Cognitive abilities and trust calibration: Understanding individual differences in automation trust dynamics

Individual differences in cognitive abilities may influence how people form and adjust trust in automated systems, yet the mechanisms underlying this relationship remain poorly understood. This study investigated how three foundational cognitive abilities - working memory (WM), attention control (AC), and fluid intelligence (Gf) - shape trust dynamics, particularly focusing on trust decline and recovery after automation failures. Participants (N = 111) completed cognitive tests measuring WM, AC, and Gf before performing a dual-task paradigm where they monitored an automated memory aid exhibiting varying reliability levels (100%-65%-100%) while completing an arithmetic task. Results revealed that WM and Gf positively predicted trust recovery after automation failures, while AC influenced long-term trust calibration, with high-AC individuals showing more nuanced trust adjustments reflecting system reliability history. Trust decline was uniform across ability levels, suggesting that trust loss may occur rapidly after salient failures. The relationship between abilities and trust may have been partially mediated by automation schemas, with WM negatively correlating with all-or-none thinking about automation. These findings suggest different cognitive abilities play distinct roles in trust dynamics: WM supports monitoring automation performance, Gf enables effective schema updating, and AC facilitates appropriate deployment of these resources for trust calibration. This work advances our understanding of trust mechanisms and has implications for designing automation interfaces that accommodate individual cognitive differences.