期刊
COGNITION
卷 242, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.cognition.2023.105659
关键词
Confidence; Face recognition; Metacognition; Individual differences
Many studies have shown that competence influences an individual's ability to monitor their item-level performance. The debate about how to explain these individual differences in metacognition persists. This study investigated the competence-based account, the performance-based account, and the metacognitive awareness account. The results showed that objectively stronger face recognizers displayed better discrimination and calibration in confidence ratings compared to weaker recognizers. Additionally, participants with greater self-assessed ability used higher levels of confidence, regardless of trial accuracy. These findings support the competence-based account.
Many studies show that competence (e.g., skill, expertise, natural ability) influences individuals' capabilities of monitoring their item-level performance. However, debate persists about how best to explain these individual differences in metacognition. The competence-based account ascribes differences in monitoring to individuals' objective ability level, arguing that the same skills necessary to perform a task are required to effectively monitor performance. The performance-based account attributes differences in monitoring to changes in overall task performance - no individual differences in competence required. Finally, the metacognitive awareness account proposes that alignment between an individuals' self-assessed and objective ability leads to differences in monitoring. In this study, 603 participants completed a self-assessment of face recognition ability, a lineup identification task, and an objective assessment of face recognition ability. We manipulated the number of encoding repetitions and delay between encoding and test to produce varying levels of task performance across objective face recognition ability. Following each lineup decision, participants provided both a numeric confidence rating and a written expression of verbal confidence. We transformed verbal confidence into a quantitative value using machine learning techniques. When matched on overall identification accuracy, objectively stronger face recognizers used numeric and verbal confidence that a) better discriminates between correct and filler lineup identifications than weaker recognizers, and b) shows better calibration to accuracy. Participants with greater self-assessed ability used higher levels of confidence, irrespective of trial accuracy. These results support the competence-based account.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据