1. Computational and Systems Biology
  2. Neuroscience
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Mice in a labyrinth exhibit rapid learning, sudden insight, and efficient exploration

  1. Matthew Rosenberg
  2. Tony Zhang
  3. Pietro Perona  Is a corresponding author
  4. Markus Meister  Is a corresponding author
  1. California Institute of Technology, United States
Research Article
  • Cited 2
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Cite this article as: eLife 2021;10:e66175 doi: 10.7554/eLife.66175


Animals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete learning. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth and measure the dynamics of learning and the behaviors that enable it. A mouse in the labyrinth makes ~2000 navigation decisions per hour. The animal explores the maze, quickly discovers the location of a reward, and executes correct 10-bit choices after only 10 reward experiences - a learning rate 1000-fold higher than in 2AFC experiments. Many mice improve discontinuously from one minute to the next, suggesting moments of sudden insight about the structure of the labyrinth. The underlying search algorithm does not require a global memory of places visited and is largely explained by purely local turning rules.

Data availability

The behavioral data and code that produced the figures are available in a public Github repository cited in the article https://github.com/markusmeister/Rosenberg-2021-Repository. We are also preparing a permanent institutional repository.

The following data sets were generated

Article and author information

Author details

  1. Matthew Rosenberg

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    No competing interests declared.
  2. Tony Zhang

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    No competing interests declared.
  3. Pietro Perona

    Computation and Neural Systems, California Institute of Technology, Pasadena, United States
    For correspondence
    Competing interests
    No competing interests declared.
  4. Markus Meister

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    For correspondence
    Competing interests
    Markus Meister, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2136-6506


Simons Foundation (543015)

  • Markus Meister

Simons Foundation (543025)

  • Pietro Perona

National Science Foundation (1564330)

  • Pietro Perona


  • Pietro Perona

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


Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to animal protocol 1656 approved by the institutional animal care and use committee (IACUC) at Caltech.

Reviewing Editor

  1. Mackenzie W Mathis, EPFL, Switzerland

Publication history

  1. Received: December 31, 2020
  2. Accepted: June 30, 2021
  3. Accepted Manuscript published: July 1, 2021 (version 1)
  4. Version of Record published: July 21, 2021 (version 2)


© 2021, Rosenberg 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.


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