Disentangling the effects of processing speed on the association between age differences and fluid intelligence

• Verfasst von: Schubert, Anna-Lena [VerfasserIn]
• Verfasst von: Hagemann, Dirk [VerfasserIn]
• Verfasst von: Löffler, Christoph [VerfasserIn]
• Titel: Disentangling the effects of processing speed on the association between age differences and fluid intelligence
• Verf.angabe: Anna-Lena Schubert, Dirk Hagemann, Christoph Löffler and Gidon T. Frischkorn
• Jahr: 2020
• Jahr des Originals: 2019
• Umfang: 20 S.
• Fussnoten: Gesehen am 13.01.2020 ; Published: 25 December 2019
• Titel Quelle: Enthalten in: Journal of Intelligence
• Ort Quelle: Basel : MDPI, 2013
• Jahr Quelle: 2020
• Band/Heft Quelle: 8(2020,1) Artikel-Nummer 1, 20 Seiten
• ISSN Quelle: 2079-3200
• DOI: doi:10.3390/jintelligence8010001
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: aging
• Sach-SW: diffusion model
• Sach-SW: EEG
• Sach-SW: ERP
• Sach-SW: intelligence
• Sach-SW: mental speed
• Sach-SW: reaction time
• K10plus-PPN: 1687233756

Abstract

Several studies have demonstrated that individual differences in processing speed fully mediate the association between age and intelligence, whereas the association between processing speed and intelligence cannot be explained by age differences. Because measures of processing speed reflect a plethora of cognitive and motivational processes, it cannot be determined which specific processes give rise to this mediation effect. This makes it hard to decide whether these processes should be conceived of as a cause or an indicator of cognitive aging. In the present study, we addressed this question by using a neurocognitive psychometrics approach to decompose the association between age differences and fluid intelligence. Reanalyzing data from two previously published datasets containing 223 participants between 18 and 61 years, we investigated whether individual differences in diffusion model parameters and in ERP latencies associated with higher-order attentional processing explained the association between age differences and fluid intelligence. We demonstrate that individual differences in the speed of non-decisional processes such as encoding, response preparation, and response execution, and individual differences in latencies of ERP components associated with higher-order cognitive processes explained the negative association between age differences and fluid intelligence. Because both parameters jointly accounted for the association between age differences and fluid intelligence, age-related differences in both parameters may reflect age-related differences in anterior brain regions associated with response planning that are prone to be affected by age-related changes. Conversely, age differences did not account for the association between processing speed and fluid intelligence. Our results suggest that the relationship between age differences and fluid intelligence is multifactorially determined.