From Prediction Accuracy to Prediction Use: A Monitoring-Centered Framework for Interactive Sport Expertise
Abstract
Action anticipation is widely recognized as a cornerstone of expert performance in interactive sport. Decades of research have shown that skilled performers predict upcoming actions more accurately than less-skilled performers by extracting kinematic cues and integrating them with contextual information. This literature has provided a powerful account of how anticipatory predictions are formed. Yet, interactive sport rarely ends when a prediction is generated, because opponents continue to move, sensory evidence changes, contextual priors may mislead, and performers must determine whether an initial prediction remains useful for action. In this narrative review, we argue that the field should move beyond anticipation accuracy toward the adaptive use of anticipation during unfolding interaction. Drawing on sport expertise research, predictive-processing accounts, performance-monitoring theory, observed-action error studies, and sport-specific neurophysiological evidence, we propose a monitoring-centered framework for interactive sport expertise. In this framework, anticipation generates probabilistic reference states, monitoring evaluates whether these states remain aligned with unfolding input, and functional discrepancy processing determines whether mismatch matters for action goals, affordances, and motor plans. We further argue that discrepancy processing is graded and functionally meaningful rather than binary. From this perspective, expert performers may not only predict better; they may also detect diagnostic mismatch more selectively, tolerate irrelevant variability, and couple action-relevant discrepancy information with sport-specific motor preparation and adjustment. The framework yields testable predictions for assessment, training, and future research on expert performance in sport.
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BibTeX
@misc{lu2026from,
title = {{From Prediction Accuracy to Prediction Use: A Monitoring-Centered Framework for Interactive Sport Expertise}},
author = {Yingzhi Lu and Mengkai Luan and Xiaoran Gao and Wenzi Wang and Chen Yiheng and Chenglin Zhou},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/kx5dp_v1},
url = {https://osf.io/preprints/psyarxiv/kx5dp_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - From Prediction Accuracy to Prediction Use: A Monitoring-Centered Framework for Interactive Sport Expertise
AU - Yingzhi Lu
AU - Mengkai Luan
AU - Xiaoran Gao
AU - Wenzi Wang
AU - Chen Yiheng
AU - Chenglin Zhou
PY - 2026
DA - 2026/09/26
PB - PsyArXiv
DO - 10.31234/osf.io/kx5dp_v1
UR - https://osf.io/preprints/psyarxiv/kx5dp_v1/
AB - Action anticipation is widely recognized as a cornerstone of expert performance in interactive sport. Decades of research have shown that skilled performers predict upcoming actions more accurately than less-skilled performers by extracting kinematic cues and integrating them with contextual information. This literature has provided a powerful account of how anticipatory predictions are formed. Yet, interactive sport rarely ends when a prediction is generated, because opponents continue to move, sensory evidence changes, contextual priors may mislead, and performers must determine whether an initial prediction remains useful for action. In this narrative review, we argue that the field should move beyond anticipation accuracy toward the adaptive use of anticipation during unfolding interaction. Drawing on sport expertise research, predictive-processing accounts, performance-monitoring theory, observed-action error studies, and sport-specific neurophysiological evidence, we propose a monitoring-centered framework for interactive sport expertise. In this framework, anticipation generates probabilistic reference states, monitoring evaluates whether these states remain aligned with unfolding input, and functional discrepancy processing determines whether mismatch matters for action goals, affordances, and motor plans. We further argue that discrepancy processing is graded and functionally meaningful rather than binary. From this perspective, expert performers may not only predict better; they may also detect diagnostic mismatch more selectively, tolerate irrelevant variability, and couple action-relevant discrepancy information with sport-specific motor preparation and adjustment. The framework yields testable predictions for assessment, training, and future research on expert performance in sport.
ER -