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 different trajectories in learning the task. Mice displayed preferences for a specific category, manifested by a choice bias in their responses, but this bias drifted with learning. We found that this drift in choice bias correlated with variability in the category boundary for sounds with ambiguous category membership. Next, we asked how stimulus-independent, individual-specific strategies informed learning. We found that the tendency to repeat choices, which is a form of perseveration, contributed to long-term learning. These results indicate that long-term trends in individual strategies during category learning affect learned category boundaries.

Methods
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