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DTSTAMP:20261009T184111Z
LOCATION:Summit Pavilion
DTSTART;TZID=America/Los_Angeles:20261021T173000
DTEND;TZID=America/Los_Angeles:20261021T183000
UID:HFESAM_ASPIRE 2026_sess257_POST163@linklings.com
SUMMARY:Examining Personality and Decision-Making Style as Predictors of A
 utomated Aid Efficiency
DESCRIPTION:Bochen Li (Oregon State University)\n\nAutomated diagnostic ai
 ds can improve performance in difficult detection tasks, yet people often 
 underuse them and fall short of optimal performance. We tested whether sta
 ble individual differences help explain this gap. Participants completed a
  signal detection task with and without a highly reliable (93%) automated 
 aid, then reported their personality traits (conscientiousness, neuroticis
 m) and decision-making tendencies (satisficing, decision difficulty). We c
 omputed each person’s aid-use efficiency by comparing their observed aided
  performance to the mathematically optimal level predicted by signal detec
 tion theory.\n\nAlthough the aid substantially improved performance overal
 l, individual differences did not predict how efficiently participants use
 d the aid. Bayesian analyses favored the null for all preregistered correl
 ations between aid-use efficiency and conscientiousness, neuroticism, sati
 sficing, and decision difficulty. Exploratory results showed expected rela
 tionships among the traits themselves, but these did not translate into di
 fferences in aid use.\n\nThese findings suggest that the common tendency t
 o underuse decision aids may reflect general cognitive limitations rather 
 than personality-based reliance strategies. For designers and practitioner
 s, this implies that improving human–automation teaming should focus on sy
 stem design and training that support effective integration for all users,
  rather than attempting to identify “better” aid users based on personalit
 y.\n\n
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