Sequential Contextual Fit Predicts Human Behavioural and Neural Dynamics Across Domains

Abstract:Human perception, action and decision making unfold in sequences, but computational predictors are often domain-specific. This study computes and tests sequential contextual fit (SCF), an embedding-based measure of how well a current information state matches its recent context. The metric uses a simple recency-weighted similarity kernel and can be applied to words, sounds, visual scenes, affective states, choices, actions and neural representations. Across language processing, music-evoked emotion, a subset of audiovisual emotion EEG data, gambling decisions, human activity recognition and decision-related EEG, lower contextual fit predicted longer processing times, larger affective or behavioural transitions and stronger neural-state changes. These effects remained after controlling for established predictors including surprisal, reinforcement-learning prediction error, acoustic change, visual change and sensor change. SCF therefore provides a computational measurement layer for relating contextual compatibility to behavioural processing and cognitive/neural state-transition dynamics.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.20179 [cs.AI]

(or arXiv:2609.20179v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.20179 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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