All Publications

  • Neural detection of socially valued community members PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Morelli, S. A., Leong, Y., Carlson, R. W., Kullar, M., Zaki, J. 2018; 115 (32): 8149–54


    As people form social groups, they benefit from being able to detect socially valuable community members-individuals who act prosocially, support others, and form strong relationships. Multidisciplinary evidence demonstrates that people indeed track others' social value, but the mechanisms through which such detection occurs remain unclear. Here, we combine social network and neuroimaging analyses to examine this process. We mapped social networks in two freshman dormitories (n = 97), identifying how often individuals were nominated as socially valuable (i.e., sources of friendship, empathy, and support) by their peers. Next, we scanned a subset of dorm members ("perceivers"; n = 50) as they passively viewed photos of their dormmates ("targets"). Perceiver brain activity in regions associated with mentalizing and value computation differentiated between highly valued targets and other community members but did not differentiate between targets with middle versus low levels of social value. Cross-validation analysis revealed that brain activity from novel perceivers could be used to accurately predict whether targets viewed by those perceivers were high in social value or not. These results held even after controlling for perceivers' own ratings of closeness to targets, and even though perceivers were not directed to focus on targets' social value. Overall, these findings demonstrate that individuals spontaneously monitor people identified as sources of strong connection in the broader community.

    View details for DOI 10.1073/pnas.1712811115

    View details for Web of Science ID 000440982000045

    View details for PubMedID 30038007

    View details for PubMedCentralID PMC6094096

  • Unrealistic Optimism in Advice Taking: A Computational Account JOURNAL OF EXPERIMENTAL PSYCHOLOGY-GENERAL Leong, Y., Zaki, J. 2018; 147 (2): 170–89


    Expert advisors often make surprisingly inaccurate predictions about the future, yet people heed their suggestions nonetheless. Here we provide a novel, computational account of this unrealistic optimism in advice taking. Across 3 studies, participants observed as advisors predicted the performance of a stock. Advisors varied in their accuracy, performing reliably above, at, or below chance. Despite repeated feedback, participants exhibited inflated perceptions of advisors' accuracy, and reliably "bet" on advisors' predictions more than their performance warranted. Participants' decisions tightly tracked a computational model that makes 2 assumptions: (a) people hold optimistic initial expectations about advisors, and (b) people preferentially incorporate information that adheres to their expectations when learning about advisors. Consistent with model predictions, explicitly manipulating participants' initial expectations altered their optimism bias and subsequent advice-taking. With well-calibrated initial expectations, participants no longer exhibited an optimism bias. We then explored crowdsourced ratings as a strategy to curb unrealistic optimism in advisors. Star ratings for each advisor were collected from an initial group of participants, which were then shown to a second group of participants. Instead of calibrating expectations, these ratings propagated and exaggerated the unrealistic optimism. Our results provide a computational account of the cognitive processes underlying inflated perceptions of expertise, and explore the boundary conditions under which they occur. We discuss the adaptive value of this optimism bias, and how our account can be extended to explain unrealistic optimism in other domains. (PsycINFO Database Record

    View details for DOI 10.1037/xge0000382

    View details for Web of Science ID 000423467500002

    View details for PubMedID 29154614

  • Dynamic Interaction between Reinforcement Learning and Attention in Multidimensional Environments. Neuron Leong, Y. C., Radulescu, A., Daniel, R., DeWoskin, V., Niv, Y. 2017; 93 (2): 451-463


    Little is known about the relationship between attention and learning during decision making. Using eye tracking and multivariate pattern analysis of fMRI data, we measured participants' dimensional attention as they performed a trial-and-error learning task in which only one of three stimulus dimensions was relevant for reward at any given time. Analysis of participants' choices revealed that attention biased both value computation during choice and value update during learning. Value signals in the ventromedial prefrontal cortex and prediction errors in the striatum were similarly biased by attention. In turn, participants' focus of attention was dynamically modulated by ongoing learning. Attentional switches across dimensions correlated with activity in a frontoparietal attention network, which showed enhanced connectivity with the ventromedial prefrontal cortex between switches. Our results suggest a bidirectional interaction between attention and learning: attention constrains learning to relevant dimensions of the environment, while we learn what to attend to via trial and error.

    View details for DOI 10.1016/j.neuron.2016.12.040

    View details for PubMedID 28103483

    View details for PubMedCentralID PMC5287409

  • Shared memories reveal shared structure in neural activity across individuals NATURE NEUROSCIENCE Chen, J., Leong, Y. C., Honey, C. J., Yong, C. H., Norman, K. A., Hasson, U. 2017; 20 (1): 115-125


    Our lives revolve around sharing experiences and memories with others. When different people recount the same events, how similar are their underlying neural representations? Participants viewed a 50-min movie, then verbally described the events during functional MRI, producing unguided detailed descriptions lasting up to 40 min. As each person spoke, event-specific spatial patterns were reinstated in default-network, medial-temporal, and high-level visual areas. Individual event patterns were both highly discriminable from one another and similar among people, suggesting consistent spatial organization. In many high-order areas, patterns were more similar between people recalling the same event than between recall and perception, indicating systematic reshaping of percept into memory. These results reveal the existence of a common spatial organization for memories in high-level cortical areas, where encoded information is largely abstracted beyond sensory constraints, and that neural patterns during perception are altered systematically across people into shared memory representations for real-life events.

    View details for DOI 10.1038/nn.4450

    View details for PubMedID 27918531

  • Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms JOURNAL OF NEUROSCIENCE Niv, Y., Daniel, R., Geana, A., Gershman, S. J., Leong, Y. C., Radulescu, A., Wilson, R. C. 2015; 35 (21): 8145-8157


    In recent years, ideas from the computational field of reinforcement learning have revolutionized the study of learning in the brain, famously providing new, precise theories of how dopamine affects learning in the basal ganglia. However, reinforcement learning algorithms are notorious for not scaling well to multidimensional environments, as is required for real-world learning. We hypothesized that the brain naturally reduces the dimensionality of real-world problems to only those dimensions that are relevant to predicting reward, and conducted an experiment to assess by what algorithms and with what neural mechanisms this "representation learning" process is realized in humans. Our results suggest that a bilateral attentional control network comprising the intraparietal sulcus, precuneus, and dorsolateral prefrontal cortex is involved in selecting what dimensions are relevant to the task at hand, effectively updating the task representation through trial and error. In this way, cortical attention mechanisms interact with learning in the basal ganglia to solve the "curse of dimensionality" in reinforcement learning.

    View details for DOI 10.1523/JNEUROSCI.2978-14.2015

    View details for Web of Science ID 000356673100010

    View details for PubMedID 26019331