Contextual inference through flexible integration of environmental features and behavioural outcomes

Jessica Passlack and Andrew F MacAskill

PLOS Computational Biology, 2026, vol. 22, issue 3, 1-42

Abstract: The ability to use context to flexibly adjust our decision-making is vital for navigating a complex world. To do this, the brain must both use environmental features and behavioural outcomes to distinguish between different, often hidden contexts; and also learn how to use these inferred contexts to guide behaviour. However, how these two interacting processes can be performed simultaneously remains unclear. Within the brain it is thought that interaction between the prefrontal cortex (PFC) and hippocampus (HPC) supports contextual inference. We show that models using environmental features (similar to those proposed to be implemented in hippocampus) readily support context-specific behaviour, but struggle to differentiate ambiguous contexts during learning. In contrast, models using behavioural outcomes (similar to those proposed in PFC) can stably differentiate contexts during periods of learning, but struggle to guide context-specific behaviour. We show that supporting feature-based with outcome-based strategies during learning overcomes the limitations of both approaches, allowing for the formation of distinct contextual representations that support contextual inference. Moreover, agents using this joint approach reproduce both behavioural- and cellular-level phenomena associated with the interaction between PFC and HPC. Together, these results provide insight into how the brain uses contextual information to guide flexible behaviour.Author summary: To behave flexibly, animals must determine which situation or context they are in, even when the information that defines that context is incomplete or no longer visible. This problem, known as contextual inference, is particularly challenging when different situations share similar sensory features and must be distinguished based on past experience. How the brain supports this process remains unclear. Here, we present a computational framework in which contextual inference emerges from the interaction between two complementary processes. One process uses environmental features to infer context, a function often associated with the hippocampus, while the other uses recent behavioural outcomes to infer context, a function linked to prefrontal systems involved in monitoring actions and consequences. We show that combining these sources of information during learning allows agents to form stable, context-specific representations even when sensory cues are weak, noisy, or transient. Applying this framework to sequential decision-making tasks, we find that joint inference reproduces key behavioural signatures of contextual learning and generates context-dependent activity patterns resembling hippocampal splitter cells. Disrupting outcome-based inference during learning selectively impairs the formation of these representations. Together, our results suggest that interactions between feature-based and outcome-based inference may support flexible behaviour when contextual information is uncertain.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1014093

DOI: 10.1371/journal.pcbi.1014093

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