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This study examined the impact of experience on individuals’ dependence behavior and response strategies when interacting with imperfect automation. 41 participants used an automated aid to complete a dual-task scenario comprising of a compensatory tracking task and a threat detection task. The entire experiment was divided into four quarters and multi-level models (MLM) were built to investigate the relationship between experience and the dependent variables. Results show that compliance and reliance behaviors and perfor- mance scores significantly increased as participants gained more experience with automation. In addition, as the experiment progressed, a significant number of participants adapted to the automation and resorted to an extreme use response strategy. The findings of this study suggest that automation response strategies are not static and most individual operators eventually follow or discard the automation. Understanding individual response strategies can support the development of individualized automation systems and improve operator training. 

We conducted a meta-analysis to determine how people blindly comply with, rely on, and depend on diagnostic automation. We searched three databases using combinations of human behavior keywords with automation keywords. The period ranges from January 1996 to June 2021. In total, 8 records and a total of 68 data points were identified. As data points were nested within research records, we built multi-level models (MLM) to quantify relationships between blind compliance and positive predictive value (PPV), blind reliance and negative predictive value (NPV), and blind dependence and overall success likelihood (OSL).Results show that as the automation’s PPV, NPV, and OSL increase, human operators are more likely to blindly follow the automation’s recommendation. Operators appear to adjust their reliance behaviors more than their compliance and dependence. We recommend that researchers report specific automation trial information (i.e., hits, false alarms, misses, and correct rejections) and human behaviors (compliance and reliance) rather than automation OSL and dependence. Future work could examine how operator behaviors change when operators are not blind to raw data. Researchers, designers, and engineers could leverage understanding of operator behaviors to inform training procedures and to benefit individual operators during repeated automation use.