Honors & Awards

  • Interdisciplinary Postdoctoral Scholar Award, Wu Tsai Neuroscience Institute, Stanford University (2023-2025)

Stanford Advisors

Lab Affiliations

All Publications

  • Task learning is subserved by a domain-general brain network. Cerebral cortex (New York, N.Y. : 1991) Yeon, J., Larson, A. S., Rahnev, D., D'Esposito, M. 2024


    One of the most important human faculties is the ability to acquire not just new memories but the capacity to perform entirely new tasks. However, little is known about the brain mechanisms underlying the learning of novel tasks. Specifically, it is unclear to what extent learning of different tasks depends on domain-general and/or domain-specific brain mechanisms. Here human subjects (n = 45) learned to perform 6 new tasks while undergoing functional MRI. The different tasks required the engagement of perceptual, motor, and various cognitive processes related to attention, expectation, speed-accuracy tradeoff, and metacognition. We found that a bilateral frontoparietal network was more active during the initial compared with the later stages of task learning, and that this effect was stronger for task variants requiring more new learning. Critically, the same frontoparietal network was engaged by all 6 tasks, demonstrating its domain generality. Finally, although task learning decreased the overall activity in the frontoparietal network, it increased the connectivity strength between the different nodes of that network. These results demonstrate the existence of a domain-general brain network whose activity and connectivity reflect learning for a variety of new tasks, and thus may underlie the human capacity for acquiring new abilities.

    View details for DOI 10.1093/cercor/bhae013

    View details for PubMedID 38282457

  • Quantifying the contribution of subject and group factors in brain activation. Cerebral cortex (New York, N.Y. : 1991) Nakuci, J., Yeon, J., Xue, K., Kim, J. H., Kim, S. P., Rahnev, D. 2023


    Research in neuroscience often assumes universal neural mechanisms, but increasing evidence points toward sizeable individual differences in brain activations. What remains unclear is the extent of the idiosyncrasy and whether different types of analyses are associated with different levels of idiosyncrasy. Here we develop a new method for addressing these questions. The method consists of computing the within-subject reliability and subject-to-group similarity of brain activations and submitting these values to a computational model that quantifies the relative strength of group- and subject-level factors. We apply this method to a perceptual decision-making task (n = 50) and find that activations related to task, reaction time, and confidence are influenced equally strongly by group- and subject-level factors. Both group- and subject-level factors are dwarfed by a noise factor, though higher levels of smoothing increases their contributions relative to noise. Overall, our method allows for the quantification of group- and subject-level factors of brain activations and thus provides a more detailed understanding of the idiosyncrasy levels in brain activations.

    View details for DOI 10.1093/cercor/bhad348

    View details for PubMedID 37771044

  • Overlapping and unique neural circuits are activated during perceptual decision making and confidence. Scientific reports Yeon, J., Shekhar, M., Rahnev, D. 2020; 10 (1): 20761


    The period of making a perceptual decision is often followed by a period of rating confidence where one evaluates the likely accuracy of the initial decision. However, it remains unclear whether the same or different neural circuits are engaged during periods of perceptual decision making and confidence report. To address this question, we conducted two functional MRI experiments in which we dissociated the periods related to perceptual decision making and confidence report by either separating their respective regressors or asking for confidence ratings only in the second half of the experiment. We found that perceptual decision making and confidence reports gave rise to activations in large and mostly overlapping brain circuits including frontal, parietal, posterior, and cingulate regions with the results being remarkably consistent across the two experiments. Further, the confidence report period activated a number of unique regions, whereas only early sensory areas were activated for the decision period across the two experiments. We discuss the possible reasons for this overlap and explore their implications about theories of perceptual decision making and visual metacognition.

    View details for DOI 10.1038/s41598-020-77820-6

    View details for PubMedID 33247212

    View details for PubMedCentralID PMC7699640

  • The suboptimality of perceptual decision making with multiple alternatives. Nature communications Yeon, J., Rahnev, D. 2020; 11 (1): 3857


    It is becoming widely appreciated that human perceptual decision making is suboptimal but the nature and origins of this suboptimality remain poorly understood. Most past research has employed tasks with two stimulus categories, but such designs cannot fully capture the limitations inherent in naturalistic perceptual decisions where choices are rarely between only two alternatives. We conduct four experiments with tasks involving multiple alternatives and use computational modeling to determine the decision-level representation on which the perceptual decisions are based. The results from all four experiments point to the existence of robust suboptimality such that most of the information in the sensory representation is lost during the transformation to a decision-level representation. These results reveal severe limits in the quality of decision-level representations for multiple alternatives and have strong implications about perceptual decision making in naturalistic settings.

    View details for DOI 10.1038/s41467-020-17661-z

    View details for PubMedID 32737317

    View details for PubMedCentralID PMC7395091

  • The Confidence Database. Nature human behaviour Rahnev, D., Desender, K., Lee, A. L., Adler, W. T., Aguilar-Lleyda, D., Akdoğan, B., Arbuzova, P., Atlas, L. Y., Balcı, F., Bang, J. W., Bègue, I., Birney, D. P., Brady, T. F., Calder-Travis, J., Chetverikov, A., Clark, T. K., Davranche, K., Denison, R. N., Dildine, T. C., Double, K. S., Duyan, Y. A., Faivre, N., Fallow, K., Filevich, E., Gajdos, T., Gallagher, R. M., de Gardelle, V., Gherman, S., Haddara, N., Hainguerlot, M., Hsu, T. Y., Hu, X., Iturrate, I., Jaquiery, M., Kantner, J., Koculak, M., Konishi, M., Koß, C., Kvam, P. D., Kwok, S. C., Lebreton, M., Lempert, K. M., Ming Lo, C., Luo, L., Maniscalco, B., Martin, A., Massoni, S., Matthews, J., Mazancieux, A., Merfeld, D. M., O'Hora, D., Palser, E. R., Paulewicz, B., Pereira, M., Peters, C., Philiastides, M. G., Pfuhl, G., Prieto, F., Rausch, M., Recht, S., Reyes, G., Rouault, M., Sackur, J., Sadeghi, S., Samaha, J., Seow, T. X., Shekhar, M., Sherman, M. T., Siedlecka, M., Skóra, Z., Song, C., Soto, D., Sun, S., van Boxtel, J. J., Wang, S., Weidemann, C. T., Weindel, G., Wierzchoń, M., Xu, X., Ye, Q., Yeon, J., Zou, F., Zylberberg, A. 2020; 4 (3): 317-325


    Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects.

    View details for DOI 10.1038/s41562-019-0813-1

    View details for PubMedID 32015487

    View details for PubMedCentralID PMC7565481