Cognitive workload is assumed to influence performance due to resource competition. However, there is a lack of evidence for a direct relationship between changes in workload within an individual over time and changes in that individual’s performance. We collected performance data using a multiple object-tracking task in which we measured workload objectively in real-time using a modified detection response task. Using a multi-level Bayesian model controlling for task difficulty and past performance, we found strong evidence that workload both during and preceding a tracking trial was predictive of performance, such that higher workload led to poorer performance. These negative workload-performance relationships were remarkably consistent across individuals. Importantly, we demonstrate that fluctuations in workload independent from the task demands accounted for significant performance variation. The outcomes have implications for designing real-time adaptive systems to proactively mitigate human performance decrements, but also highlight the pervasive influence of cognitive workload more generally.

由于资源竞争，假设认知工作负载会影响性能。然而，缺乏证据表明个人工作量的变化之间存在直接关系。随着时间的推移以及个人表现的变化。我们使用多对象跟踪任务收集性能数据，在该任务中，我们使用修改后的检测响应任务客观地实时测量工作负载。使用控制任务难度和过去表现的多级贝叶斯模型，我们发现强有力的证据表明，在跟踪试验期间和之前的工作量可以预测性能，因此更高的工作量会导致更差的性能。这些负面的工作负载-绩效关系在个人之间非常一致。重要的是，我们证明了独立于任务需求的工作量波动导致显着的性能变化。结果对设计实时自适应系统以主动减轻人类绩效下降具有影响，但也更普遍地突出了认知工作量的普遍影响。