Automating licking bias correction in a two-choice delayed match-to-sample task to accelerate learning

Area of Science:

• Neuroscience
• Animal Behavior

Background:

• Choice bias in animals can hinder learning in tasks like the delayed match-to-sample (DMS).
• Traditional DMS tasks using two-alternative lickports are valuable for studying cognitive functions but are limited by training time due to side bias.

Purpose of the Study:

• To implement and evaluate an automated system for correcting side bias in head-fixed mice during olfactory DMS tasks.
• To investigate the effectiveness of dynamic adjustments in lickport position and trial reward ratios in mitigating biased licking responses.

Main Methods:

• An automated side bias correction system was developed for olfactory DMS tasks.
• The system dynamically adjusted lickport distances and the ratio of left- vs. right-rewarded trials based on licking behavior.
• The correction algorithm repositioned lickports and altered trial proportions to counterbalance bias.

Main Results:

• The automated system effectively reduced side bias in mice by adjusting lickport distances and trial reward proportions.
• These adjustments correlated with significant improvements in behavioral performance during the DMS task.
• The implemented correction strategy proved successful in mitigating biased licking responses.

Conclusions:

• The automated side bias correction system is a valuable tool for improving the efficiency and applicability of two-alternative choice tasks in animal research.
• This method can accelerate training and enhance the study of sensory processing, working memory, and associative memory.