发表机构
Tongji University; Tuebingen University（同济大学; 图宾根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出序列上下文匹配度（SCF）度量，基于嵌入和近期加权相似性核，跨多个领域预测人类行为与神经动态，且效果独立于既有预测器。
AI 中文摘要
人类的感知、行动和决策以序列方式展开，但计算预测器通常是领域特定的。本研究计算并测试了序列上下文匹配度（SCF），这是一种基于嵌入的度量，用于衡量当前信息状态与其近期上下文的匹配程度。该度量使用简单的近期加权相似性核，可应用于词语、声音、视觉场景、情感状态、选择、动作和神经表征。在语言处理、音乐诱发情绪、视听情绪脑电图数据子集、赌博决策、人类活动识别和决策相关脑电图中，较低的上下文匹配度预测了更长的处理时间、更大的情感或行为转变以及更强的神经状态变化。在控制了包括惊奇度、强化学习预测误差、声学变化、视觉变化和传感器变化在内的既有预测器后，这些效应仍然存在。因此，SCF提供了一个计算测量层，用于将上下文兼容性与行为处理及认知/神经状态转换动态联系起来。
英文摘要
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.
