ArticlebioRxiv : the preprint server for biology2025
Decoding hidden goal-directed navigational states and their neuronal representations using a novel labyrinth paradigm and probabilistic modeling framework.
Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Goal-directed navigation involves a sequence of planned actions aimed at achieving long-term goals through reinforcement, but detecting hidden states that support this process and their neuronal substrates remains a fundamental challenge. To address this, we developed a complex labyrinth test that mimics naturalistic foraging and implemented a novel hierarchical probabilistic modeling framework, Cognitive Mapping of Planned Actions with State Spaces (CoMPASS). This framework infers a nested state structure, comprising short-term surveillance-ambulation states (Level 1) and long-term goal-oriented navigational states (Level 2). Using CoMPASS, we show that successful navigation in wild-type mice is marked by increased recruitment of both surveillance and goal-oriented states specifically at decision nodes, revealing how sequential behavioral decisions culminate in long-term goals. In contrast, the humanized
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