Jan Drugowitsch

Dr. Jan Drugowitsch

orcid

Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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14 March 2012 in The Journal of Neuroscience
Verified
Journal Article
Open access
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JDJan Drugowitsch
Jan Drugowitsch
RBRubén Moreno Bote
Rubén Moreno Bote
ACAnne K. Churchland
Anne K. Churchland
MSMichael N. Shadlen
Michael N. Shadlen
APAlexandre Pouget
Alexandre Pouget
Decision making often involves the accumulation of information over time, but acquiring information typically comes at a cost. Little is known about the cost incurred by animals and humans for acquiring additional information from sensory variables due, for instance, to attentional efforts. Through a novel integration of diffusion models and dynamic programming, we were able to estimate the cost of making additional observations per unit of time from two monkeys and six humans in a reaction time (RT) random-dot motion discrimination task. Surprisingly, we find that the cost is neither zero nor constant over time, but for the animals and humans features a brief period in which it is constant but increases thereafter. In addition, we show that our theory accurately matches the observed reaction time distributions for each stimulus condition, the time-dependent choice accuracy both conditional on stimulus strength and independent of it, and choice accuracy and mean reaction times as a function of stimulus strength. The theory also correctly predicts that urgency signals in the brain should be independent of the difficulty, or stimulus strength, at each trial.

23 February 2016 in Nature Neuroscience
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Journal Article
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APAlexandre Pouget
Alexandre Pouget
JDJan Drugowitsch
Jan Drugowitsch
AKAdam Kepecs
Adam Kepecs

18 August 2016 in Nature Communications
Verified
Journal Article
Open access
STSatohiro Tajima
Satohiro Tajima
JDJan Drugowitsch
Jan Drugowitsch
APAlexandre Pouget
Alexandre Pouget
For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models’ decision boundaries to collapse over time, and to depend on thea prioriknowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down.

1 December 2016 in Neuron
Verified
Journal Article
Closed access
JDJan Drugowitsch
Jan Drugowitsch
VWValentin Wyart
Valentin Wyart
ADAnne-Dominique Devauchelle
Anne-Dominique Devauchelle
EKEtienne Koechlin
Etienne Koechlin

14 June 2014 in eLife
Verified
Journal Article
Open access
JDJan Drugowitsch
Jan Drugowitsch
Gregory DeAngelisGregory DeAngelis
Gregory DeAngelis
EKEliana M Klier
Eliana M Klier
DADora E Angelaki
Dora E Angelaki
APAlexandre Pouget
Alexandre Pouget
Humans and animals can integrate sensory evidence from various sources to make decisions in a statistically near-optimal manner, provided that the stimulus presentation time is fixed across trials. Little is known about whether optimality is preserved when subjects can choose when to make a decision (reaction-time task), nor when sensory inputs have time-varying reliability. Using a reaction-time version of a visual/vestibular heading discrimination task, we show that behavior is clearly sub-optimal when quantified with traditional optimality metrics that ignore reaction times. We created a computational model that accumulates evidence optimally across both cues and time, and trades off accuracy with decision speed. This model quantitatively explains subjects's choices and reaction times, supporting the hypothesis that subjects do, in fact, accumulate evidence optimally over time and across sensory modalities, even when the reaction time is under the subject's control.

19 June 2020 in Science
Verified
Journal Article
Closed access
PDF file
NNNicole L. Neubarth
Nicole L. Neubarth
AEAlan J. Emanuel
Alan J. Emanuel
YLYin Liu
Yin Liu
MSMark W. Springel
Mark W. Springel
AHAnnie Handler
Annie Handler
DGDavid D. Ginty
David D. Ginty
Secrets of Meissner corpuscles: The Meissner corpuscle, a mechanosensory end organ, was discovered more than 165 years ago and has since been found in the glabrous skin of all mammals, including that on human fingertips. Although prominently featured in textbooks, the function of the Meissner corpuscle is unknown. Neubarth et al. generated adult mice without Meissner corpuscles and used them to show that these corpuscles alone mediate behavioral responses to, and perception of, gentle forces (see the Perspective by Marshall and Patapoutian). Each Meissner corpuscle is innervated by two molecularly distinct, yet physiologically similar, mechanosensory neurons. These two neuronal subtypes are developmentally interdependent and their endings are intertwined within the corpuscle. Both Meissner mechanosensory neuron subtypes are homotypically tiled, ensuring uniform and complete coverage of the skin, yet their receptive fields are overlapping and offset with respect to each other. Science , this issue p. eabb2751 ; see also p. 1311