Benchmarking automation-aided performance in a forensic face matching task
重新分析Carragher和Hancock(2023)的数据，用贝叶斯分层信号检测模型评估自动化辅助人脸匹配的效率，发现人机协作表现高度低效，接近最差的自动化依赖模型。
Carragher and Hancock (2023) investigated how individuals performed in a one-to-one face matching task when assisted by an Automated Facial Recognition System (AFRS). Across five pre-registered experiments they found evidence of suboptimal aided performance, with AFRS-assisted individuals consistently failing to reach the level of performance the AFRS achieved alone. The current study reanalyses these data (Carragher and Hancock, 2023), to benchmark automation-aided performance against a series of statistical models of collaborative decision making, spanning a range of efficiency levels. Analyses using a Bayesian hierarchical signal detection model revealed that collaborative performance was highly inefficient, falling closest to the most suboptimal models of automation dependence tested. This pattern of results generalises previous reports of suboptimal human-automation interaction across a range of visual search, target detection, sensory discrimination, and numeric estimation decision-making tasks. The current study is the first to provide benchmarks of automation-aided performance in the one-to-one face matching task.