Decomposing the relationship between mental speed and mental abilities

Schubert, Anna‐Lena; Hagemann, Dirk; Voss, Andreas; Schankin, Andrea; Bergmann, Katharina

doi:10.1016/j.intell.2015.05.002

Cited by 84 publications

(159 citation statements)

References 45 publications

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“…In any diffusion model study, the probably most important issue that a researcher has to examine is validity of parameters. In recent years, several experimental validation studies (e.g., Arnold, Bröder, & Bayen, 2015;Voss et al, 2004;Wagenmakers, Ratcliff, et al, 2008) and correlational analyses (e.g., Ratcliff, Thapar, & McKoon, 2011;Schubert, Hagemann, Voss, Schankin, & Bergmann, 2015) have supplied promising results regarding parameter validity. However, for any new paradigm, the validity has to be first examined.…”

Section: Discussionmentioning

confidence: 99%

How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria

2016

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Diffusion models (Ratcliff, 1978) make it possible to identify and separate different cognitive processes underlying responses in binary decision tasks (e.g., the speed of information accumulation vs. the degree of response conservatism). This becomes possible because of the high degree of information utilization involved. Not only mean response times or error rates are used for the parameter estimation, but also the response time distributions of both correct and error responses. In a series of simulation studies, the efficiency and robustness of parameter recovery were compared for models differing in complexity (i.e., in numbers of free parameters) and trial numbers (ranging from 24 to 5,000) using three different optimization criteria (maximum likelihood, Kolmogorov-Smirnov, and chi-square) that are all implemented in the latest version of fast-dm (Voss, Voss, & Lerche, 2015). The results revealed that maximum likelihood is superior for uncontaminated data, but in the presence of fast contaminants, Kolmogorov-Smirnov outperforms the other two methods. For most conditions, chi-square-based parameter estimations lead to less precise results than the other optimization criteria. The performance of the fast-dm methods was compared to the EZ approach (Wagenmakers, van der Maas, & Grasman, 2007) and to a Bayesian implementation (Wiecki, Sofer, & Frank, 2013). Recommendations for trial numbers are derived from the results for models of different complexities. Interestingly, under certain conditions even small numbers of trials (N < 100) are sufficient for robust parameter estimation.

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Section: Discussionmentioning

confidence: 99%

How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria

2016

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“…Schmiedek et al [27], however, reported a small but significant positive correlation between a latent non-decision time factor and reasoning ability, r = .25. Schubert et al [28] also found evidence for an association between the non-decision time parameter and general intelligence, but in the direction that more intelligent individuals had shorter non-decision times, r = −.42. More research is needed to conclude whether intelligence is consistently associated with the non-decision time parameter.…”

Section: Correlations Between Diffusion Model Parameters and Mental Amentioning

confidence: 98%

“…Manifest correlations between drift rate parameters estimated from response time distributions of these tasks ranged from r = .18 to .67 with the matrix reasoning subtest, and from r = .28 to .68 with the verbal subtest [26]. Schmiedek et al [27] reported a latent correlation of r = .79 between reasoning ability and drift rates in eight response time tasks (including verbal, numerical, and spatial tasks) in a student sample, and Schubert et al [28] reported a correlation of r = .50 between general intelligence and a drift rate factor derived from three response time tasks (choice response task, recognition task, semantic discrimination task). Taken together, these results indicate that smarter individuals have a greater speed of information uptake as reflected in the drift rate parameter.…”

Section: Correlations Between Diffusion Model Parameters and Mental Amentioning

confidence: 99%

“…Applications in individual differences research include studies on individual differences in attention [23], in impulsivity, e.g., [24], in mental abilities, e.g., [25][26][27][28], in numeracy [29], and in word recognition [30].…”

Section: The Diffusion Model: a Process Model Of Speeded Binary Decismentioning

confidence: 99%

“…This research is based on the assumption that individual differences in these model parameters reflect trait-like properties of cognitive processes. Four studies analyzing the relation between diffusion model parameters and mental abilities reported substantial correlations between drift rate and mental abilities ranging from r = .18 to .90 [25][26][27][28]. Ratcliff et al [25] asked participants in three age groups (college age, 60-74 years old, 75-90 years old) to complete the vocabulary and matrix reasoning subtests of the Wechsler intelligence test and analyzed correlations with a latent drift rate factor from a numerosity discrimination, recognition memory, and lexical decision response time task.…”

Section: Correlations Between Diffusion Model Parameters and Mental Amentioning

confidence: 99%

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Trait Characteristics of Diffusion Model Parameters

et al. 2016

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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.

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In search of a measure to investigate mental performance among children: Development of the mental speed test

Doğan

Akbulut

Sak

2023

Infant and Child Development

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Processing speed is a component of general intelligence and an indicator of learning potential. There is a need for robust measures of mental speed based on contemporary theoretical developments. The current study addressed this need by proposing a mental speed test for children aged 60 to 96 months (5 to 8 years) and examining its psychometric properties. The test included indicators of perceptual speed, memory speed, reasoning speed, and fluency‐flexibility speed presented through nonverbal items administered individually using touchscreen tablets. After establishing gender non‐bias and concurrent validity with a contemporary intelligence test (i.e., ASIS, r = .59) with 107 children, the next administration included 373 children. Exploratory factor analysis (EFA) with subtest scores revealed a single‐factor structure accounting for 45% of the total variance. Additional data from 212 children were used to assess structural validity and gender bias, which showed acceptable goodness of fit, providing evidence of the validity and reliability of the new measure for further use.

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Decomposing the relationship between mental speed and mental abilities

Cited by 84 publications

References 45 publications

How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria

How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria

Trait Characteristics of Diffusion Model Parameters

In search of a measure to investigate mental performance among children: Development of the mental speed test

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