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Mental Speed and Response Times in Cognitive Tests

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A special issue of Journal of Intelligence (ISSN 2079-3200).

Deadline for manuscript submissions: closed (31 March 2016) | Viewed by 105291

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Prof. Dr. Oliver Wilhelm

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Guest Editor

Institute for Psychology and Pedagogy, Ulm University, Albert-Einstein-Allee 47, 89081 Ulm, Germany
Interests: fluid intelligence; working memory; mental speed; innovative measurement approaches; ability related self-report constructs

Special Issue Information

Dear Colleagues,

Mental speed has had many roles in the history of intelligence research, ranging from an irrelevant nuisance variable to the mechanism underlying general intelligence. Mental speed is the subject of or essential in many important methodological controversies in the field of ability research, including the speed-accuracy trade-off and mathematical modeling of human choice. Mental speed has been used as a construct label and is also known under the labels “processing speed”, “elementary cognitive speed”, “clerical speed”, etc., and it should, or could, have been used as such for many popular attention measures still in use today. Mental speed, however, was also used as a tool to express mental work, for example time required per correct response or number of correct responses per time unit. Although recent models in item-response theory or simplified diffusion models greatly facilitate the analysis and understanding of speed data, these approaches are rarely used in individual difference settings. The status of mental speed in the realm of human intelligence is therefore surprisingly vague. This Special Issue solicits contributions addressing at least one of the following fields:

New methods to analyze cognitive speed
The generality and coherence of mental speed
Mental speed and intelligence structure
Mental speed in applied settings

Prof. Dr. Oliver Wilhelm
Guest Editor

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Keywords

Mental speed
Processing speed
Clerical speed
Response times
Reaction times
Reaction time modeling
Chronometry
Attention
Decay
Intelligence structure

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Published Papers (11 papers)

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3210 KiB

Open AccessArticle

Paper-Based Assessment of the Effects of Aging on Response Time: A Diffusion Model Analysis

by Judith Dirk, Gesa Katharina Kratzsch, John P. Prindle, Ulf Kröhne, Frank Goldhammer and Florian Schmiedek

J. Intell. 2017, 5(2), 12; https://doi.org/10.3390/jintelligence5020012 - 10 Apr 2017

Cited by 6 | Viewed by 8809

Abstract

The effects of aging on response time were examined in a paper-based lexical-decision experiment with younger (age 18–36) and older (age 64–75) adults, applying Ratcliff’s diffusion model. Using digital pens allowed the paper-based assessment of response times for single items. Age differences previously reported by Ratcliff and colleagues in computer-based experiments were partly replicated: older adults responded more conservatively than younger adults and showed a slowing of their nondecision components of RT by 53 ms. The rates of evidence accumulation (drift rate) showed no age-related differences. Participants with a higher score in a vocabulary test also had higher drift rates. The experiment demonstrates the possibility to use formal processing models with paper-based tests. Full article

(This article belongs to the Special Issue Mental Speed and Response Times in Cognitive Tests)

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1784 KiB

Open AccessArticle

Modeling Mental Speed: Decomposing Response Time Distributions in Elementary Cognitive Tasks and Correlations with Working Memory Capacity and Fluid Intelligence

by Florian Schmitz and Oliver Wilhelm

J. Intell. 2016, 4(4), 13; https://doi.org/10.3390/jintelligence4040013 - 14 Oct 2016

Cited by 46 | Viewed by 9617

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, 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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1836 KiB

Open AccessArticle

Cognitive Aging in the Seattle Longitudinal Study: Within-Person Associations of Primary Mental Abilities with Psychomotor Speed and Cognitive Flexibility

by Gizem Hülür, Nilam Ram, Sherry L. Willis, K. Warner Schaie and Denis Gerstorf

J. Intell. 2016, 4(3), 12; https://doi.org/10.3390/jintelligence4030012 - 14 Sep 2016

Cited by 8 | Viewed by 9538

Abstract

It has long been proposed that cognitive aging in fluid abilities is driven by age-related declines of processing speed. Although study of between-person associations generally supports this view, accumulating longitudinal between-person and within-person evidence indicates less strong associations between speed and fluid cognitive performance. Initial evidence also suggests that cognitive flexibility may explain within-person variability in cognitive performance. In the present study, we used up to nine waves of data over 56 years from a subsample of 582 participants of the Seattle Longitudinal Study to examine (a) within-person associations of psychomotor speed and cognitive flexibility with cognitive aging in primary mental abilities (including inductive reasoning, number ability, verbal meaning, spatial orientation, and word fluency); and (b) how these within-person associations change with age. In line with the processing speed theory, results revealed that within persons, primary mental abilities (including fluid, crystallized, and visualization measures) were indeed associated with psychomotor speed. We also observed age-related increases in within-person couplings between primary mental abilities and psychomotor speed. While the processing speed theory focuses primarily on associations with fluid abilities, age-related increases in coupling were found for a variety of ability domains. Within-person associations between primary mental abilities and cognitive flexibility were weaker and relatively stable with age. We discuss the role of speed and flexibility for cognitive aging. Full article

(This article belongs to the Special Issue Mental Speed and Response Times in Cognitive Tests)

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1819 KiB

Open AccessArticle

Sometimes More Is Better, and Sometimes Less Is Better: Task Complexity Moderates the Response Time Accuracy Correlation

by Nicolas Becker, Florian Schmitz, Anja S. Göritz and Frank M. Spinath

J. Intell. 2016, 4(3), 11; https://doi.org/10.3390/jintelligence4030011 - 25 Aug 2016

Cited by 12 | Viewed by 7666

Abstract

This study addresses the relationship between item response time and item accuracy (i.e., the response time accuracy correlation, RTAC) in figural matrices tests. The dual processing account of response time effects predicts negative RTACs in tasks that allow for relatively automatic processing and positive RTACs in tasks that require controlled processing. Contrary to these predictions, several studies found negative RTACs for reasoning tests. Nevertheless, it was demonstrated that the RTAC is moderated by task complexity (i.e., the interaction between person ability and item difficulty) and that under conditions of high complexity (i.e., low ability and high difficulty) the RTAC was even slightly positive. The goal of this study was to demonstrate that with respect to task complexity the direction of the RTAC (positive vs. negative) can change substantially even within a single task paradigm (i.e., figural matrices). These predictions were tested using a figural matrices test that employs a constructed response format and has a broad range of item difficulties in a sample with a broad range of ability. Confirming predictions, strongly negative RTACs were observed when task complexity was low (i.e., fast responses tended to be correct). With increasing task complexity, the RTAC flipped to be strongly positive (i.e., slow responses tended to be correct). This flip occurred earlier for people with lower ability, and later for people with higher ability. Cognitive load of the items is suggested as an explanation for this phenomenon. Full article

(This article belongs to the Special Issue Mental Speed and Response Times in Cognitive Tests)

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3464 KiB

Open AccessArticle

Response Mixture Modeling of Intraindividual Differences in Responses and Response Times to the Hungarian WISC-IV Block Design Test

by Dylan Molenaar, Maria Bolsinova, Sandor Rozsa and Paul De Boeck

J. Intell. 2016, 4(3), 10; https://doi.org/10.3390/jintelligence4030010 - 4 Aug 2016

Cited by 11 | Viewed by 7306

Abstract

Response times may constitute an important additional source of information about cognitive ability as it enables to distinguishing between different intraindividual response processes. In this paper, we present a method to disentangle interindividual variation from intraindividual variation in the responses and response times of 978 subjects to the 14 items of the Hungarian WISC-IV Block Design test. It is found that faster and slower responses differ in their measurement properties suggesting that there are intraindivual differences in the response processes adopted by the subjects. Full article

(This article belongs to the Special Issue Mental Speed and Response Times in Cognitive Tests)

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353 KiB

Open AccessArticle

The Worst Performance Rule as Moderation: New Methods for Worst Performance Analysis

by Gidon T. Frischkorn, Anna-Lena Schubert, Andreas B. Neubauer and Dirk Hagemann

J. Intell. 2016, 4(3), 9; https://doi.org/10.3390/jintelligence4030009 - 20 Jul 2016

Cited by 9 | Viewed by 7122

Abstract

Worst performance in cognitive processing tasks shows larger relationships to general intelligence than mean or best performance. This so called Worst Performance Rule (WPR) is of major theoretical interest for the field of intelligence research, especially for research on mental speed. In previous research, the increases in correlations between task performance and general intelligence from best to worst performance were mostly described and not tested statistically. We conceptualized the WPR as moderation, since the magnitude of the relation between general intelligence and performance in a cognitive processing task depends on the performance band or percentile of performance. On the one hand, this approach allows testing the WPR for statistical significance and on the other hand, it may simplify the investigation of possible constructs that may influence the WPR. The application of two possible implementations of this approach is shown and compared to results of a traditional worst performance analysis. The results mostly replicate the WPR. Beyond that, a comparison of results on the level of unstandardized relationships (e.g., covariances or unstandardized regression weights) to results on the level of standardized relationships (i.e., correlations) indicates that increases in the inter-individual standard deviation from best to worst performance may play a crucial role for the WPR. Altogether, conceptualizing the WPR as moderation provides a new and straightforward way to conduct Worst Performance Analysis and may help to incorporate the WPR more prominently into empirical practice of intelligence research. Full article

(This article belongs to the Special Issue Mental Speed and Response Times in Cognitive Tests)

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693 KiB

Open AccessArticle

Predicting Fluid Intelligence by Components of Reaction Time Distributions from Simple Choice Reaction Time Tasks

by Yoanna Schulz-Zhecheva, Manuel C. Voelkle, André Beauducel, Monica Biscaldi and Christoph Klein

J. Intell. 2016, 4(3), 8; https://doi.org/10.3390/jintelligence4030008 - 18 Jul 2016

Cited by 20 | Viewed by 10082

Abstract

Mean reaction times (RT) and the intra-subject variability of RT in simple RT tasks have been shown to predict higher-order cognitive abilities measured with psychometric intelligence tests. To further explore this relationship and to examine its generalizability to a sub-adult-aged sample, we administered different choice RT tasks and Cattell’s Culture Fair Intelligence Test (CFT 20-R) to n = 362 participants aged eight to 18 years. The parameters derived from applying Ratcliff’s diffusion model and an ex-Gaussian model to age-residualized RT data were used to predict fluid intelligence using structural equation models. The drift rate parameter of the diffusion model, as well as σ of the ex-Gaussian model, showed substantial predictive validity regarding fluid intelligence. Our findings demonstrate that stability of performance, more than its mere speed, is relevant for fluid intelligence and we challenge the universality of the worst performance rule observed in adult samples. Full article

(This article belongs to the Special Issue Mental Speed and Response Times in Cognitive Tests)

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948 KiB

Open AccessArticle

Trait Characteristics of Diffusion Model Parameters

by Anna-Lena Schubert, Gidon T. Frischkorn, Dirk Hagemann and Andreas Voss

J. Intell. 2016, 4(3), 7; https://doi.org/10.3390/jintelligence4030007 - 18 Jul 2016

Cited by 48 | Viewed by 11570

Abstract

Cognitive modeling of response time distributions has seen a huge rise in popularity in individual differences research. In particular, several studies have shown that individual differences in the drift rate parameter of the diffusion model, which reflects the speed of information uptake, are substantially related to individual differences in intelligence. However, if diffusion model parameters are to reflect trait-like properties of cognitive processes, they have to qualify as trait-like variables themselves, i.e., they have to be stable across time and consistent over different situations. To assess their trait characteristics, we conducted a latent state-trait analysis of diffusion model parameters estimated from three response time tasks that 114 participants completed at two laboratory sessions eight months apart. Drift rate, boundary separation, and non-decision time parameters showed a great temporal stability over a period of eight months. However, the coefficients of consistency and reliability were only low to moderate and highest for drift rate parameters. These results show that the consistent variance of diffusion model parameters across tasks can be regarded as temporally stable ability parameters. Moreover, they illustrate the need for using broader batteries of response time tasks in future studies on the relationship between diffusion model parameters and intelligence. Full article

(This article belongs to the Special Issue Mental Speed and Response Times in Cognitive Tests)

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496 KiB

Open AccessArticle

Validity of the Worst Performance Rule as a Function of Task Complexity and Psychometric g: On the Crucial Role of g Saturation

by Thomas H. Rammsayer and Stefan J. Troche

J. Intell. 2016, 4(1), 5; https://doi.org/10.3390/jintelligence4010005 - 16 Mar 2016

Cited by 13 | Viewed by 7436

Abstract

Within the mental speed approach to intelligence, the worst performance rule (WPR) states that the slower trials of a reaction time (RT) task reveal more about intelligence than do faster trials. There is some evidence that the validity of the WPR may depend on high g saturation of both the RT task and the intelligence test applied. To directly assess the concomitant influence of task complexity, as an indicator of task-related g load, and g saturation of the psychometric measure of intelligence on the WPR, data from 245 younger adults were analyzed. To obtain a highly g-loaded measure of intelligence, psychometric g was derived from 12 intelligence scales. This g factor was contrasted with the mental ability scale that showed the smallest factor loading on g. For experimental manipulation of g saturation of the mental speed task, three versions of a Hick RT task with increasing levels of task complexity were applied. While there was no indication for a general WPR effect when a low g-saturated measure of intelligence was used, the WPR could be confirmed for the highly g-loaded measure of intelligence. In this latter condition, the correlation between worst performance and psychometric g was also significantly higher for the more complex 1-bit and 2-bit conditions than for the 0-bit condition of the Hick task. Our findings clearly indicate that the WPR depends primarily on the g factor and, thus, only holds for the highly g-loaded measure of psychometric intelligence. Full article

(This article belongs to the Special Issue Mental Speed and Response Times in Cognitive Tests)

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767 KiB

Open AccessReview

Use of Response Time for Measuring Cognitive Ability

by Patrick C. Kyllonen and Jiyun Zu

J. Intell. 2016, 4(4), 14; https://doi.org/10.3390/jintelligence4040014 - 1 Nov 2016

Cited by 93 | Viewed by 18523

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 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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649 KiB

Open AccessBrief Report

Phenotypic, Genetic, and Environmental Correlations between Reaction Times and Intelligence in Young Twin Children

by Julie Aitken Schermer and Philip A. Vernon

J. Intell. 2015, 3(4), 160-167; https://doi.org/10.3390/jintelligence3040160 - 17 Dec 2015

Viewed by 6251

Abstract

Phenotypic, genetic, and environmental correlations between various reaction time measures and intelligence were examined in a sample of six-year-old twin children (N = 530 individuals). Univariate genetic analyses conducted on the same-sex pairs (101 monozygotic (MZ) pairs and 132 same-sex dizygotic (DZ) pairs) demonstrated that the intelligence measure and four of the seven reaction time measures had a genetic component (ranging from 44% to 76%). At the phenotypic level, half of the reaction time measures had significant negative correlations with the intelligence measure. Bivariate genetic analyses revealed that only two of the observed phenotypic correlations could be explained by common genetic factors and that the remaining correlations were better explained by common environmental factors. Full article

(This article belongs to the Special Issue Mental Speed and Response Times in Cognitive Tests)

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