Intelligence IS Cognitive Flexibility: Why Multilevel Models of Within-Individual Processes Are Needed to Realise This

Birney, Damian P.; Beckmann, Jens F.

Abstract

Despite substantial evidence for the link between an individual’s intelligence and successful life outcomes, questions about what defines intelligence have remained the focus of heated dispute. The most common approach to understanding intelligence has been to investigate what performance on tests of intellect is and is not associated with. This psychometric approach, based on correlations and factor analysis is deficient. In this review, we aim to substantiate why classic psychometrics which focus on between-person accounts will necessarily provide a limited account of intelligence until theoretical considerations of within-person accounts are incorporated. First, we consider the impact of entrenched psychometric presumptions that support the status quo and impede alternative views. Second, we review the importance of process-theories, which are critical for any serious attempt to build a within-person account of intelligence. Third, features of dynamic tasks are reviewed, and we outline how static tasks can be modified to target within-person processes. Finally, we explain how multilevel models are conceptually and psychometrically well-suited to building and testing within-individual notions of intelligence, which at its core, we argue is cognitive flexibility. We conclude by describing an application of these ideas in the context of microworlds as a case study.

Citation

Birney, D. P., & Beckmann, J. F. (2022). Intelligence IS Cognitive Flexibility: Why Multilevel Models of Within-Individual Processes Are Needed to Realise This. Journal of Intelligence, 10(3), https://doi.org/10.3390/jintelligence10030049

• Journal Article Type: Article
• Acceptance Date: Dec 26, 2022
• Online Publication Date: Aug 1, 2022
• Publication Date: 2022
• Deposit Date: Dec 12, 2022
• Publicly Available Date: Dec 12, 2022
• Journal: Journal of Intelligence
• Electronic ISSN: 2079-3200
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 3
• DOI: https://doi.org/10.3390/jintelligence10030049
• Public URL: https://durham-repository.worktribe.com/output/1185190