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J. Intell., Volume 4, Issue 4 (December 2016)

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Research

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Open AccessArticle Modeling Mental Speed: Decomposing Response Time Distributions in Elementary Cognitive Tasks and Correlations with Working Memory Capacity and Fluid Intelligence
J. Intell. 2016, 4(4), 13; doi:10.3390/jintelligence4040013
Received: 18 April 2016 / Revised: 24 August 2016 / Accepted: 15 September 2016 / Published: 14 October 2016
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Abstract
Previous research has shown an inverse relation between response times in elementary cognitive tasks and intelligence, but findings are inconsistent as to which is the most informative score. We conducted a study (N = 200) using a battery of elementary cognitive tasks,
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Previous research has shown an inverse relation between response times in elementary cognitive tasks and intelligence, but findings are inconsistent as to which is the most informative score. We conducted a study (N = 200) using a battery of elementary cognitive tasks, working memory capacity (WMC) paradigms, and a test of fluid intelligence (gf). Frequently used candidate scores and model parameters derived from the response time (RT) distribution were tested. Results confirmed a clear correlation of mean RT with WMC and to a lesser degree with gf. Highly comparable correlations were obtained for alternative location measures with or without extreme value treatment. Moderate correlations were found as well for scores of RT variability, but they were not as strong as for mean RT. Additionally, there was a trend towards higher correlations for slow RT bands, as compared to faster RT bands. Clearer evidence was obtained in an ex-Gaussian decomposition of the response times: the exponential component was selectively related to WMC and gf in easy tasks, while mean response time was additionally predictive in the most complex tasks. The diffusion model parsimoniously accounted for these effects in terms of individual differences in drift rate. Finally, correlations of model parameters as trait-like dispositions were investigated across different tasks, by correlating parameters of the diffusion and the ex-Gaussian model with conventional RT and accuracy scores. Full article
(This article belongs to the Special Issue Mental Speed and Response Times in Cognitive Tests)
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Review

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Open AccessReview Use of Response Time for Measuring Cognitive Ability
J. Intell. 2016, 4(4), 14; doi:10.3390/jintelligence4040014
Received: 11 May 2016 / Revised: 19 September 2016 / Accepted: 24 October 2016 / Published: 1 November 2016
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Abstract
The purpose of this paper is to review some of the key literature on response time as it has played a role in cognitive ability measurement, providing a historical perspective as well as covering current research. We discuss the speed-level distinction, dimensions of
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The purpose of this paper is to review some of the key literature on response time as it has played a role in cognitive ability measurement, providing a historical perspective as well as covering current research. We discuss the speed-level distinction, dimensions of speed and level in cognitive abilities frameworks, speed–accuracy tradeoff, approaches to addressing speed–accuracy tradeoff, analysis methods, particularly item response theory-based, response time models from cognitive psychology (ex-Gaussian function, and the diffusion model), and other uses of response time in testing besides ability measurement. We discuss several new methods that can be used to provide greater insight into the speed and level aspects of cognitive ability and speed–accuracy tradeoff decisions. These include item-level time limits, the use of feedback (e.g., CUSUMs), explicit scoring rules that combine speed and accuracy information (e.g., count down timing), and cognitive psychology models. We also review some of the key psychometric advances in modeling speed and level, which combine speed and ability measurement, address speed–accuracy tradeoff, allow for distinctions between response times on items responded to correctly and incorrectly, and integrate psychometrics with information-processing modeling. We suggest that the application of these models and tools is likely to advance both the science and measurement of human abilities for theory and applications. Full article
(This article belongs to the Special Issue Mental Speed and Response Times in Cognitive Tests)
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