TAKEHARA ​LAB
 

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Cost-effective, open-source, automated apparatus for testing transitive inference in mice

 

Silvia Margarian, Yihan Chen, Jumana Waheed, Parham Rezaeimanesh, Vic Shao-Chih Chiang & Kaori Takehara-Nishiuchi

Scientific Reports

 

Reasoning is often thought of as a uniquely human ability, but many animals can make logical inferences. One example is transitive inference: if A is better than B, and B is better than C, we can infer that A is better than C—even without seeing that comparison directly. Studying this kind of reasoning in mice is powerful because it allows researchers to combine behavioral tasks with modern neuroscience tools that can monitor and manipulate brain circuits. However, traditional transitive inference tasks in rodents rely heavily on manual stimulus presentation, making experiments slow, labor-intensive, and prone to variability.

 

To address this challenge, we developed AutoTI, a cost-effective, open-source, fully automated apparatus for testing transitive inference in mice. This system allows precise control of task event timing, automatic logging of responses and timestamps, and continuous, undisturbed behavioral monitoring. Using this system, we established a robust training protocol that reliably produces successful transitive inference performance in mice. Interestingly, mice also show hallmark behavioral patterns previously observed in humans, including the symbolic distance effect and the serial position effect.

 

We hope this tool will help researchers investigate the neural mechanisms underlying inferential reasoning and support translational research on its impairment in conditions such as autism, schizophrenia, and Alzheimer’s disease. It may also inform efforts to improve reasoning in artificial intelligence systems.

Read the paper here.

Download the code and design files here.

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