Processing speed, working memory, and executive functions: Independent or inter-related predictors of general intelligence

Frischkorn, Gidon T.; Schubert, Anna‐Lena; Hagemann, Dirk

doi:10.1016/j.intell.2019.05.003

“…For this purpose, we used a neurocognitive psychometrics account of mental speed to decompose the association between age differences and fluid intelligence. Reanalyzing data from two previously published datasets (Frischkorn et al 2019;Schubert et al 2017), we investigated whether individual differences in diffusion model parameters and in ERP latencies associated with higher-order attentional processing explained the negative association between age differences and fluid intelligence. Moreover, we investigated if the phenomenon that age does not account for the association between processing speed and intelligence can be generalized to electrophysiological and model-based measures of processing speed.…”

Section: The Present Studymentioning

confidence: 99%

“…In the study by Schubert et al (2017), conditions were presented blockwise with 10 practice and 100 experimental trials each and the order of conditions was counterbalanced across participants. In the study by Frischkorn et al (2019), conditions were presented blockwise with 10 practice and 40 experimental trials each and the order of conditions was kept constant across participants. The Posner letter matching task consisted of two conditions: The physical identity (PI) condition, in which participants had to decide whether two letters were physically identical, and the name identity (NI) condition, in which participants had to decide whether two letters were semantically identical.…”

Section: Experimental Tasksmentioning

confidence: 99%

“…Conditions were presented blockwise and the order of conditions (PI first) was kept constant across participants. In the study by Schubert et al (2017), participants completed 10 practice and 300 experimental trials of each condition, whereas in the study by Frischkorn et al (2019), participants completed 10 practice and 40 experimental trials of each condition.…”

Section: Experimental Tasksmentioning

confidence: 99%

See 1 more Smart Citation

Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence

Schubert

¹

,

Hagemann

²

,

Löffler

³

et al. 2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…However, experimentally validated difference score measures of cognitive control are often task-specific, show low reliabilities, and show little variation between individuals (Gärtner & Strobel, 2019;Hedge et al, 2018;Rey-Mermet et al, 2018;Rouder & Haaf, 2019). For these reasons, correlations between difference score measures of cognitive control and fluid intelligence are typically lower than those of mean performance measures and often fail to reach statistical significance at all (e.g., Friedman et al, 2006;Frischkorn et al, 2019;Rey-Mermet et al, 2019).…”

Section: Limitations Of Behavioral Measures Of Cognitive Controlmentioning

confidence: 99%

A chronometric model of the relationship between frontal midline theta functional connectivity and human intelligence

Schubert¹,

Hagemann²,

Löffler³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Individual differences in cognitive control have been suggested to act as a domain-general bottleneck constraining performance in a variety of cognitive ability measures including but not limited to fluid intelligence, working memory capacity, and processing speed. However, due to psychometric problems associated with the measurement of individual differences in cognitive control, it has been challenging to empirically test the assumption that individual differences in cognitive control underlie individual differences in cognitive abilities. In the present study, we addressed these issues by analyzing the chronometry of intelligence-related differences in midfrontal global theta connectivity, which has been shown to reflect cognitive control functions. We demonstrate in a sample of 98 adults, who completed a cognitive control task while their EEG was recorded, that individual differences in mid-frontal global theta connectivity during stages of higher-order information-processing explained 65 percent of the variance in fluid intelligence. In comparison, task-evoked theta connectivity during earlier stages of information processing was not related to fluid intelligence. These results suggest that more intelligent individuals benefit from an adaptive modulation of theta-band synchronization during the time-course of information processing. Moreover, they emphasize the role of interregional goal-directed informationprocessing for cognitive control processes in human intelligence and support theoretical accounts of intelligence which propose that individual differences in cognitive control processes give rise to individual differences in cognitive abilities.

show abstract

“…For this purpose, we used a neurocognitive psychometrics account of mental speed to decompose the association between age differences and fluid intelligence. Reanalyzing data from two previously published datasets [51,63], we investigated whether individual differences in diffusion model parameters and in ERP latencies associated with higher-order attentional processing explained the negative association between age differences and fluid intelligence. Moreover, we investigated if the phenomenon that age does not account for the association between processing speed and intelligence can be generalized to electrophysiological and model-based measures of processing speed.…”

Section: The Present Studymentioning

confidence: 99%

Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence

Schubert¹,

Hagemann²,

Löffler³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

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. Data and analysis code are available at https://osf.io/hy5fw/.

show abstract

Processing speed, working memory, and executive functions: Independent or inter-related predictors of general intelligence

Cited by 58 publications

References 106 publications

Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence

Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence

A chronometric model of the relationship between frontal midline theta functional connectivity and human intelligence

Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence

Contact Info

Product

Resources

About