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DTSTAMP:20261012T210905Z
LOCATION:Summit Pavilion
DTSTART;TZID=America/Los_Angeles:20261021T173000
DTEND;TZID=America/Los_Angeles:20261021T183000
UID:HFESAM_ASPIRE 2026_sess257_POST220@linklings.com
SUMMARY:Navigating AI Errors: The Role of Mental Models in Human-AI Team P
 erformance
DESCRIPTION:Danielle Sanchez, Caitlin Lancaster, and Ryan Villarreal (Sand
 ia National Laboratories)\n\nPerformance of human-AI teams depends on both
  AI’s overall accuracy and human’s ability to detect errors to form an acc
 urate understanding of system behaviors. Less prior research has compared 
 how different patterns of AI errors (i.e., when and how they occur) shapes
  users’ mental models that underpin human oversight. To address this gap, 
 we conducted an experiment (N=380) exploring how the error patterns (betwe
 en subjects) influence individuals' ability to detect AI errors and develo
 p effective mental models for system accuracy in a phishing email classifi
 cation task. Our findings demonstrate that while different error patterns 
 did not significantly influence participants’ ability to detect AI errors,
  it did shape their perceptions of how the AI performed over the interacti
 on. These findings contribute to the HFES literature by demonstrating that
  error pattern effects are more evident in users’ interpretations of syste
 m behavior than in their detection performance. This distinction advances 
 theoretical models of monitoring and mental model development in AI-suppor
 ted decision environments.   \n\nSNL is managed and operated by NTESS unde
 r DOE NNSA contract DE-NA0003525. SAND2026-17363A.\n\n
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