Mice in a labyrinth show rapid learning, sudden insight, and efficient exploration
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Curated by eLife
Evaluation Summary:
This study lays the groundwork for a new level of precision in understanding mouse navigation behaviour by studying complex decisions that approximate those made in the wild, but can nevertheless be analysed with mathematically precise tools. Several exciting observations are made about navigation strategy. The manuscript will therefore be of broad interest across behavioural neuroscience. However, in its current form, some questions remain about some of the major claims.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)
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Abstract
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.
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Joint Public Review:
Strengths & Overall Comments:
This behavioral study aims to provide an account of the spontaneous behavior of mice as they learn to explore a novel maze in search of a water reward. The authors analyze the trajectories of mice as they adapt to the labyrinth with particular focus on decisions taken at nodes and T junctions. They describe extremely rapid route learning to home and discontinuous exploratory learning or 'light bulb' moments as evident by instantaneous improvements in navigation performance. The authors capture most of the variance in their overall data with a predictive Markov models that could account for the much subsequent actions of the mouse as it moves from one node to the next. The study should be important to anyone who spends their time thinking about decision-making in mice. It highlights the …
Joint Public Review:
Strengths & Overall Comments:
This behavioral study aims to provide an account of the spontaneous behavior of mice as they learn to explore a novel maze in search of a water reward. The authors analyze the trajectories of mice as they adapt to the labyrinth with particular focus on decisions taken at nodes and T junctions. They describe extremely rapid route learning to home and discontinuous exploratory learning or 'light bulb' moments as evident by instantaneous improvements in navigation performance. The authors capture most of the variance in their overall data with a predictive Markov models that could account for the much subsequent actions of the mouse as it moves from one node to the next. The study should be important to anyone who spends their time thinking about decision-making in mice. It highlights the importance of considering ethologically relevant tasks for understanding decision making in rodent species.
In this submission, the authors introduce a new experimental paradigm for the study decision making in naturalistic contexts, presenting an opportunity to observe these dynamics away from the standard two-alternative-forced-choice paradigm. The application of modern tracking and posture analysis to maze exploration by rodents generates rich and interesting data, and allows the authors to do their experiments with many animals, and with nearly no human interference or specific instructions. The design of the maze is clever, using an underlying tree-like structure (with the tree folded so it precisely and fully occupies a rectangular area), and relatively deep (6 branching points from main trunk to a leaf node). Mice explore this voluntarily, and water-restricted mice learn to find water rewards at a leaf of the maze. The authors thus study truly voluntary and highly interesting complex behavior, and in a high-throughput way. By studying the dynamics of a mouse in a maze, the authors perform a careful set of analyses, describing discontinuous learning dynamics and the effects of history on decision-making. These results should be of interest to a wide group of behavioral neuroscientists that are attempting to understand the neural basis of how animals make decisions in complicated natural environments.
The data set released with this submission will be of broad use to the community, and we would not be surprised to see dozens of papers using it moving forward.
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Evaluation Summary:
This study lays the groundwork for a new level of precision in understanding mouse navigation behaviour by studying complex decisions that approximate those made in the wild, but can nevertheless be analysed with mathematically precise tools. Several exciting observations are made about navigation strategy. The manuscript will therefore be of broad interest across behavioural neuroscience. However, in its current form, some questions remain about some of the major claims.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)
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