Confidence-guided updating of choice bias during perceptual decisions is a widespread behavioral phenomenon

  1. Armin Lak  Is a corresponding author
  2. Emily Hueske
  3. Junya Hirokawa
  4. Paul Masset
  5. Torben Ott
  6. Anne E Urai
  7. Tobias H Donner
  8. Matteo Carandini
  9. Susumu Tonegawa
  10. Naoshige Uchida
  11. Adam Kepecs  Is a corresponding author
  1. University College London, United Kingdom
  2. MIT, United States
  3. Doshisha University, Japan
  4. Cold Spring Harbor Laboratory, United States
  5. University Medical Center Hamburg-Eppendorf, Germany
  6. The Picower Institute for Learning and Memory, RIKEN-MIT Center for Neural Circuit Genetics, Massachusetts Institute of Technology, United States
  7. Harvard University, United States

Abstract

Learning from past successes and failures improves decisions to produce appropriate actions in each perceived situation. However, reinforcement learning is not thought to be engaged during well-trained perceptual decision tasks, —after task acquisition is complete and performance is stable—, since choice accuracy is limited by perception. We report a novel form of reinforcement learning during perceptual decisions: past rewards bias future perceptual choices specifically when the previous stimulus was difficult to judge, and the confidence in obtaining the reward was low. We identified this phenomenon in six datasets from four laboratories, across mice, rats and humans, and sensory modalities from olfaction and audition to vision. We show that reinforcement learning models incorporating decision confidence into their teaching signal explain this choice updating. Thus, reinforcement learning mechanisms are continually engaged to produce systematic adjustments of choices even in well-learned perceptual decisions in order to optimize behavior in an uncertain world.

Data availability

The data used in this study is available at http://dx.doi.org/10.6084/m9.figshare.4300043

The following previously published data sets were used

Article and author information

Author details

  1. Armin Lak

    Institute of Ophthalmology, University College London, London, United Kingdom
    For correspondence
    armin.lak@dpag.ox.ac.uk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1926-5458
  2. Emily Hueske

    Picower Institute, MIT, Cambridge, United States
    Competing interests
    No competing interests declared.
  3. Junya Hirokawa

    Graduate School of Brain Science, Doshisha University, Kyotanabe, Japan
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1238-5713
  4. Paul Masset

    Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2001-7515
  5. Torben Ott

    Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  6. Anne E Urai

    Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5270-6513
  7. Tobias H Donner

    Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Competing interests
    Tobias H Donner, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7559-6019
  8. Matteo Carandini

    UCL Institute of Ophthalmology, University College London, London, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4880-7682
  9. Susumu Tonegawa

    Department of Brain and Cognitive Sciences, Department of Biology, The Picower Institute for Learning and Memory, RIKEN-MIT Center for Neural Circuit Genetics, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    No competing interests declared.
  10. Naoshige Uchida

    Center for Brain Science, Harvard University, Cambridge, United States
    Competing interests
    Naoshige Uchida, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5755-9409
  11. Adam Kepecs

    Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    For correspondence
    akepecs@wustl.edu
    Competing interests
    No competing interests declared.

Funding

Wellcome (106101)

  • Armin Lak

Wellcome (213465)

  • Armin Lak

National Institutes of Health (R01 MH110404)

  • Naoshige Uchida

National Institutes of Health (R01MH097061 and R01DA038209)

  • Naoshige Uchida

Wellcome (205093)

  • Matteo Carandini

Deutsche Forschungsgemeinschaft (DO 1240/2-1 and DO 1240/3-1)

  • Tobias H Donner

RIKEN-CBS

  • Emily Hueske
  • Susumu Tonegawa

JPB Foundation

  • Emily Hueske
  • Susumu Tonegawa

Howard Hughes Medical Institute

  • Emily Hueske
  • Susumu Tonegawa

German Academic Exchange Service

  • Anne E Urai

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: The experimental procedures were approved by Institutional committees at Cold Spring Harbor Laboratory (for experiments on rats), MIT and Harvard University (for mice auditory experiments) and were in accordance with National Institute of Health standards (project ID: 18-14-11-08-1). Experiments on mice visual decisions were approved by the home Office of the United Kingdom (license 70/8021). Experiments in humans were approved by the ethics committee at the University of Amsterdam (project ID: 2014­-BC­-3376).

Human subjects: The ethics committee at the University of Amsterdam approved the study, and all observers gave their informed consent.project ID: 2014-BC-3376

Copyright

© 2020, Lak et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 7,656
    views
  • 1,160
    downloads
  • 91
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Armin Lak
  2. Emily Hueske
  3. Junya Hirokawa
  4. Paul Masset
  5. Torben Ott
  6. Anne E Urai
  7. Tobias H Donner
  8. Matteo Carandini
  9. Susumu Tonegawa
  10. Naoshige Uchida
  11. Adam Kepecs
(2020)
Confidence-guided updating of choice bias during perceptual decisions is a widespread behavioral phenomenon
eLife 9:e49834.
https://doi.org/10.7554/eLife.49834

Share this article

https://doi.org/10.7554/eLife.49834

Further reading

    1. Neuroscience
    Ankur Sinha, Padraig Gleeson ... Robin Angus Silver
    Tools and Resources

    Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites, and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes makes the construction and reuse of biologically detailed models challenging. A wide range of tools have been developed to aid their construction and simulation, but differences in design and internal representation act as technical barriers to those who wish to use data-driven models in their research workflows. NeuroML, a model description language for computational neuroscience, was developed to address this fragmentation in modeling tools. Since its inception, NeuroML has evolved into a mature community standard that encompasses a wide range of model types and approaches in computational neuroscience. It has enabled the development of a large ecosystem of interoperable open-source software tools for the creation, visualization, validation, and simulation of data-driven models. Here, we describe how the NeuroML ecosystem can be incorporated into research workflows to simplify the construction, testing, and analysis of standardized models of neural systems, and supports the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, thus promoting open, transparent and reproducible science.

    1. Neuroscience
    Sharon Inberg, Yael Iosilevskii ... Benjamin Podbilewicz
    Research Article

    Dendrites are crucial for receiving information into neurons. Sensory experience affects the structure of these tree-like neurites, which, it is assumed, modifies neuronal function, yet the evidence is scarce, and the mechanisms are unknown. To study whether sensory experience affects dendritic morphology, we use the Caenorhabditis elegans' arborized nociceptor PVD neurons, under natural mechanical stimulation induced by physical contacts between individuals. We found that mechanosensory signals induced by conspecifics and by glass beads affect the dendritic structure of the PVD. Moreover, developmentally isolated animals show a decrease in their ability to respond to harsh touch. The structural and behavioral plasticity following sensory deprivation are functionally independent of each other and are mediated by an array of evolutionarily conserved mechanosensory amiloride-sensitive epithelial sodium channels (degenerins). Calcium imaging of the PVD neurons in a micromechanical device revealed that controlled mechanical stimulation of the body wall produces similar calcium dynamics in both isolated and crowded animals. Our genetic results, supported by optogenetic, behavioral, and pharmacological evidence, suggest an activity-dependent homeostatic mechanism for dendritic structural plasticity, that in parallel controls escape response to noxious mechanosensory stimuli.