Biology subjects

FeldmanHall, O.

Publications and source records attributed to FeldmanHall, O..

6 recordsLinked to original sources

bioRxiv2025

Medial temporal lobe encodes cognitive maps of real-world social networks

People routinely navigate their complex social networks1: From gossiping strategically with others2-4 to brokering connections between siloed groups5,6, our ability to make adaptive social choices hinges on whether we can construct useful mental representations of the social ties within our communities7. While decades of neuroscience research have shown that the medial temporal lobe encodes cognitive maps of physical8-10 or conceptual space11, how the brain represents our social networks in the wild to solve social problems remains unknown. By combining computational models with functional neuroimaging and longitudinal measurement of an evolving and densely interconnected real-world human network (N=187), we show that the entorhinal cortex encodes a cognitive map of the long-range connectivity between pairs of network members. This social map reflects the particular demands of social navigation and is specifically formatted to encode the simultaneous connectivity between network members, which critically enables tracking how information diffuses across the network. Moreover, the strength of its encoding in the entorhinal cortex aids in brokering connections that improve cohesion within peoples social communities. Our results illuminate how a domain-general neural mechanism12,13 is tailored to prioritize the natural dynamics of social phenomena in order to support adaptive navigation through these highly complex environments.

neuroscience↗

bioRxiv2024

Dissociable neural signals for reward and emotion prediction errors

Reinforcement learning models focus on reward prediction errors (PEs) as the driver of behavior. However, recent evidence indicates that deviations from emotion expectations, termed affective PEs, play a crucial role in shaping behavior. Whether there is neural separability between emotion and reward signals remains unknown. We employ electroencephalography during social learning to investigate the neural signatures of reward and affective PEs. Behavioral results reveal that while affective PEs predict choices when little is known about how a partner will behave, reward PEs become more predictive overtime as uncertainty about a partners behavior diminishes. This functional dissociation is mirrored neurally by engagement of distinct event-related potentials. The FRN indexes reward PEs while the P3b tracks affective PEs. Only the P3b predicts subsequent choices, highlighting the mechanistic influence of affective PEs during social learning. These findings present evidence for a neurobiologically viable emotion learning signal that is distinguishable--behaviorally and neurally--from reward. SignificanceFor nearly a century, scientists have asked how humans learn about their worlds. Learning models borrowed from computer science--namely, reinforcement learning--provide an elegant and simple framework that showcases how reward prediction errors are used to update ones knowledge about the environment. However, a fundamental question persists: what exactly is reward? This gap in knowledge is problematic, especially when we consider the multiplicity of social contexts where external rewards must be contextualized to gain value and meaning. We leverage electroencephalography to interrogate the role of emotion prediction errors--violations of emotional expectations--during learning. We observe distinct neural signals for reward and emotion prediction errors, suggesting that emotions may act as a bridge between external rewards and subjective value.

neuroscience↗

bioRxiv2023

Replay shapes abstract cognitive maps for efficient social navigation

To make adaptive social decisions, people must anticipate how information flows through their social network. While this requires knowledge of how people are connected, networks are too large to have firsthand experience with every possible route between individuals. How, then, are people able to accurately track information flow through social networks? We find that people immediately cache abstract knowledge about social network structure as they learn who is friends with whom, which enables the identification of efficient routes between remotely-connected individuals. These cognitive maps of social networks, which are built while learning, are then reshaped through overnight rest. During these extended periods of rest, a replay-like mechanism helps to make these maps increasingly abstract, which privileges improvements in social navigation accuracy for the longest communication paths that span distinct communities within the network. Together, these findings provide mechanistic insight into the sophisticated mental representations humans use for social navigation.

neuroscience↗

bioRxiv2023

Uncertainty aversion predicts the neural expansion of semantic representations

Correctly identifying the meaning of a stimulus requires activating the appropriate semantic representation among many alternatives. One way to reduce this uncertainty is to differentiate semantic representations from each other, thereby expanding the semantic space. In four experiments, we test this semantic-expansion hypothesis, finding that uncertainty averse individuals exhibit increasingly differentiated and separated semantic representations. This effect is mirrored at the neural level, where uncertainty aversion predicts greater distances between activity patterns in the left inferior frontal gyrus when reading words, and enhanced sensitivity to the semantic ambiguity of these words in the ventromedial prefrontal cortex. Two direct tests of the behavioral consequences of semantic-expansion further reveal that uncertainty averse individuals exhibit reduced semantic interference and poorer generalization. Together, these findings demonstrate that the internal structure of our semantic representations is shaped in a principled manner: aversion to uncertainty acts as an organizing principle to make the world more identifiable.

neuroscience↗

bioRxiv2020

Intolerance to uncertainty modulates neural synchrony between political partisans

Political partisans see the world through an ideologically biased lens. What drives political polarization? It has been posited that polarization arises because holding extreme political views satisfies a need for certain and stable beliefs about the world. We examined the relationship between uncertainty tolerance and political polarization using brain-to-brain synchrony analysis, which measured committed liberals and conservatives subjective interpretation of a continuous political narrative. Participants (N=44) watched a political debate while undergoing fMRI. Shared ideology between participants increased neural synchrony in many brain areas including key regions of the valuation and theory-of-mind networks (e.g. temporoparietal junction). The degree of neural synchrony was modulated by uncertainty aversion: Uncertainty-intolerant individuals experienced greater brain-to-brain synchrony with politically like-minded peers and lower synchrony with political opponents. This effect was observed for liberals and conservatives alike. Moreover, increasing neural synchrony between committed partisans predicted subsequent polarized attitude formation about the debate after the scanning session. These results suggest that uncertainty attitudes gate the shared neural processing of political narratives, thereby fueling polarized attitude formation about hot-button issues.

neuroscience↗

bioRxiv2020

Latent motives guide structure learning during adaptive social choice

Predicting the behavior of others is an essential part of human cognition that enables strategic social behavior (e.g., cooperation), and is impaired in multiple clinical populations. Despite its ubiquity, social prediction poses a generalization problem that remains poorly understood: We can neither assume that others will simply repeat their past behavior in new settings, nor that their future actions are entirely unrelated to the past. Here we demonstrate that humans solve this challenge using a structure learning mechanism that uncovers other people’s latent, unobservable motives, such as greed and risk aversion. In three studies, participants were tasked with predicting the decisions of another player in multiple unique economic games such as the Prisoner’s Dilemma. Participants achieved accurate social prediction by learning the hidden motivational structure underlying the player’s actions to cooperate or defect (e.g., that greed led to defecting in some cases but cooperation in others). This motive-based abstraction enabled participants to attend to information diagnostic of the player’s next move and disregard irrelevant contextual cues. Moreover, participants who successfully learned another’s motives were more strategic in a subsequent competitive interaction with that player, reflecting that accurate social structure learning can lead to more optimal social behaviors. These findings demonstrate that advantageous social behavior hinges on parsimonious and generalizable mental models that leverage others’ latent intentions.Significance statement A hallmark of human cognition is being able to predict the behavior of others. How do we achieve social prediction given that we routinely encounter others in a dizzying array of social situations? We find people achieve accurate social prediction by inferring another’s hidden motives—motives that do not necessarily have a one-to-one correspondence with observable behaviors. Participants were able to infer another’s motives using a structure learning mechanism that enabled generalization. Individuals used what they learned about others in one setting to predict their actions in an entirely new setting. This cognitive process can explain a wealth of social behaviors, ranging from strategic economic decisions to stereotyping and racial bias.Competing Interest StatementThe authors have declared no competing interest.View Full Text

animal behavior and cognition↗