More on “g is the Loch Ness Monster of psychology”

They say “a picture is worth a thousand words.”  How about the following 6 pictures?  See prior “g is the Loch Ness monster of psychology” post here.  Click on each image to enlarge for easy reading.  Created with add of AI agent Grok.

Research Alert: Is It Mine or Not Mine? Cognitive Offloading to Generative AI and Concern over the Erosion of Cognitive Ownership

Email-based research alert—open access link to download

 

Is It Mine or Not Mine? Cognitive Offloading to Generative AI and Concern over the Erosion of Cognitive Ownership 
https://www.mdpi.com/2079-3200/14/8/182

 

 

Abstract

Cognitive offloading—delegating cognitive work to external tools—is basic to human cognition, but generative AI (GenAI) amplifies it radically: entire cognitive products can now be produced on request. This sharpens a question about agency over one's own thinking: when cognition is habitually offloaded, does its product still feel like one's own? We surveyed 239 pre-service teachers, operationalizing the felt loss of cognitive ownership as concern over the erosion of teaching-design subjectivity (TSC)—the metacognitive appraisal that AI-assisted work is not genuinely one's own and that independent capacity is declining. Guided by the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, we tested whether AI anxiety/fear of missing out (affective) and impulsivity (self-regulatory) relate to this concern through behavioral GenAI dependency—habitual offloading. In a structural equation model with bias-corrected bootstrapping, dependency strongly predicted TSC and partially mediated the effect of AI anxiety; impulsivity raiseddependency but showed a suppression pattern (a positive indirect effect offset by a null total effect), and the model explained 35% of the variance in the concern. Habitual offloading—that is, GenAI dependency—rather than generic AI use, accompanies the metacognitive loss of cognitive ownership.

Keywords: cognitive offloading; agency; cognitive ownership; metacognition; generative AI; GenAI dependency

Click on image to enlarge for easy reading.

 

Research Alert: Proximal and distal factors influencing performance in mental and written calculations: A study with the network analysis

 An open access article that can be dowloaded and read. 👍  https://www.sciencedirect.com/science/article/pii/S2405844026007565

 

Click on images to enlarge for easy reading

Abstract

In the present study, we aimed to provide a first estimate of the general relationships among math skills through network analysis. Using clinically validated instruments, we examined the performance of a group of 166 typically developing Italian children attending 4th and 5th grade in several math abilities (mental and written calculation, arithmetic facts retrieval, magnitude processing, number transcoding, knowledge of computation procedures, and computation strategies), and domain-general factors (working memory, processing speed, verbal fluency, and visuospatial reasoning). A first network, based on math tasks, indicated that mental calculation is more associated with automatization in retrieving arithmetic facts, while written calculation is more associated with magnitude processing. Both mental and written calculation are strongly related to computation strategies, a central node in the network. A second network indicated that domain-general factors appear peripheral in the network (except for visuospatial reasoning), without direct associations with calculation abilities.

 

Comments from IQs Corner’s blogmaster:

 

I love when newer psychometric network analysis methods are applied to cognitive+achievement variables.  No one methodology answers all questions, but PNA offers unique advantages that has the potential to improve cognitive-ach intervention research.  As I’ve stated elsewhere (McGrew, 2023)

 

•  In the current context, the primary value of these descriptive models is their ability to function as a bridge to theory formation and the ability to hypothesize, and empirically test or statistically simulate, potential causal mechanisms in the network (Borsboom et al. 2021; Haslbeck et al. 2021) (McGrew et al. 2023, p. 5). PNA models can be used to generate causal hypotheses between abilities measured by individual node measures, offering insights regarding the most likely influential targets (or target systems) for intervention (Haslbeck et al. 2021; McGrew et al. 2023).

• Traditional statistical prediction models of achievement, such as multiple regression, provide few clues regarding potential complex causal relations between and among variables. The PNA cognitive-achievement interpretations offered here, although speculative, when informed by the extant substantive research and theoretical literature, have greater potential to elucidate the complex relations between and among CHC cognitive and achievement constructs. The descriptive PNA models (Figures 1 and 2) can be explored with various tools from network science (e.g., exploratory and confirmatory PNA; exploratory stepwise search algorithms to guide the removal or addition of nodes to improve the model; in silico mathematical simulations where changes in network nodes are statistically modified [or constrained] to see how the effect propagates through the entire network and potentially reveals causal mechanisms in the network; etc.) (Epskamp et al. 2017; Haslbeck et al. 2021; Lunansky et al. 2022)  (McGrew et al. 2023, p. 6). PNA could assume a pivotal role in improving CHC cognitive-achievement relations SEM modeling research as it acts as a natural interface between correlation and causality . . . [as] the typical attempt to determine directed SEMs from correlation structures in fact appears somewhat haphazard in psychology, a historical accident in a field that has been prematurely directed to hypothesis testing at the expense of systematic exploration (Epskamp et al. 2017, pp. 924). PNA methods could facilitate CHC SEM modeling via the systematic identification of relations between multiple variables unfettered by concerns for direct causal relations, reciprocal causation, latent common causes, semantic overlap between items [variables], or homeostatic coupling of parameters (Epskamp et al. 2017, p. 925).

 

 

Research alert: Specific cognitive abilities: A discussion of advantages and disadvantages of measurement methods - ScienceDirect

Important article for CHC g, broad, narrow cognitive ability and achievement research.

 

Specific cognitive abilities: A discussion of advantages and disadvantages of measurement methods - ScienceDirect 
https://www.sciencedirect.com/science/article/abs/pii/S0160289626000425

 

Abstract

Several models of cognitive abilities emphasize both general and specific abilities. While there have been several studies of ways to create measures of general cognitive ability, little information has been consolidated about methods for measuring specific abilities. In this article several models found in the literature are presented and discussed. Of particular importance, the reliance on face validity to develop measures of specific ability is discouraged for being unscientific, prone to personal bias and not necessarily repeatable. Factor analytic methods, hierarchical factor analysis, bifactor analysis, and principal factors and principal components methods are discussed and the need for statements about decisions made while conducting the analysis is strongly encouraged. Lastly, an examination of the methods, and advantages and disadvantages are presented.

 

Introduction

Over the past five decades, there has been a resurgence of interest in the study of cognitive abilities, particularly in relation to Spearman's two-factor theory of intelligence and factor analytic models of cognitive structure. In his seminal work, Spearman (1904) using the tetrad differences method, factor-analyzed data from schoolchildren and identified a common source of variance underlying performance across diverse cognitive tasks, which he termed the general factor, or g. The empirical observation that cognitive tests tend to correlate positively—a phenomenon known as positive manifold—has been consistently cited as evidence for the existence of g, a finding further supported by hierarchical and bifactor factor analytic approaches (e.g., Jensen, 1998). Several studies have addressed methodological approaches to measuring g (e.g., Jensen & Weng, 1994; Ree & Earles, 1991; Reeve & Blacksmith, 2009). Each cognitive test also captures unique variance not attributable to g, creating specific factors, or s. Despite their theoretical importance, there is a notable paucity of research focused on the measurement of s, with the work of Coyle and Greiff (2021) and Kell and Lang, 2017, Kell and Lang, 2018 representing examples of the few contributions in this area. While specific variance is typically unique to individual measures, in cases where multiple tests share a common specific source of variance, the resulting construct is classified as a group factor.

Whereas g represents broad, generalized cognitive functioning, specific abilities denote more narrowly defined cognitive abilities. These specific abilities are typically assessed using cognitive tests or tasks that measure both g and specific abilities. Specific abilities are rarely measured without also measuring general cognitive ability.

 

Specific abilities can be thought of as a continuum going from narrow versus broader factors. A good example is a broad spatial factor (spatial working memory, mental rotation, spatial reasoning) versus a narrowly defined spatial factor focusing on just visualization.

 

Thomson (1916) proposed an ability sampling model (also called the bonds model) of cognitive ability as an alternative to Spearman's two-factor theory. His model proposed that intelligence is related to the number and complexity of neural patterns in the brain rather than the existence of a general factor. Analyzing the same data as Spearman, Thomson found no need for a general factor, instead proposing that test scores are the sum of specific factors only. Thomson proposed that intelligence is not the consequence of a single pervasive factor. Rather, it emerges from the sampling of multiple independent mental elements, which he called “bonds.” The theory posits that the human brain learns to associate various bonds. The bonds are described very abstractly, so it is unclear what they correspond to psychologically or neurologically (e.g., cognitive processes, connections, skills, etc.). Because the model is cast mainly as a statistical sampling scheme, critics have argued that it is not a worked-out theory of cognitive architecture, but more a how-you-could-get-the-correlations story (Thomson, 1916). Bartholomew et al. (2009) contend that Thomson's model deserves more consideration. They argue that modern factor analysis cannot distinguish between Spearman's and Tomson's models; that is, they are mathematically equivalent in terms of fit to the data.

Tredoux (2025) contends that there is a lack of substantive evidence for Thomson's model and that it relies heavily on probabilistic reasoning and arguments based on chance. Tredoux further notes that Thomas' model serves as a method to illustrate cognitive functioning rather than how the mind actually works.

 

There are some competing process models of human ability. For example, the construct “working memory” has been proposed as a central organizing system for learning and action (Baddeley & Hitch, 1974; Miller et al., 1960).

 

A range of cognitive processes has been proposed to account for the observed relationships, including processing speed, executive functioning, levels of processing theory (Craik & Lockhart, 1972), and the Cattell-Horn-Carrol (CHC) three-stratum theory of intelligence (Carroll, 1996). These perspectives have also been discussed in relation to Spearman's contribution to theories of human abilities (see Dennis & Tapsfield, 1996).

 

The model selected by a researcher to represent general and specific cognitive abilities carries significant implications for the interpretation of cognitive structure. To properly interpret the results of a cognitive structure factor analysis, it is essential to remove the variance attributable to general cognitive ability (g) from specific (s) factors and ensure that they are orthogonal to one another. Without orthogonality, the analysis cannot effectively distinguish whether the variance is predominantly associated with g or with specific cognitive abilities. For example, hierarchical factor analysis may implicitly support the view that specific abilities are subsumed under g due to the inherent dependence structure of the model.

 

In contrast, bifactor models propose that all observed indicators (e.g., test scores) are directly influenced by a general factor (g), while specific factors (s) directly influence only a subset of indicators. Importantly, these specific factors are orthogonal both to g and to each other, thus precluding any assumptions regarding their origin or causal relationship to g.

 

Despite their mathematical distinction, unrotated principal components analysis and unrotated principal factors analysis yield orthogonal components or orthogonal factors and do not allow attributing causal origins. Consequently, differences in analytical approaches can lead to divergent theoretical interpretations of cognitive structure, particularly regarding the nature and independence of specific abilities.

 

There are several reasons for assessing specific cognitive abilities. Notable among them is the need to evaluate whether one or more of the specific abilities provide incremental predictive validity beyond general cognitive ability for important outcomes such as educational attainment, training success, career success, or job performance. Furthermore, the development and refinement of theoretical models of the structural organization of cognitive abilities or the possible origins of specific abilities necessitate precise measurement and empirical evaluation. The validity of conclusions drawn from such investigations depends both upon the appropriateness of the model and the reliability of the measures used. The accurate measurement of specific abilities facilitates the construction of detailed cognitive compendia, which are essential for theory development, individualized educational planning, and development of personnel measurement and selection instruments. There is no guarantee that tests or cognitive tasks created by researchers are isomorphic with brain structure and function. External tasks do not necessarily reveal internal brain structures and processes (Ree et al., 2024).

 

The purpose of this article is to present a critical analysis of key considerations in the assessment of specific abilities. The article presents six distinct approaches to defining specific abilities, followed by an analysis of the strengths and limitations of each. The discussion concludes with an evaluation of the implications these approaches have for both theoretical model development and applied practice. The approaches examined include: (a) Spearman's (1904) model, (b) face validity, (c) the use of unrotated factors and unrotated components (Hotelling, 1936), (d) hierarchical factor analysis (Holzinger & Swineford, 1937), (e) orthogonalized hierarchical factor analysis (Schmid & Leiman, 1957), and (f) bifactor analysis (Holzinger & Harman, 1938; Holzinger & Swineford, 1937; Mansolf & Reise, 2016).

Pardon typos and spelling errors-Message may be sent from iPhone and I've always had spelling problems :)

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Kevin S. McGrew, PhD
Educational & School Psychologist
Director
Institute for Applied Psychometrics (IAP)
https://www.themindhub.com
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