Individual-specific strategies inform category learning

Categorization is an essential task for sensory perception. Individuals learn category labels using a variety of strategies to ensure that sensory signals, such as sounds or images, can be assigned to proper categories. Categories are often learned on the basis of extreme examples, and the boundary between categories can differ among individuals. The trajectories for learning also differ among individuals, as different individuals rely on different strategies, such as repeating or alternating choices. However, little is understood about the relationship between individual learning trajectories and learned categorization. To study this relationship, we trained mice to categorize auditory stimuli into two categories using a two-alternative forced choice task. Because the mice took several weeks to learn the task, we were able to quantify the time course of individual strategies and how they relate to how mice categorize stimuli around the categorization boundary. Different mice exhibited ..., This dataset contains raw behavioral data collected from 19 mice performing an auditory two-alternative forced choice categorization task, in which they turned a Lego wheel clockwise or counterclockwise to report whether a presented tone burst stimuli was perceived as being drawn from a low- or high-frequency distribution.
It also contains learning trajectories (parameterized by three evolving weights: \"Low Category Knowledge\", \"High Category Knowledge\" and \"Choice Bias\") extracted from the behavioral data using PsyTrack (Roy et al, 2021).
It also contains fitting results from:

Fitting psychometric curves to testing sessions using PyBADS (Singh et al, 2023)
Fitting two reinforcement learning models to training data
, , # Mouse 2AFC Categorization: Open Source Data for Collina et al, \"Individual-specific strategies in category learning inform learned boundaries\"

This data, necessary to reproduce figures, is available at [https://doi.org/10.5061/dryad.73n5tb359](https://doi.org/10.5061/dryad.73n5tb359)

## Description of the data and file structure

This data summarizes the behavior of mice performing a 2AFC task in which they were trained to respond to categorical stimuli using a wheel. The *trajectories* of their learning of the associations between wheel turns and stimulus categories were extracted and studied, and a computational model was used to understand the factors that might explain individual variability in learning trajectory.

The data is divided into 5 folders:

### 1. MouseData

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a. At the first level, MouseData is divided into folders based on the mouse ID. For example, the folder \"GS027\" contains all of the files pertaining to the behavior performed by the mouse GS027. Within that f...,