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

Abstract

Transitive inference is a form of reasoning that relies on prior knowledge and benefits survival in diverse species. While initially designed for humans, the transitive inference task has been adapted for mice, allowing for integration with various state-of-the-art manipulation and monitoring tools for mechanistic investigations. Existing paradigms, however, rely on manually presented stimuli, making them time-consuming, labor-intensive, and error-prone. Here, we introduce AutoTI, a fully automated behavioral apparatus that precisely controls the timing of task events and logs timestamps for events and responses. The automation also enables continuous, undisturbed monitoring of spontaneous behavior, allowing for detailed analyses of movement trajectory beyond basic accuracy metrics. Using AutoTI, we developed a robust training protocol that reliably achieved high success rates on transitive tests in mice. Notably, mice exhibited hallmark behavioral patterns seen in humans, including the symbolic distance effect and the serial position effect. AutoTI provides a cost-effective and scalable system for investigating the neurobiological basis of inferential reasoning. It also holds promise for translational research targeting its impairment in autism, schizophrenia, and Alzheimer’s disease, as well as advancing the reasoning capabilities of artificial intelligence.

Keywords: Cognitive map; Decision making; Mice; Reasoning; Relational memory; Schema.