Timing Matters - The Role of Timing in Explanation Delivery

With the rapid advancements of autonomous systems and their integration into everyday life, explainability has become essential for fostering user trust and promoting effective human–system collaboration. However, the utility of explanations depends not only on content but also on timing. Prior research shows that pre-action explanations improve trust and understanding, yet the optimal timing remains unclear—especially under varying cognitive workloads.Building on our earlier theoretical framework based on the SEEV (Salience, Effort, Expectancy, Value) attention model, we empirically tested optimal timing through a two-phase interactive game. In Phase 1, participants completed a Reaction Time Determination task, responding to colour–word cues to establish a baseline for processing minimal instructions. In Phase 2, the Reactive Game, participants collected coins of a target colour indicated by a brief cue, requiring quick interpretation amid distractions.Seventeen participants (mean age 44.7 years, SD = 16.4) completed the study. Analysis of the gameplay data revealed an average reaction time of 2.58s to act on explanations—closely matching the 3s window predicted by our prior model. Subjective workload was evaluated using the NASA-TLX, which indicated moderate mental and temporal, low physical strain, and significant correlations between mental demand, effort, and frustration—highlighting the impact of timing on cognitive load.This study contributes to human-centred system design by providing evidence-based insights into optimising explanation timing for improved user comprehension and performance. The approach shows how explanation strategies can be informed by cognitive models and validated in interactive, user-centred settings. Future work will explore adaptive, context-aware explanations tailored to individual cognitive states.