Process Overlap Theory: A Unified Account of the General Factor of Intelligence

Kovács, Kristóf; Conway, Andrew R. A.

doi:10.1080/1047840x.2016.1153946

Cited by 355 publications

(395 citation statements)

References 194 publications

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“…Individual differences in WM capacity (WMC) have been shown to be strongly related to other higher-order cognitive abilities, including fluid intelligence, attention, shifting, inhibition (Kyllonen & Christal, 1990;Miyake et al, 2000;Miyake & Shah, 1999;Oberauer, Süß, Wilhelm, & Wittmann, 2008;Süß, Oberauer, Wittmann, Wilhelm, & Schulze, 2002), and a wide variety of complex everyday tasks (see Feldmann Barrett et al, 2004 for an overview). Based on the process overlap theory (Kovavs & Conway, 2016), the theoretical rationale behind WM training is that extensive practice on a set of WM tasks enhances not only WMC, but also transfers to non-trained but related cognitive tasks or abilities that share cognitive processes with WM.…”

Section: Working Memory Training In Older Adults: Bayesian Evidence Fmentioning

confidence: 99%

Working memory training in older adults: Bayesian evidence supporting the absence of transfer.

Guye¹,

Bastian²

2017

Psychology and Aging

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The question of whether working memory training leads to generalized improvements in untrained cognitive abilities is a longstanding and heatedly debated one. Previous research provides mostly ambiguous evidence regarding the presence or absence of transfer effects in older adults. Thus, to draw decisive conclusions regarding the effectiveness of working memory training interventions, methodologically sound studies with larger sample sizes are needed. In this study, we investigated whether or not a computer-based working memory training intervention induced near and far transfer in a large sample of 142 healthy older adults (65-80 years). Therefore, we randomly assigned participants to either the experimental group, which completed 25 sessions of adaptive, process-based working memory training, or to the active, adaptive visual search control group. Bayesian linear mixed-effects models were used to estimate performance improvements on the level of abilities, using multiple indicator tasks for near (working memory) and far transfer (fluid intelligence, shifting, and inhibition). Our data provided consistent evidence supporting the absence of near transfer to untrained working memory tasks and the absence of far transfer effects to all of the assessed abilities. Our results suggest that working memory training is not an effective way to improve general cognitive functioning in old age.

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Section: Working Memory Training In Older Adults: Bayesian Evidence Fmentioning

confidence: 99%

Working memory training in older adults: Bayesian evidence supporting the absence of transfer.

Guye¹,

Bastian²

2017

Psychology and Aging

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“…Bartholomew, Deary and Lawn (2009), and more recently Kovacs and Conway (2016) reintroduced the sampling theory of general intelligence originally proposed by Thorndike (1927) and Thomson (1916) [14][15][16][17]. In the sampling model, positive correlations between test scores are due to the fact that any two cognitive tests will always share some underlying basic processes.…”

Section: The Sampling Modelmentioning

confidence: 99%

Network Models for Cognitive Development and Intelligence

Maas

Kan

Marsman

et al. 2017

Preprint

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Cronbach's (1957) famous division of scientific psychology into two disciplines is still actual for the fields of cognition (general mechanisms) and intelligence (dimensionality of individual differences). The welcome integration of the two fields requires the construction of mechanistic models of cognition and cognitive development that explain key phenomena in individual differences research. In this paper we argue that network modeling is a promising approach to integrate the processes of cognitive development and (developing) intelligence into one unified theory. Network models are defined mathematically, describe mechanisms on the level of the individual, and are able to explain positive correlations among intelligence subtest scores -the empirical basis for the well-known g-factor -as well as more complex factorial structures. Links between network modeling, factor modeling and item response theory allow for a common metric, encompassing both discrete and continuous characteristics, for cognitive development and intelligence.

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“…One such class of models involve feedback loops between cognitive processes (e.g., Kovacs & Conway, 2016;Van der Maas et al, 2006). Although these models have their own limitations (see, e.g., Gignac, 2014;Oberauer, 2016;Protzko, 2015), they come with the same magnitude of causal assumptions that may be individually tested.…”

Section: Nonhierarchical Modelsmentioning

confidence: 99%

“…As an example, the underlying assumption of one such nonhierarchical model is that g emerges from different overlapping cognitive processes (Kovacs & Conway, 2016). The extent of this emergence is dictated by the weakest executive processthe weaker the process, the lower the emergent g (really, positive manifold).…”

Section: Nonhierarchical Modelsmentioning

confidence: 99%

“…), although the high ability makes it unlikely. To give a hierarchical model built on between-subjects differences a within-subjects interpretation (John has a high probability of being right because he has a high g) is called the local homogeneity assumption (see the M-IRT model in Kovacs & Conway, 2016, for an explicit model of intelligence granting this assumption).…”

Section: Between-and Within-subjects Modelingmentioning

confidence: 99%

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Effects of cognitive training on the structure of intelligence

Protzko

2016

Psychon Bull Rev

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Targeted cognitive training, such as n-back or speed of processing training, in the hopes of raising intelligence is of great theoretical and practical importance. The most important theoretical contribution, however, is not about the malleability of intelligence. Instead, I argue the most important and novel theoretical contribution is understanding the causal structure of intelligence. The structure of intelligence, most often taken as a hierarchical factor structure, necessarily prohibits transfer from subfactors back up to intelligence. If this is the true structure, targeted cognitive training interventions will fail to increase intelligence not because intelligence is immutable, but simply because there is no causal connection between, say, working memory and intelligence. Seeing the structure of intelligence for what it is, a causal measurement model, allows us to focus testing on the presence and absence of causal links. If we can increase subfactors without transfer to other facets, we may be confirming the correct causal structure more than testing malleability. Such a blending into experimental psychometrics is a strong theoretical pursuit.

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Process Overlap Theory: A Unified Account of the General Factor of Intelligence

Cited by 355 publications

References 194 publications

Working memory training in older adults: Bayesian evidence supporting the absence of transfer.

Working memory training in older adults: Bayesian evidence supporting the absence of transfer.

Network Models for Cognitive Development and Intelligence

Effects of cognitive training on the structure of intelligence

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