About

Oriel FeldmanHall is the Lab Director at FeldmanHallLab and holds a Ph.D. from the University of Cambridge as well as a BA from Cornell University. She completed her postdoctoral training at New York University (NYU). Her research interests focus on understanding the neural basis of human social behavior, particularly in areas such as morality, altruism, trust, and reciprocity. Throughout her career, she has received numerous prestigious awards, including the Cognitive Neuroscience Society’s Young Investigator Award for outstanding contributions to science, the Association for Psychological Science’s Janet Taylor Spence Award for Transformative Early Career Contributions, and the American Psychological Association’s Distinguished Scientific Award for Early Career Contribution to Psychology. In addition to her research, she teaches courses including Social Psychology (CLPS 700), the Moral Brain (CLPS 1760), and From Neuroeconomics to Political Polarization (CLPS 1740). Outside of her professional work, she enjoys reading early 19th century novels, dreaming about sky-diving in the Namib desert, and spending time with her energetic children.

Research topics

• Psychology
• Social psychology
• Virology
• Developmental psychology

Selected publications

• Errors in social network knowledge predict how ties evolve in the future

  2026-04-01

  articleOpen accessSenior author

  A striking feature of people’s social knowledge is that it is riddled with errors. These errors—e.g., making mistakes about who are actually friends in a social network—has typically been attributed to cognitive limitations or heuristics that prioritize knowledge of general social patterns over details about specific ties. Here, we demonstrate that such errors instead reflect an adaptive link prediction process that anticipates how the network will evolve far into the future. By longitudinally following a large, real-world social network (N=196) and tracking the formation and dissolution of social ties over the course of a year, we show that judgments that are wrong in the moment accurately forecast which relationships will emerge or dissolve up to six months later. On average, participants’ errors predicted tie formation nearly 3x, and tie loss more than 1.2x above chance levels. Beyond the use of simple heuristics like triadic closure and homophily, computational modeling reveals that successful link prediction relies on mental representations of the social network that integrates information about both direct and indirect, long-range ties between individuals. Having a social network representation that is future-oriented yields distinct social advantages: those who are better able to predict changes in the network become more centrally positioned over time, contingent on their initial position.

  PublisherDOI

• The roots of rationality

  PsyArXiv (OSF Preprints) · 2026-04-11

  preprintOpen access

  Functional accounts of mind, brain, and behavior have profoundly shaped thinking across many disciplines. Despite this common lens, the processes driving adaptive outcomes are often studied in isolation, leading to narrow and localized explanations. This paper brings together perspectives across fields by identifying six dynamics that enable agents to behave adaptively. These dynamics span multiple timescales and systems, including individual-level processes—thinking, learning, and development—and population-level processes—market selection, cultural evolution, and genetic evolution. We examine the reasons behind functional explanations of cognition and behavior, the conditions that foster rationality, and the interactions between adaptive processes. Our goal is to bring the cognitive, social, biological, and computer sciences into closer conversation by exposing researchers to a wide range of perspectives on adaptive decision-making.

  Publisher

• The roots of rationality

  2026-04-12

  articleOpen access

  Functional accounts of mind, brain, and behavior have profoundly shaped thinking across many disciplines. Despite this common lens, the processes driving adaptive outcomes are often studied in isolation, leading to narrow and localized explanations. This paper brings together perspectives across fields by identifying six dynamics that enable agents to behave adaptively. These dynamics span multiple timescales and systems, including individual-level processes—thinking, learning, and development—and population-level processes—market selection, cultural evolution, and genetic evolution. We examine the reasons behind functional explanations of cognition and behavior, the conditions that foster rationality, and the interactions between adaptive processes. Our goal is to bring the cognitive, social, biological, and computer sciences into closer conversation by exposing researchers to a wide range of perspectives on adaptive decision-making.

  PublisherDOI

• Asymmetric social ties are dynamic, detectable, and consequential for inference

  2026-04-20

  articleOpen accessSenior author

  Relational asymmetry—the common phenomenon in which two people hold fundamentally different views of their shared relationship—has long been treated as a static structural property of social networks. We challenge this view, demonstrating that asymmetry is instead an active process driven by interpersonal uncertainty resolution that unfolds through continuous belief updating within dyads. Tracking an emerging social network longitudinally, we reveal that the path from asymmetry to resolution follows a striking nonlinear pattern: convergence is most likely when initial disagreements are either small enough to reconcile with minimal effort or large enough to be impossible to ignore, suggesting that extreme relational mismatches act as a powerful catalyst for corrective action. At the network level, this dyadic process produces a dynamic equilibrium—asymmetries resolve while previously aligned dyads drift apart, sustaining a remarkably stable aggregate rate of relational asymmetry over time despite constant turnover in which ties are asymmetric. The consequences of asymmetry propagate beyond the dyad. Third-party network members perceive dyad-level asymmetry and use it to make adaptive social inferences, including how likely new ties will form and how fluidly will information flow through the network. Individuals whose own social spheres contain greater relational asymmetry show diminished ability to rely on network structure when making these inferences. These findings fundamentally recast relational asymmetry from a structural artifact to a dynamic, cross-level signal—one that shapes not only how dyads evolve but how entire networks organize.

  PublisherDOI

• Asymmetric social ties are dynamic, detectable, and consequential for inference

  PsyArXiv (OSF Preprints) · 2026-04-19

  preprintOpen accessSenior author

  Relational asymmetry—the common phenomenon in which two people hold fundamentally different views of their shared relationship—has long been treated as a static structural property of social networks. We challenge this view, demonstrating that asymmetry is instead an active process driven by interpersonal uncertainty resolution that unfolds through continuous belief updating within dyads. Tracking an emerging social network longitudinally, we reveal that the path from asymmetry to resolution follows a striking nonlinear pattern: convergence is most likely when initial disagreements are either small enough to reconcile with minimal effort or large enough to be impossible to ignore, suggesting that extreme relational mismatches act as a powerful catalyst for corrective action. At the network level, this dyadic process produces a dynamic equilibrium—asymmetries resolve while previously aligned dyads drift apart, sustaining a remarkably stable aggregate rate of relational asymmetry over time despite constant turnover in which ties are asymmetric. The consequences of asymmetry propagate beyond the dyad. Third-party network members perceive dyad-level asymmetry and use it to make adaptive social inferences, including how likely new ties will form and how fluidly will information flow through the network. Individuals whose own social spheres contain greater relational asymmetry show diminished ability to rely on network structure when making these inferences. These findings fundamentally recast relational asymmetry from a structural artifact to a dynamic, cross-level signal—one that shapes not only how dyads evolve but how entire networks organize.

  Publisher

• Emotion and Choice: The Integral Role of Emotion in Constructing Value

  2026-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Author response: Advantageous and disadvantageous inequality aversion can be taught through learning of others’ preferences

  2025-12-11

  peer-reviewOpen access
  PublisherDOI

• Advantageous and disadvantageous inequality aversion can be taught through vicarious learning of others’ preferences

  eLife · 2025-01-06

  preprintOpen access

  Abstract While enforcing egalitarian social norms is critical for human society, punishing social norm violators often incurs a cost to the self. This cost looms even larger when one can benefit from an unequal distribution of resources, a phenomenon known as advantageous inequity—for example, receiving a higher salary than a colleague with the identical role. In the Ultimatum Game, a classic testbed for fairness norm enforcement, individuals rarely reject (or punish) such unequal proposed divisions of resources because doing so entails a sacrifice of one’s own benefit. Recent work has demonstrated that observing and implementing another’s punitive responses to unfairness can efficiently alter the punitive preferences of an observer. It remains an open question, however, whether such contagion is powerful enough to impart advantageous inequity aversion to individuals—that is, can observing another’s preferences to punish inequity result in increased enforcement of equality norms, even in the difficult case of AI? Using a variant of the Ultimatum Game in which participants are tasked with responding to fairness violations on behalf of another ‘Teacher’—whose aversion to advantageous (versus disadvantageous) inequity was systematically manipulated—we probe whether individuals subsequently increase their punishment unfair after experiencing fairness violations on their own behalf. In two experiments, we found individuals can acquire aversion to advantageous inequity ‘vicariously’ through observing (and implementing) the Teacher’s preferences. Computationally, these learning effects were best characterized by a model which learns the latent structure of the Teacher’s preferences, rather than a simple Reinforcement Learning account. In summary, our study is the first to demonstrate that people can swiftly and readily acquire another’s preferences for advantageous inequity, suggesting in turn that behavioral contagion may be one promising mechanism through which social norm enforcement— which people rarely implement in the case of advantageous inequality—can be enhanced.

  PublisherDOI

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

  OSF Preprints (OSF Preprints) · 2025-01-01

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Prosociality

  Cambridge University Press eBooks · 2025-02-20

  book-chapter1st authorCorresponding
  PublisherDOI

Recent grants

• Administrative Core

  NIH · $46.6M · 2013–2025

• NCS-FO: Cognitive maps as a framework for organizing relationships in large-scale real social networks

  NSF · $776k · 2021–2024

Frequent coauthors

• Marc–Lluís Vives

  Leiden University

  54 shared

• Tim Dalgleish

  Medical Research Council

  49 shared

• Dean Mobbs

  45 shared

• Jeroen M. van Baar

  Brown University

  44 shared

• Matthew R. Nassar

  Allen Institute for Brain Science

  44 shared

• Joseph Heffner

  Yale University

  44 shared

• Apoorva Bhandari

  43 shared

• Jae-Young Son

  43 shared

Labs

• FeldmanHallLabPI

Awards & honors

• Cognitive Neuroscience Society’s Young Investigator Award fo…
• APS's Janet Taylor Spence Award for Transformative Early Car…
• Social and Affective Neuroscience Society Early Career Award…
• Society for Neuroeconomics Early Career Award (2020)
• NARSAD's Young Investigator Award (2017)