Differences in numeric, verbal, and spatial reasoning between engineering and literature students through a neurocognitive lens

Liang, Chaoyun; Liu, Yucheng; Chang, Yu‐Hsuan; Liang, Chao-Tung

doi:10.1016/j.cogsys.2019.11.003

Cited by 4 publications

(4 citation statements)

References 31 publications

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“…While research on fluid intelligence and its different components has been extensive (Bart et al 1980;Langdon and Warrington 2000;Liang et al 2020), research on crystallized intelligence has been severely limited due to the methodological constrains associated with its measuring. The only component of this type of intelligence that has been thoroughly studied is vocabulary size (Aguasvivas et al 2020;Brysbaert et al 2016), which has resulted in the common misuse of ''crystallized intelligence'' and ''verbal ability'' as synonyms.…”

Section: Discussionmentioning

confidence: 99%

“…While its general and simplistic nature is a solid first step toward stablishing a taxonomy of intelligence, it also makes it insufficiently specific, and further subdivisions of both fluid and crystallized intelligence are still required. For instance, when it comes to fluid intelligence, drawing conclusions from a series of premises -i.e., reasoning-and the ability to mentally manipulate visual information are clearly separate cognitive skills that must be individually evaluated (Bart et al 1980;Langdon & Warrington 2000;Liang et al 2020). This issue becomes even more egregious when it comes to crystallized intelligence.…”

Section: Introductionmentioning

confidence: 99%

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Intelligence subcomponents and their relationship to general knowledge

Buades-Sitjar

Duñabeitia

2022

J Cult Cogn Sci

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Research on the different components of fluid intelligence and how they relate to each other is quite extensive. Meanwhile, when it comes to crystallized intelligence, only vocabulary size has been somewhat thoroughly studied, while other key components, such as general knowledge, remain largely unexplored. This study aims to further our understanding of general knowledge as a key component of crystallized intelligence, and of general intelligence as a whole, by exploring how it is influenced by other components of intelligence. To that end, we had 90 participants complete an extensive general knowledge questionnaire, as well as several tests aimed at measuring various components of intelligence, and computed linear regressions to examine how these various components influence general knowledge scores. Our results reveal that, even though general intelligence is able to predict general knowledge scores, only some specific components of intelligence have a direct positive impact on general knowledge. These findings are discussed in regard to intellectual investment theories on the relationship between fluid and crystallized intelligence.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligence subcomponents and their relationship to general knowledge

Buades-Sitjar

Duñabeitia

2022

J Cult Cogn Sci

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“…In this study, aptitude test are made as references. Aptitude tests are designed to evaluate cognitive and reasoning abilities [41] . Abstract, quantitative, verbal, data verification and spatial are among the areas that are measured as cognitive abilities [41] .…”

Section: Case Study Preparationmentioning

confidence: 99%

“…Aptitude tests are designed to evaluate cognitive and reasoning abilities [41] . Abstract, quantitative, verbal, data verification and spatial are among the areas that are measured as cognitive abilities [41] . Based on that references, three sets of mathematical and logical analytic questions were prepared.…”

Section: Case Study Preparationmentioning

confidence: 99%

Cardiac Biometrics and Perceived Workload Regression Analysis Using Random Forest Regressor in Cognitive Manufacturing Tasks

Harmayanti,

Tama,

Gapsari

et al. 2024

MECHTA

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Workload is crucial in managing and maintaining good performance of human resources and allocations. In an advanced manufacturing industry, human job functions had shifted to cognitive tasks. Thus, cognitive workload evaluation should be used to monitor worker’s workload in optimal condition. Most common tool of cognitive workload tools are perceived measurement, like NASA – TLX questionnaire. Despite of its sensitivity to capture workload felt by the workers, this subjective measurement was prone to bias. Objective measurement utilizing biometrics data of the human body during working state was useful to eliminate bias. Cardiac biometrics were one of the many that were closely related to mental activity changes. The objective of this study was to understand the relationship of cardiac biometrics to perceived workload as an indicator of cognitive workload analysis. The study utilized four biometrics, heart rate, HRV low frequency power, total frequency power and ratio of low and high frequency power, were used to analyzed a one hour long cognitive based study case. The study case was designed in a manufacturing planning context referring to manufacturing aptitude tests, to induce cognition process on 30 participants. The biometrics and NASA – TLX score result of all the participants, were then calculated as effect size standardization before input into random forest regressor model to analyze relationship between cardiac biometrics and perceived workload. The result found a moderate relationship between the two (r2 = 0.576). Features importance also showed the most impactful feature to the model is the effect size of frequency power ratio. However, it is recommended to always consider evaluating multiple cardiac biometrics in workload analysis to ensure good model performance.

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