"The Abilities of Man"

Bless, A. A.

doi:10.1126/science.68.1750.38-b

Cited by 7 publications

(7 citation statements)

References 1 publication

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“…Perhaps the most sophisticated implicit biology methodology makes use of the common observation that test scores on various kinds of CATs tend to be positively correlated with one another. Dating as far back as Spearman (1927), many psychometricians have postulated that the mechanism underlying this pattern of intercorrelations is an index of general ability, commonly called a g or general ability factor (Cattell, 1963). This factor is assumed to have a physiological basis and to be assessed by various CATs in differing amounts.…”

Section: Pseudo-racial Group Comparisonsmentioning

confidence: 99%

Why is there no study of cultural equivalence in standardized cognitive ability testing?

Helms¹

1992

American Psychologist

376

373

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Black-White differences in performance on cognitive ability tests (CATs) typically have been explained by assumptions inherent in the implicit biological or environmental philosophical perspectives. It is argued that these perspectives are based on deficient conceptualizations of culture. Consequently, neither can provide useful information about the cultural equivalence (meaning) of test scores across racial or ethnic groups. The culturalist perspective is presented as an alternative for investigating racial cultural influences on CATs using Eurocentric and Afrocentric cultural dimensions. Implications and recommendations for examining cultural equivalence in CATs from a culturalist perspective are discussed.

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Section: Pseudo-racial Group Comparisonsmentioning

confidence: 99%

Why is there no study of cultural equivalence in standardized cognitive ability testing?

Helms¹

1992

American Psychologist

376

373

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“…In this study, we calculate the reward of progressive pass types by calculating the frequency of shots created (Power et al, 2017), the expected goals (xG) of these shots (Lucey et al, 2015) which is an indicator of the quality of shot (Link, Lang & Seidenschwarz, 2016;Goes et al, 2022) and the proportion of passes that lead to goals (Rein, Raabe & Memmert, 2017;Steiner et al, 2019;Anzer, Bauer & Brefeld, 2021). In terms of measurement of risk associated to a pass, recent development has seen the introduction of expected pass completion models for all available options to the passer using tracking data (Spearman et al, 2017;Anzer & Bauer, 2022). However, in this study we calculate the risk as the actual observed likelihood of not completing the progressive pass to a teammate, which has been widely used (Power et al, 2017;Goes et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Finding repeatable progressive pass clusters and application in international football

Deb,

Fernandez-Navarro,

McRobert

et al. 2024

JSA

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Progressive passing in football (soccer) is a key aspect in creating positive possession outcomes. Whilst this is well established, there is not a consistent way to describe the different types of progressive passes. We expand on the previous literature, providing a complete methodological approach to progressive pass clustering from selection of the number of clusters (k) to risk-reward profiling of these progressive pass types. In this paper the Separation and Concordance (SeCo) framework is utilised to provide a process to analyse k-means clustering solutions in a more repeatable way. The results demonstrate that we can find stable progressive pass clusters in International Football and their efficacy with progressive passes “Mid Central to Mid Half Space” in build-up and “Mid Half Space to Final Central” into the final 3rd having the best balance between risk (turnover) and reward (shot created) in the subsequent possession. This allowed for opposition profiling of player and team patterns in different phases of play, with a case study presented for the teams in the Last 16 of the 2022 World Cup.

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“…It is obvious that the representative indicators must be based on good tests. Spearman (1928) proposed that human intelligence includes two factors: One is the general intelligence factor (GIF), which is derived from innate inheritance and manifested in general activities, indicating the level of individual ability. The other is a specific factor, which is the ability of an individual to perform a special activity that differs from others.…”

Section: Introductionmentioning

confidence: 99%

“…Drawing on the GIF ( Spearman, 1928 ; Vernon, 1966 ) and GPF ( Musek, 2007 ; Wu, 2017 ; Wu et al, 2021 ), the current study put forward the concept of the general microexpression recognition ability (GMERA). We assumed that human microexpression recognition ability has a three-layer hierarchical structure: The highest layer is the GMERA, which may be derived from innate heredity and influenced by learning, and manifests the level of individual microexpression recognition ability (the current study); the second layer is the individual’s ability to recognize a type of microexpression under different expression backgrounds, for example, anger microexpression under sadness, fear, disgust, neutral, surprise, and happiness expression backgrounds ( Yin et al, 2020 ; Zhang et al, 2020b ); and the third layer is an individual’s ability to recognize a certain microexpression under a certain expression background, for example, anger microexpression under sadness expression background (please refer to Figure 1 , Zhang et al, 2017 ; Zhu et al, 2017 ; Yin et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

The establishment of the general microexpression recognition ability and its relevant brain activity

Zhang

Yin

Shu

et al. 2022

Front. Hum. Neurosci.

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Microexpressions are very transitory expressions lasting about 1/25∼1/2 s, which can reveal people’s true emotions they try to hide or suppress. The PREMERT (pseudorandom ecological microexpression recognition test) could test the individual’s microexpression recognition ability with six microexpression Ms (the mean of accuracy rates of a microexpression type under six expression backgrounds), and six microexpression SDs (the standard deviation of accuracy rates of this microexpression type under six expression backgrounds), but it and other studies did not explore the general microexpression recognition ability (the GMERA) or could not test the GMERA effectively. Therefore, the current study put forward and established the GMERA with the behavioral data of the PREMERT. The spontaneous brain activity in the resting state is a stable index to measure individual cognitive characteristics. Therefore, the current study explored the relevant resting-state brain activity of the GMERA indicators to prove that GMERA is an individual cognitive characteristic from brain mechanisms with the neuroimaging data of the PREMERT. The results showed that (1) there was a three-layer hierarchical structure in human microexpression recognition ability: The GMERA (the highest layer); recognition of a type of microexpression under different expression backgrounds (the second layer); and recognition of a certain microexpression under a certain expression background (the third layer). A common factor GMERA was extracted from the six microexpression types recognition in PREMERT. Four indicators of the GMERA were calculated from six microexpression Ms and six microexpression SDs, such as GMERAL (level of GMERA), GMERAF (fluctuation of GMERA), GMERAB (background effect of GMERA), and GMERABF (fluctuation of GMERAB), which had good parallel-forms reliability, calibration validity, and ecological validity. The GMERA provided a concise and comprehensive overview of the individual’s microexpression recognition ability. The PREMERT was proved as a good test to measure the GMERA. (2) ALFFs (the amplitude of low-frequency fluctuations) in both eyes-closed and eyes-opened resting-states and ALFFs-difference could predict the four indicators of the GMERA. The relevant resting-state brain areas were some areas of the expression recognition network, the microexpression consciousness and attention network, and the motor network for the change from expression backgrounds to microexpression. (3) The relevant brain areas of the GMERA and different types of microexpression recognition belonged to the three cognitive processes, but the relevant brain areas of the GMERA were the “higher-order” areas to be more concise and critical than those of different types of microexpression recognition.

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"The Abilities of Man"

Cited by 7 publications

References 1 publication

Why is there no study of cultural equivalence in standardized cognitive ability testing?

Why is there no study of cultural equivalence in standardized cognitive ability testing?

Finding repeatable progressive pass clusters and application in international football

The establishment of the general microexpression recognition ability and its relevant brain activity

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