Sandia National Laboratories, Mail Stop 1327, P.O. Box 5800, Albuquerque, NM, 87185-1327, USA.
Cogn Res Princ Implic. 2024 Aug 26;9(1):56. doi: 10.1186/s41235-024-00586-2.
This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML outputs. In each of the experiments, participants were given the T and L task with T-shaped targets and L-shaped distractors. They were tasked with categorizing each image as target present or target absent. In Experiment 1, they performed this task without the aid of ML outputs. In Experiments 2-5, they were shown images with bounding boxes, representing the output of an ML model. The outputs could be correct (hits and correct rejections), or they could be erroneous (false alarms and misses). Experiment 2 manipulated the overall accuracy of these mock ML outputs. Experiment 3 manipulated the proportion of different types of errors. Experiments 4 and 5 manipulated the importance of specific types of stimuli or model errors, as well as the framing of the task in terms of human or model performance. These experiments showed that model misses were consistently harder for participants to detect than model false alarms. In general, as the model's performance increased, human performance increased as well, but in many cases the participants were more likely to overlook model errors when the model had high accuracy overall. Warning participants to be on the lookout for specific types of model errors had very little impact on their performance. Overall, our results emphasize the importance of considering human cognition when determining what level of model performance and types of model errors are acceptable for a given task.
这项研究探讨了在支持目标检测任务时提供正确和错误的机器学习 (ML) 输出对认知的影响。该研究由五个实验组成，这些实验操纵了模拟 ML 输出的准确性和重要性。在每个实验中，参与者都要完成 T 和 L 任务，其中 T 形目标和 L 形干扰物。他们的任务是对每张图像进行分类，判断是否存在目标。在实验 1 中，他们在没有 ML 输出辅助的情况下完成了这项任务。在实验 2-5 中，他们观看了带有边界框的图像，这些边界框代表了 ML 模型的输出。这些输出可以是正确的（命中和正确拒绝），也可以是错误的（误报和漏报）。实验 2 操纵了这些模拟 ML 输出的整体准确性。实验 3 操纵了不同类型错误的比例。实验 4 和 5 操纵了特定类型的刺激或模型错误的重要性，以及任务在人类或模型性能方面的表述方式。这些实验表明，模型漏报比模型误报更难被参与者发现。一般来说，随着模型性能的提高，人类的表现也会提高，但在许多情况下，当模型整体准确率较高时，参与者更有可能忽略模型错误。警告参与者要注意特定类型的模型错误对他们的表现影响很小。总的来说，我们的研究结果强调了在确定给定任务中可接受的模型性能水平和模型错误类型时，考虑人类认知的重要性。
