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Computational Neuroscience Journal Club

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  • UserMate Lengyel and Jasmine Stone
  • ClockTuesday 01 June 2021, 15:00-16:30
  • HouseOnline on Zoom.

If you have a question about this talk, please contact Jake Stroud.

Please join us for our fortnightly journal club online via zoom where two presenters will jointly present a topic together. The next topic is ‘Inference noise in reward-guided learning’ presented by Mate Lengyel and Jasmine Stone.

Zoom information: Meeting ID: 841 9788 6178 Passcode: 659046

Making decisions in uncertain and volatile environments is something humans and animals must do as they interact with the world. These decisions are variable and often suboptimal. Computational noise has been proposed as a source of this variability. However, there may be some benefits to computational noise in decision making as well — in particular, humans and animals are much more adaptable than current AI systems with exact (noise-free) computations, and computation noise may have more of an effect in higher volatility environment where this adaptability is more important.

We present a set of four studies; two show computational noise as a bug corrupting optimal decisions, while two show computational noise as a feature, enabling adaptation in volatile environments.

List of references:


Drugowitsch, J., Wyart, V., Devauchelle, A.-D., & Koechlin, E. (2016). Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality. Neuron, 92(6), 1398–1411.

Findling, C., Skvortsova, V., Dromnelle, R., Palminteri, S., & Wyart, V. (2019). Computational noise in reward-guided learning drives behavioral variability in volatile environments. Nature Neuroscience, 22(12), 2066–2077.


Findling, C., Chopin, N. & Koechlin, E. Imprecise neural computations as a source of adaptive behaviour in volatile environments. Nat Hum Behav 5, 99–112 (2021).

Findling, C., & Wyart, V. (2020). Computation noise promotes cognitive resilience to adverse conditions during decision-making [Preprint]. Neuroscience.



Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. E., & Pouget, A. (2012). Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability. Neuron, 74(1), 30–39.

Wyart, V., & Koechlin, E. (2016). Choice variability and suboptimality in uncertain environments. Current Opinion in Behavioral Sciences, 11, 109–115.


Findling, C., & Wyart, V. (2021). Computation noise in human learning and decision-making: Origin, impact, function. Current Opinion in Behavioral Sciences, 38, 124–132.

This talk is part of the Computational Neuroscience series.

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