# Sequential Contextual Fit Predicts Human Behavioural and Neural Dynamics Across Domains

arXiv: https://arxiv.org/abs/2609.20179 · HTML: https://mlsanity.com/paper/2609.20179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 85 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior](https://mlsanity.com/paper/2006.01912/) (2020)
- [Toward a realistic model of speech processing in the brain with self-supervised learning](https://mlsanity.com/paper/2206.01685/) (2022)
- [Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables](https://mlsanity.com/paper/1811.08127/) (2018)
- [Attention-aware semantic relevance predicting Chinese sentence reading](https://mlsanity.com/paper/2403.18542/) (2024)
- [Contextual Semantic Relevance and Word Surprisal Predict N400 and P600 Dynamics During Naturalistic Reading](https://mlsanity.com/paper/2607.04107/) (2026)
- [Contextual Semantic Relevance Tracks fMRI BOLD Responses During Naturalistic Speech Comprehension](https://mlsanity.com/paper/2607.15856/) (2026)

## Abstract (arXiv)

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.