Examining Personality and Decision-Making Style as Predictors of Automated Aid Efficiency

Automated diagnostic aids can improve decision making, yet operators often fail to use them effectively. This study examined whether personality traits and decision-making styles predict aid-use efficiency in a signal detection task. One hundred seventy-five undergraduate participants completed a signal detection task with and without a 93% reliable automated decision aid and completed measures of conscientiousness, neuroticism, satisficing, and decision difficulty. After preregistered exclusions, Bayesian analyses examined whether individual differences predicted aid-use efficiency, quantified as the ratio between observed and optimal aided performance. The aid significantly improved sensitivity, providing evidence of its diagnostic value. However, Bayesian correlations provided evidence against relationships between aid-use efficiency and conscientiousness, neuroticism, satisficing, or decision difficulty. Additional analyses showed that neuroticism was associated with greater decision difficulty, whereas conscientiousness was associated with lower decision difficulty. These findings suggest that individual differences in personality and decision style may not explain inefficiencies in automation use.