Joshua I. Gold, PhD

faculty photo
Professor of Neuroscience
Co-Director, Computational Neuroscience Initiative
Department: Neuroscience
Graduate Group Affiliations

Contact information
D407 Richards
3610 Hamilton Walk
University of Pennsylvania School of Medicine
Philadelphia, PA 19104-6060
Office: (215) 746-0028
Fax: (215) 573-9050
Sc.B. (Neural Sciences)
Brown University , 1991.
Ph.D. (Neurosciences)
Stanford University, 1997.
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Description of Research Expertise

Many aspects of higher brain function rely on two closely related capacities, inference and learning. Inference is the process of drawing conclusions from uncertain data, like forming a percept from noisy sensory information or predicting the most rewarding future outcome from the recent history of outcomes. These inferences often inform decisions that determine behavior. Learning uses experience to shape how these kinds of inference and decision processes function, often optimizing them to meet particular goals. Recent work has begun to identify how and where in the brain inference processes are implemented, particularly in the service of perceptual and reward-based decision-making. Research in my laboratory focuses on how these processes are shaped by learning to provide the flexibility a decision-maker needs to survive in a complex and dynamic world.

We use several complementary approaches to study this complex issue.

1) Quantitative measures of behavior (“psychophysics”) combined with non-invasive measures of physiological variables like pupil diameter in human subjects. These studies allow us to prototype new behavioral tasks, identify and quantify interesting behaviors, and begin to make inferences about and understand the underlying neural mechanisms.

2) Psychophysics and electrophysiology in non-human primates. These studies allow us to test directly ideas about the relationship between neural activity in a particular brain region or regions and behavior.

3) Computational modeling. These studies help to define optimal limits on behavior, characterize relationships between behavioral and neural data, and identify particular computations that can drive complex behaviors.

The goal of our work is to provide new insights into the neural mechanisms that govern complex, learned behaviors and ultimately translate these insights into new approaches to understand, diagnose, and treat disorders of learning and cognition.

Selected Publications

Piasini E, Filipowicz ALS, Levine J, Gold JI: Embo: a Python package for empirical data analysis using the Information Bottleneck. Journal of Open Research Software 9(10), 2021 Notes: DOI:

Fan Y, Gold JI, Ding L: Frontal eye field and caudate neurons make different contributions to reward-biased perceptual decisions. eLife Nov 2020.

Kao C-H, Lee S, Gold JI, Kable JW: Neural encoding of task-dependent errors during adaptive learning. eLife Page: 1-20, Oct 2020.

Doi T, Fan Y, Gold JI, Ding L: The caudate nucleus contributes causally to decisions that balance reward and uncertain visual information. eLife Jun 2020.

Filipowicz ALS, Glaze CM, Kable JW, Gold JI: Pupil diameter encodes the idiosyncratic, cognitive complexity of belief updating. eLife May 2020.

Kao C-H, Khambhati AN, Bassett DS, Nassar MR, McGuire JT, Gold JI, Kable JW: Functional brain network reconfiguration during learning in a dynamic environment. Nature Communications 11, 2020.

Lee S, Gold JI, Kable JW: The human as delta-rule learner. Decision 7(1), 2020.

Li YS, Nassar MR, Kable JW, Gold JI: Individual neurons in the cingulate cortex encode action monitoring, not selection, during adaptive decision-making. The Journal of Neuroscience 39(34): 6668-6683, Aug 2019.

Tsunada J, Cohen YE*, Gold JI* (*contributed equally): Post-decision processing in primate prefrontal cortex influences subsequent choices on an auditory decision-making task. eLife 8, Jun 2019.

Fan Y, Gold JI, Ding L: Ongoing, rational calibration of reward-driven perceptual biases. eLife 7, Oct 2018 Notes: PMCID: PMC6203438

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Last updated: 07/12/2021
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