Jan Drugowitsch
Jan Drugowitsch, PhD
Assistant Professor of Neurobiology

Every decision and behavior is haunted by uncertainty, introduced by the noisy and ambiguous nature of the world that surrounds us. Despite this, we make such decisions with seeming ease. Our goal is to understand the fundamental computations and their neurobiological implementations that allow the nervous system to support such efficient behavior.

By using tools from machine learning and neuroscience, we approach this goal by developing theories about how networks of neurons are able to infer, represent, and process the state of the world, and how this processing leads to competent decisions. These theories are on one hand guided by statistical principles and the approximations required to keep the resulting computations tractable. On the other hand, they are constrained by and scrutinized in the light of our knowledge of the architecture of the nervous systems, and by observed behavior and neural activity.

Current research focuses on decisions based on perceptual evidence. We have previously shown that, in this context, decision-makers are able to trade off the time they contemplate such decisions with their accuracy in a close-to-optimal manner. This was demonstrated in rather restricted situations, and the current aim is to extend theories and collect behavioral evidence that pushes the boundary towards the realism of every-day decisions. We are further asking how decision strategies change once these decisions are between options of different intrinsic values, and what might be the computations involved to make up one’s mind about these values.

In the close future, we plan on extending our investigations not only to decisions that require statistical computations of higher complexity, but also to address behavior of higher dimensions, such as spatial navigation under uncertainty.

 

Publications View
Interacting with volatile environments stabilizes hidden-state inference and its brain signatures.
Authors: Authors: Weiss A, Chambon V, Lee JK, Drugowitsch J, Wyart V.
Nat Commun
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Optimal policy for attention-modulated decisions explains human fixation behavior.
Authors: Authors: Jang AI, Sharma R, Drugowitsch J.
Elife
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Human visual motion perception shows hallmarks of Bayesian structural inference.
Authors: Authors: Yang S, <a href="https://connects.catalyst.harvard.edu/Profiles/profile/87841848">Bill J</a>, <a href="https://connects.catalyst.harvard.edu/Profiles/profile/59923576">Drugowitsch J</a>, Gershman SJ.
Sci Rep
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Scaling of sensory information in large neural populations shows signatures of information-limiting correlations.
Authors: Authors: Kafashan M, Jaffe AW, Chettih SN, Nogueira R, Arandia-Romero I, Harvey CD, Moreno-Bote R, Drugowitsch J.
Nat Commun
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Distributional Reinforcement Learning in the Brain.
Authors: Authors: Lowet AS, Zheng Q, Matias S, Drugowitsch J, Uchida N.
Trends Neurosci
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Hierarchical structure is employed by humans during visual motion perception.
Authors: Authors: Bill J, Pailian H, Gershman SJ, Drugowitsch J.
Proc Natl Acad Sci U S A
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Heuristics and optimal solutions to the breadth-depth dilemma.
Authors: Authors: Moreno-Bote R, Ramírez-Ruiz J, Drugowitsch J, Hayden BY.
Proc Natl Acad Sci U S A
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Meissner corpuscles and their spatially intermingled afferents underlie gentle touch perception.
Authors: Authors: Neubarth NL, Emanuel AJ, Liu Y, Springel MW, Handler A, Zhang Q, Lehnert BP, Guo C, Orefice LL, Abdelaziz A, DeLisle MM, Iskols M, Rhyins J, Kim SJ, Cattel SJ, Regehr W, Harvey CD, Drugowitsch J, Ginty DD.
Science
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The impact of learning on perceptual decisions and its implication for speed-accuracy tradeoffs.
Authors: Authors: Mendonça AG, Drugowitsch J, Vicente MI, DeWitt EEJ, Pouget A, Mainen ZF.
Nat Commun
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Control of Synaptic Specificity by Establishing a Relative Preference for Synaptic Partners.
Authors: Authors: Xu C, Theisen E, Maloney R, Peng J, Santiago I, Yapp C, Werkhoven Z, Rumbaut E, Shum B, Tarnogorska D, Borycz J, Tan L, Courgeon M, Griffin T, Levin R, Meinertzhagen IA, de Bivort B, Drugowitsch J, Pecot MY.
Neuron
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