Are impulsivity and intelligence truly related constructs? Evidence based on the fixed-links model

Lozano, José H.

doi:10.1016/j.paid.2015.04.049

Cited by 18 publications

(10 citation statements)

References 26 publications

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“…Alderton & Larson, 1990;Arthur & Woehr, 1993). RAPM may not comprise a fully homogenous test taping solely reasoning ability, but the items' position and difficulty (both strongly correlating in the original paper-and-pencil variant) may contribute to additional variance that may constitute another dimension (see also Lozano, 2015;Ren et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Metacognitive experience on Raven’s matrices versus insight problems

Chuderski

Jastrzębski

Kroczek

et al. 2020

Metacognition Learning

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Participants rated Intuition, Suddenness, Pleasure, and Certainty accompanying their solutions to items of a popular fluid intelligence test – Raven’s Advanced Progressive Matrices (RAPM) – that varied from easy (around 80% correct) to difficult (around 20% correct). The same ratings were collected from four insight problems interleaved with RAPM. Suddenness and Certainty substantially decreased from easy to difficult matrices (Pleasure strongly overlapped with Certainty). In easy matrices, subjective experience matched that observed during insight problems, suggesting the highly fluent processing resulting in vivid and univocal solutions. By contrast, processing difficult matrices seemed to involve effortful incremental combination of complex information that yielded uncertain outcomes, resembling full-blown analytic problems. Only Intuition, generally rated low, was unaffected by RAPM difficulty. These results suggest that RAPM constitutes a heterogeneous test, with easy vs. difficult items involving relatively distinct types of processing. This novel knowledge can help in understanding the processes underlying solving Raven’s matrices. The study also contributes to the understanding of the validity of subjective ratings as measures of metacognition.

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Section: Discussionmentioning

confidence: 99%

Metacognitive experience on Raven’s matrices versus insight problems

Chuderski

Jastrzębski

Kroczek

et al. 2020

Metacognition Learning

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show abstract

“…For example, there are a number of studies demonstrating that completing a homogeneous set of achievement items stimulates the item-position effect (e.g., Birney, Beckmann, Beckmann, & Double, 2017;Debeer & Janssen, 2013;Debeer, Buchholz, Hartig, & Janssen, 2014;Embretson, 1991;Hartig & Buchholz, 2012;Kubinger, 2008;Lozano, 2015;Ren et al, 2014;Schweizer, Schreiner, & Gold, 2009;Troche et al, 2016;Verguts & De Boeck, 2000;. A special characteristic of this effect is that it leads to a systematic increase of the proportion of systematic variance from the first to last items (Knowles, 1988).…”

Section: Discussionmentioning

confidence: 99%

Approaches for Structural Investigations of Binary Data Using Confirmatory Factor Models

Schweizer¹,

Reiß²,

Troche³

2018

IJSP

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An investigation of the suitability of threshold-based and threshold-free approaches for structural investigations of binary data is reported. Both approaches implicitly establish a relationship between binary data following the binomial distribution on one hand and continuous random variables assuming a normal distribution on the other hand. In two simulation studies we investigated: whether the fit results confirm the establishment of such a relationship, whether the differences between correct and incorrect models are retained and to what degree the sample size influences the results. Both approaches proved to establish the relationship. Using the threshold-free approach it was achieved by customary ML estimation whereas robust ML estimation was necessary in the threshold-based approach. Discrimination between correct and incorrect models was observed for both approaches. Larger CFI differences were found for the threshold-free approach than for the threshold-based approach. Dependency on sample size characterized the threshold-based approach but not the threshold-free approach. The threshold-based approach tended to perform better in large sample sizes, while the threshold-free approach performed better in smaller sample sizes.

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“…Some recent studies provide supporting evidence (e.g., Shipstead et al, 2014 ; Unsworth et al, 2014 ), while others fail to do so (e.g., Friedman et al, 2006 ; Rey-Mermet et al, 2019 ). A possible reason for these inconsistent results are the method effects, such as the effect of latent processing speed ( Chuderski, 2013 , 2015 ) and the effect of item-position ( Lozano, 2015 ; Ren et al, 2017 ), which may impair the quality of fluid reasoning measurement. In order to rule out impairment due to such method effects, we investigated the relationships between inhibition, WM, and fluid reasoning using a refined representation of fluid reasoning.…”

Section: Introductionmentioning

confidence: 99%

An Investigation on How Inhibition in Cognitive Processing Contributes to Fluid Reasoning

Wang¹,

Li²,

Wei³

et al. 2020

ACP

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This article reports an investigation of how inhibition contributes to fluid reasoning when it is decomposed into the reasoning ability, item-position, and speed components to control for possible method effects. Working memory was also taken into consideration. A sample of 223 university students completed a fluid reasoning scale, two tasks tapping prepotent response inhibition, and two working memory tasks. Fixed-links modeling was used to separate the effect of reasoning ability from the effects of item-position and speed. The goodness-of-fit results confirmed the necessity to consider the reasoning ability, item-position, and speed components simultaneously. Prepotent response inhibition was only associated with reasoning ability. This association disappeared when working memory served as a mediator. Taken together, these results reflect the inhomogeneity of what is tapped by the fluid reasoning scale on one hand and, on the other, suggest inhibition as an important component of working memory.

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Are impulsivity and intelligence truly related constructs? Evidence based on the fixed-links model

Cited by 18 publications

References 26 publications

Metacognitive experience on Raven’s matrices versus insight problems

Metacognitive experience on Raven’s matrices versus insight problems

Approaches for Structural Investigations of Binary Data Using Confirmatory Factor Models

An Investigation on How Inhibition in Cognitive Processing Contributes to Fluid Reasoning

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