scholarly journals Psychometric Network Analysis of the Hungarian WAIS

2019 ◽  
Vol 7 (3) ◽  
pp. 21 ◽  
Author(s):  
Christopher J. Schmank ◽  
Sara Anne Goring ◽  
Kristof Kovacs ◽  
Andrew R. A. Conway
Keyword(s):  
Factor Analysis ◽  
Network Analysis ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Latent Variable ◽  
Secondary Data ◽  
Network Models ◽  
Higher Order ◽  
Underlying Structure ◽  
Adult Intelligence Scale

The positive manifold—the finding that cognitive ability measures demonstrate positive correlations with one another—has led to models of intelligence that include a general cognitive ability or general intelligence (g). This view has been reinforced using factor analysis and reflective, higher-order latent variable models. However, a new theory of intelligence, Process Overlap Theory (POT), posits that g is not a psychological attribute but an index of cognitive abilities that results from an interconnected network of cognitive processes. These competing theories of intelligence are compared using two different statistical modeling techniques: (a) latent variable modeling and (b) psychometric network analysis. Network models display partial correlations between pairs of observed variables that demonstrate direct relationships among observations. Secondary data analysis was conducted using the Hungarian Wechsler Adult Intelligence Scale Fourth Edition (H-WAIS-IV). The underlying structure of the H-WAIS-IV was first assessed using confirmatory factor analysis assuming a reflective, higher-order model and then reanalyzed using psychometric network analysis. The compatibility (or lack thereof) of these theoretical accounts of intelligence with the data are discussed.

scholarly journals Psychometric Network Analysis of the Hungarian WAIS

2019 ◽  
Author(s):  
Christopher J. Schmank ◽  
Sara Anne Goring ◽  
Kristof Kovacs ◽  
Andrew R. A. Conway
Keyword(s):  
Network Analysis ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Latent Variable ◽  
Network Models ◽  
Latent Variable Models ◽  
Latent Variable Modeling ◽  
Attractive Alternative ◽  
Adult Intelligence Scale ◽  
Network Analyses

The positive manifold—the finding that cognitive ability measures demonstrate positive correlations with one another—has led to models of intelligence that include a general cognitive ability or general intelligence (g). This view has been reinforced using factor analysis and latent variable models. However, a new theory of intelligence, Process Overlap Theory (POT; Kovacs & Conway, 2016), posits that g is not a psychological attribute but an index of cognitive abilities that results from an interconnected network of cognitive processes. From this perspective, psychometric network analysis is an attractive alternative to latent variable modeling. Network analyses display partial correlations among observed variables that demonstrate direct relationships among observed variables. To demonstrate the benefits of this approach, the Hungarian Wechsler Adult Intelligence Scale Fourth Edition (H-WAIS-IV; Wechsler, 2008) was analyzed using both psychometric network analysis and latent variable modeling. Network models were directly compared to latent variable models. Results indicate that the H-WAIS-IV data was better fit by network models than by latent variable models. We argue that POT, and network models, provide a more accurate view of the structure of intelligence than traditional approaches.

scholarly journals Making Cognitive Latent Variables Manifest: Distinct Neural Networks for Fluid Reasoning and Processing Speed

2015 ◽  
Vol 27 (6) ◽  
pp. 1249-1258 ◽  
Author(s):  
Christian Habeck ◽  
Jason Steffener ◽  
Daniel Barulli ◽  
Yunglin Gazes ◽  
Qolamreza Razlighi ◽  
...  
Keyword(s):  
Individual Differences ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Latent Variables ◽  
Latent Variable ◽  
Cognitive Tasks ◽  
Neural Activation ◽  
Strongly Correlated ◽  
Activation Patterns ◽  
Fluid Reasoning

Cognitive psychologists posit several specific cognitive abilities that are measured with sets of cognitive tasks. Tasks that purportedly tap a specific underlying cognitive ability are strongly correlated with one another, whereas performances on tasks that tap different cognitive abilities are less strongly correlated. For these reasons, latent variables are often considered optimal for describing individual differences in cognitive abilities. Although latent variables cannot be directly observed, all cognitive tasks representing a specific latent ability should have a common neural underpinning. Here, we show that cognitive tasks representing one ability (i.e., either perceptual speed or fluid reasoning) had a neural activation pattern distinct from that of tasks in the other ability. One hundred six participants between the ages of 20 and 77 years were imaged in an fMRI scanner while performing six cognitive tasks, three representing each cognitive ability. Consistent with prior research, behavioral performance on these six tasks clustered into the two abilities based on their patterns of individual differences and tasks postulated to represent one ability showed higher similarity across individuals than tasks postulated to represent a different ability. This finding was extended in the current report to the spatial resemblance of the task-related activation patterns: The topographic similarity of the mean activation maps for tasks postulated to reflect the same reference ability was higher than for tasks postulated to reflect a different reference ability. Furthermore, for any task pairing, behavioral and topographic similarities of underlying activation patterns are strongly linked. These findings suggest that differences in the strengths of correlations between various cognitive tasks may be because of the degree of overlap in the neural structures that are active when the tasks are being performed. Thus, the latent variable postulated to account for correlations at a behavioral level may reflect topographic similarities in the neural activation across different brain regions.

scholarly journals Psychometric Models of Individual Differences in Reading Comprehension: A Reanalysis of Freed, Hamilton, and Long (2017)

2019 ◽  
Author(s):  
Sara Anne Goring ◽  
Christopher J. Schmank ◽  
Michael J. Kane ◽  
Andrew R. A. Conway
Keyword(s):  
Reading Comprehension ◽  
Individual Differences ◽  
Cognitive Abilities ◽  
Latent Variable ◽  
Convergent Validity ◽  
Cognitive Task ◽  
Network Modeling ◽  
Network Models ◽  
Measurement Model ◽  
Common Cause

Individual differences in reading comprehension have often been explored using latent variable modeling (LVM), to assess the relative contribution of domain-general and domain-specific cognitive abilities. However, LVM is based on the assumption that the observed covariance among indicators of a construct is due to a common cause (i.e., a latent variable; Pearl, 2000). This is a questionable assumption when the indicator variables are measures of performance on complex cognitive tasks. According to Process Overlap Theory (POT; Kovacs & Conway, 2016), multiple processes are involved in cognitive task performance and the covariance among tasks is due to the overlap of processes across tasks. Instead of a single latent common cause, there are thought to be multiple dynamic manifest causes, consistent with an emerging view in psychometrics called network theory (Barabási, 2012; Borsboom & Cramer, 2013). In the current study, we reanalyzed data from Freed et al. (2017) and compared two modeling approaches: LVM (Study 1) and psychometric network modeling (Study 2). In Study 1, two exploratory LVMs demonstrated problems with the original measurement model proposed by Freed et al. Specifically, the model failed to achieve discriminant and convergent validity with respect to reading comprehension, language experience, and reasoning. In Study 2, two network models confirmed the problems found in Study 1, and also served as an example of how network modeling techniques can be used to study individual differences. In conclusion, more research, and a more informed approach to psychometric modeling, is needed to better understand individual differences in reading comprehension.

scholarly journals Mitochondrial Functioning and Its Relation to Higher-Order Cognitive Processes

2020 ◽  
Author(s):  
Alexander P. Burgoyne ◽  
Randall W Engle
Keyword(s):  
Cognitive Ability ◽  
Cognitive Processes ◽  
Cognitive Abilities ◽  
Higher Order ◽  
G Factor

One of the most replicated findings in psychology is the positive manifold between cognitive ability measures (Jensen, 1998). That is, scores on different tests of cognitive ability are positively correlated, implying the existence of a g factor. Our research attempts to explain these relationships among broad cognitive abilities using a combination of experimental and differential approaches (Cronbach, 1957).

A network psychometric approach to neurocognitive functioning in Alzheimer's disease

2020 ◽  
Author(s):  
Cameron Ferguson
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Psychometric Properties ◽  
Graphical Models ◽  
Network Structure ◽  
Latent Variable ◽  
Network Models ◽  
Information Criterion ◽  
Graphical Lasso

Introduction: descriptions of the typical pattern of neurocognitive impairment in Alzheimer’s disease (AD) refer to relationships between neurocognitive domains as well as deficits within domains. However, the former of these relationships have not been statistically modelled. Accordingly, this study aimed to model the unique variance between neurocognitive variables in AD, amnestic mild cognitive impairment (aMCI), and cognitive normality (CN) using network analysis. Methods: Gaussian Graphical Models with Extended Bayesian Information Criterion model selection and graphical lasso regularisation were used to estimate network models of neurocognitive variables in AD (n = 229), aMCI (n = 397) and CN (n = 193) groups. The psychometric properties of the models were investigated using simulation and bootstrapping procedures. Exploratory analyses of network structure invariance across groups were conducted. Results: neurocognitive network models were estimated for each group and found to have good psychometric properties. Exploratory investigations suggested that network structure was not invariant across CN and aMCI (p = 0.03), CN and AD (p < 0.01), and aMCI and AD neurocognitive networks (p < 0.01).Conclusions: network analysis can be used to robustly model the relationships between neurocognitive variables in AD, aMCI and CN. Network structure was not invariant, suggesting that relationships between neurocognitive variables differ across groups along the AD spectrum. Points of convergence and contrast with latent-variable models are explored.

scholarly journals The Worst Performance Rule, or the Not-Best Performance Rule? Latent-Variable Analyses of Working Memory Capacity, Mind-Wandering Propensity, and Reaction Time

2020 ◽  
Vol 8 (2) ◽  
pp. 25 ◽  
Author(s):  
Matthew S. Welhaf ◽  
Bridget A. Smeekens ◽  
Matt E. Meier ◽  
Paul J. Silvia ◽  
Thomas R. Kwapil ◽  
...  
Keyword(s):  
Working Memory ◽  
Reaction Time ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Latent Variable ◽  
Response Times ◽  
Working Memory Capacity ◽  
Empirical Finding ◽  
Memory Capacity ◽  
Better Than

The worst performance rule (WPR) is a robust empirical finding reflecting that people’s worst task performance shows numerically stronger correlations with cognitive ability than their average or best performance. However, recent meta-analytic work has proposed this be renamed the “not-best performance” rule because mean and worst performance seem to predict cognitive ability to similar degrees, with both predicting ability better than best performance. We re-analyzed data from a previously published latent-variable study to test for worst vs. not-best performance across a variety of reaction time tasks in relation to two cognitive ability constructs: working memory capacity (WMC) and propensity for task-unrelated thought (TUT). Using two methods of assessing worst performance—ranked-binning and ex-Gaussian-modeling approaches—we found evidence for both the worst and not-best performance rules. WMC followed the not-best performance rule (correlating equivalently with mean and longest response times (RTs)) but TUT propensity followed the worst performance rule (correlating more strongly with longest RTs). Additionally, we created a mini-multiverse following different outlier exclusion rules to test the robustness of our findings; our findings remained stable across the different multiverse iterations. We provisionally conclude that the worst performance rule may only arise in relation to cognitive abilities closely linked to (failures of) sustained attention.

scholarly journals Individual differences in cortical processing speed predict cognitive abilities: a model-based cognitive neuroscience account

2018 ◽  
Author(s):  
Anna-Lena Schubert ◽  
Michael D. Nunez ◽  
Dirk Hagemann ◽  
Joachim Vandekerckhove
Keyword(s):  
Individual Differences ◽  
Cognitive Ability ◽  
Processing Speed ◽  
Cognitive Abilities ◽  
Test Scores ◽  
Cognitive Modeling ◽  
Higher Order ◽  
Neural Processing ◽  
Ability Test ◽  
Evidence Accumulation

AbstractPrevious research has shown that individuals with greater cognitive abilities display a greater speed of higher-order cognitive processing. These results suggest that speeded neural information-processing may facilitate evidence accumulation during decision making and memory updating and thus yield advantages in general cognitive abilities. We used a hierarchical Bayesian cognitive modeling approach to test the hypothesis that individual differences in the velocity of evidence accumulation mediate the relationship between neural processing speed and cognitive abilities. We found that a higher neural speed predicted both the velocity of evidence accumulation across behavioral tasks as well as cognitive ability test scores. However, only a small part of the association between neural processing speed and cognitive abilities was mediated by individual differences in the velocity of evidence accumulation. The model demonstrated impressive forecasting abilities by predicting 36% of individual variation in cognitive ability test scores in an entirely new sample solely based on their electrophysiological and behavioral data. Our results suggest that individual differences in neural processing speed might affect a plethora of higher-order cognitive processes, that only in concert explain the large association between neural processing speed and cognitive abilities, instead of the effect being entirely explained by differences in evidence accumulation speeds.

scholarly journals Worst performance rule, or not-best performance rule?

2020 ◽  
Author(s):  
Matt Welhaf ◽  
Bridget Anne Smeekens ◽  
Matt Ethan Meier ◽  
Paul Silvia ◽  
Thomas Richard Kwapil ◽  
...  
Keyword(s):  
Cognitive Ability ◽  
Task Performance ◽  
Cognitive Abilities ◽  
Latent Variable ◽  
Working Memory Capacity ◽  
Empirical Finding ◽  
Memory Capacity ◽  
Gaussian Modeling ◽  
Better Than ◽  
And Task

The worst performance rule (WPR) is a robust empirical finding reflecting that people’s worst task performance shows stronger relations to cognitive ability compared to their average or best performance. However, recent meta-analytic work has proposed this be renamed the “not-best-performance” rule because mean and worst performance seem to predict cognitive ability to similar degrees (with both predicting ability better than best performance). We re-analyzed data from a previously published latent-variable study to test for worst vs. not-best performance across a variety of reaction time tasks in relation to two cognitive ability constructs: working memory capacity (WMC) and task-unrelated thought (TUT) rate. Using two methods of assessing worst performance—ranked-binning and ex-Gaussian-modeling approaches—we found evidence for both worst and not-best performance rules. WMC followed the not-best performance rule (correlating equivalently with mean and worst RTs) but TUT propensity followed the worst performance rule (correlating more strongly with worst RTs). Additionally, we created a mini-multiverse following different outlier exclusion rules to test the robustness of our findings; our findings remained stable across the different multiverse iterations. We provisionally conclude that the worst performance rule may only arise in relation to cognitive abilities closely linked to (failures of) sustained attention.

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