity of populations of neurons in the visual and prefrontal cortex in conjunction with wireless recordings of oculomotor events while freely moving macaques engaged in social cooperation. As animals learned to cooperate, visual and executive areas refined the representation of social variables, such as the conspecific or reward, by distributing socially relevant information among neurons in each area. Decoding population activity showed that viewing social cues influences the decision to cooperate. Learning social events increased coordinated spiking between visual and prefrontal cortical neurons, which was associated with improved accuracy of neural populations to encode social cues and the decision to cooperate. These results indicate that the visual-frontal cortical network prioritizes relevant sensory information to facilitate learning social interactions while freely moving macaques interact in a naturalistic environment. Behavioural tracking and wireless neural and eye-tracking recordings show that freely moving macaques learn to cooprate using visually guided signals along the visual-frontal cortical network.

While there is no doubt that social signals affect human reinforcement learning, there is still no consensus about how this process is computationally implemented. To address this issue, we compared three psychologically plausible hypotheses about the algorithmic implementation of imitation in reinforcement learning. The first hypothesis, decision biasing (DB), postulates that imitation consists in transiently biasing the learner’s action selection without affecting their value function. According to the second hypothesis, model-based imitation (MB), the learner infers the demonstrator’s value function through inverse reinforcement learning and uses it to bias action selection. Finally, according to the third hypothesis, value shaping (VS), the demonstrator’s actions directly affect the learner’s value function. We tested these three hypotheses in 2 experiments ( N = 24 and N = 44) featuring a new variant of a social reinforcement learning task. We show through model comparison and model simulation that VS provides the best explanation of learner’s behavior. Results replicated in a third independent experiment featuring a larger cohort and a different design ( N = 302). In our experiments, we also manipulated the quality of the demonstrators’ choices and found that learners were able to adapt their imitation rate, so that only skilled demonstrators were imitated. We proposed and tested an efficient meta-learning process to account for this effect, where imitation is regulated by the agreement between the learner and the demonstrator. In sum, our findings provide new insights and perspectives on the computational mechanisms underlying adaptive imitation in human reinforcement learning.

Many prosocial and antisocial behaviors simultaneously impact both ourselves and others, requiring us to learn from their joint outcomes to guide future choices. However, the neurocomputational processes supporting such social learning remain unclear. Across three pre-registered studies, participants learned how choices affected both themselves and others. Computational modeling tested whether people simulate how other people value their choices or integrate self- and other-relevant information to guide choices. An integrated value framework, rather than simulation, characterizes multi-outcome social learning. People update the expected value of choices using different types of prediction errors related to the target (e.g., self, other) and valence (e.g., positive, negative). This asymmetric value update is represented in brain regions that include ventral striatum, subgenual and pregenual anterior cingulate, insula, and amygdala. These results demonstrate that distinct encoding of self- and other-relevant information guides future social behaviors across mutually beneficial, mutually costly, altruistic, and instrumentally harmful scenarios. When learning to make choices that simultaneously affect the self and others, asymmetric encoding of information guides future social behaviors across mutually beneficial, mutually costly, altruistic, and instrumentally harmful contexts.

•There are distinct neural mechanisms underlying imitation learning and co-presence learning.•Imitation learning and co-presence learning influenced word formation learning.•The left middle frontal gyrus is an important neural basis for co-presence learning.•The neural activity flowing in a unidirectional manner from the imitator to the demonstrator in imitation learning. Imitation learning and co-presence learning are common forms of social learning. However, the effects of these two types of learning on acquiring word formation rules have gone relatively underexplored, particularly in the context of adult social learning. The current study uses functional near-infrared spectroscopy (fNIRS) hyperscanning techniques to record the cognitive neural mechanisms of acquiring word formation rules during imitation learning and co-presence learning among dyads of 120 healthy adults. The experiment was a 2 (word learning type: within-subjects, easy word formation rules vs. difficult word formation rules) × 2 (social learning type: between-subjects, imitation learning vs. co-presence learning) mixed design. We used FDR correction to control for false positive rates. Co-presence learning enhanced interbrain synchronization and representation similarity among co-learners in the left middle frontal gyrus. In contrast, imitation learning increased interbrain synchronization in the right superior frontal gyrus, with Granger causality analysis indicating a unidirectional flow of neural activity from the imitator to the demonstrator. These findings suggest that there are distinct neural mechanisms underlying imitation learning and co-presence learning.