Research Alert: Cognitive Networks for Knowledge Modeling: A Gentle Introduction for Data‐ and Cognitive Scientists

Quick FYI email-based Research Alert.  Is open access👍

 

 

Cognitive Networks for Knowledge Modeling: A Gentle Introduction for Data- and Cognitive Scientists - Haim - 2026 - WIREs Cognitive Science - Wiley Online Library 

https://wires.onlinelibrary.wiley.com/doi/10.1002/wcs.70026

 

ABSTRACT

In this paper, we introduce the reader to the field of cognitive network science, that is, the application of network science methods to study human cognition and knowledge structures. Cognitive networks are representations of associative knowledge between concepts in a cognitive system apt at acquiring, storing, processing and producing language, that is, the mental lexicon. In a cognitive network, nodes represent concepts with links expressing relations, such as semantic, syntactic, phonological and visual connections, for example, “canine” and “dog” (nodes) linked by “being synonyms” (link). Hence, cognitive networks represent associative knowledge in mathematical, measurable and quantifiable ways. Can such structure be used to gain insights over cognitive phenomena? We explore this research question by reviewing recent, pioneering key applications and limitations of cognitive networks across visual, auditory, and semantic language processing tasks, either in healthy or clinical populations. We also review applications of cognitive networks modeling language acquisition, reconstructing text content and assessing creativity or personality traits in individuals. Our paper also gently introduces the reader to mathematical notations, definitions and measures about single-layer and multiplex networks as well as hypergraphs. Last but not least, across phonological, semantic and syntactic networks, we guide the reader through relevant psychological frameworks, datasets and software packages that might all aid current and future cognitive network scientists.

This article is categorized under:

• Psychology > Memory
• Psychology > Theory and Methods
• Linguistics > Cognitive

Graphical Abstract

Cognitive network science helps organize associative knowledge—that is, the connections between concepts. These connections play a key role in cognitive processes such as language understanding and context interpretation, even though they are not obvious in language use. For example, we do not see syntactic links, as depicted in the figure, between words in a written or spoken text. Further information can be highlighted visually in cognitive representations, such as the emotional valence of words (here indicated with the colors blue for positive, red for negative, gray for neutral and purple for a connection between positive and negative concepts). Giving structure to knowledge via cognitive networks represents a new frontier. This gentle primer offers a clear overview and introduces tools for cognitive scientists and psychologists interested in exploring cognitive representations.

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Research alert: Creative #self-beliefs and #criticalthinking disposition: A #network#analysis approach

Quick email research alert blog post.  Is an open access article 👍

 

Creative self-beliefs and critical thinking disposition: A network analysis approach - ScienceDirect 
https://www.sciencedirect.com/science/article/pii/S0160289626000206#f0005

Click on image to enlarge for better readability

 

 

Abstract

This study examined the relationship between creative self-beliefs and critical thinking disposition using a network analysis approach. The sample comprised 672 final-year undergraduates who completed the Short Scale of Creative Self (SSCS) and the Critical Thinking Disposition Scale (CTDS). A regularized partial correlation network estimated via EBICglasso revealed that the two domains were largely organized into distinct but weakly connected communities. Although cross-construct associations were generally small, bridge centrality analyses identified specific items—particularly those reflecting openness to new ideas and perceived capacity to cope with complex situations—as key connectors between the two systems. Classical centrality indices further indicated that creative personal identity constituted the structural core of the creative self-beliefs network, whereas reflective self-monitoring emerged as central within critical thinking disposition. Community detection analysis further supported a two-community structure consistent with partial segregation between constructs. Overall, the findings suggest that creative self-beliefs and critical thinking disposition function as relatively differentiated yet selectively integrated systems. These results highlight the importance of targeting specific bridge processes when designing educational interventions aimed at fostering both creative and critical thinking in higher education.

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

Research alert: #Cognitive #networks for #knowledge modeling: A gentle introduction for data- and cognitive scientists

Click on image to enlarge for easy reading.

An open access article available here. 

ABSTRACT 

In this paper, we introduce the reader to the field of cognitive network science, that is, the application of network science methods to study human cognition and knowledge structures. Cognitive networks are representations of associative knowledge between concepts in a cognitive system apt at acquiring, storing, processing and producing language, that is, the mental lexicon. In a cognitive network, nodes represent concepts with links expressing relations, such as semantic, syntactic, phonological and visual connections, for example, “canine” and “dog” (nodes) linked by “being synonyms” (link). Hence, cognitive networks represent associative knowledge in mathematical, measurable and quantifiable ways. Can such structure be used to gain insights over cognitive phenomena? We explore this research question by reviewing recent, pioneering key applications and limitations of cog-nitive networks across visual, auditory, and semantic language processing tasks, either in healthy or clinical populations. We also review applications of cognitive networks modeling language acquisition, reconstructing text content and assessing creativity or personality traits in individuals. Our paper also gently introduces the reader to mathematical notations, definitions and measures about single-layer and multiplex networks as well as hypergraphs. Last but not least, across phonological, semantic and syntactic networks, we guide the reader through relevant psychological frameworks, datasets and software packages that might all aid current and future cognitive network scientists.

Research alert-very important article: Beyond Working Memory Capacity: Attention Control as the Underlying Mechanism of Cognitive Abilities - #cognitive #intelligence #Gwm #attentionalcontrol #AC #workingmemory #WJIV #WJV #schoolpsychology #schoolpsychologists #cognition

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Very important article (open source..click here to read/download) regarding cognitive functioning and working memory capacity and attentional control. For at least 15 years I’ve been monitoring research on the attentional-control working memory complex system (AC-Gwm)…(click here for numerous posts regarding the important of AC-Gwm).  I’m convinced that the AC-Gwm complex system is one of the core cognitive efficiency systems that helps us understand general intellectual functioning.  It has been found to be important in cognitive functioning and also in various forms of psychopathology.  

Abstract

Working memory capacity (WMC) has long served as a central indicator of individual differences in complex cognition. However, growing evidence suggests that a substantial portion of its predictive power may reflect attention control (AC)—including goal maintenance, interference management, and inhibition—rather than storage capacity alone. This review synthesizes findings across six domains: (1) perception and sensory discrimination, (2) learning and problem solving, (3) cognitive control and decision making, (4) retrieval and memory performance, (5) multitasking and real-world performance, and (6) clinical applications. Across these areas, WMC-related effects frequently align with demands on AC, though the strength and nature of this alignment vary by domain. We highlight the importance of incorporating reliable AC measures and recommend latent-variable approaches to more clearly separate storage, control, and representational processes underlying complex performance.

Keywords: attention control; working memory capacity; executive attention; fluid intelligence; interference control; individual differences; latent-variable modeling; cognitive measurement

From conclusions:

Across six domains, the evidence reviewed here suggests that the broad predictive power traditionally associated with WMC often reflects the AC operations embedded within complex-span tasks—particularly goal maintenance, interference suppression, and disengagement. This does not diminish the importance of WMC as a measurable construct; rather, it clarifies that many WMC tasks draw on AC mechanisms, which are more directly tied to performance in interference-heavy contexts.

McGrew et al. (2023) identified a similar AC-Gwm complex system in a recent WJ V psychometric network analysis study.  See the relevant research and comments  from that article below (click here to access and download the paper).  Again, a reminder—click on image to enlarge for easy reading.

Journal of Intelligence “Best Paper Award” for McGrew, Schneider, Decker & Bulut (2023) Psychometric network analysis of CHC measures - #psychometric #networkanalysis #intelligence #CHC #WJIV #bestpaper #schoolpsychology #schoolpsychologist

Today I (Kevin McGrew), and colleagues Joel Schneider, Scott Decker, and Okan Bulut, were pleased to learn that our recent 2023 Journal of Intelligence article listed above (open access—click link to read or download) was selected as 1 of 2 “Best Paper Awards” for 2023.  

As stated at the journal award page, “The Journal of Intelligence Best Paper Award is granted annually to highlight publications of high quality, scientific significance, and extensive influence. The evaluation committee members choose two articles of exceptional quality that were published in the journal the previous year and announce them online by the end of June.”

Below is the abstract and two figures that may pique your interest. We thank the members of the JOI evaluation committee.

Abstract

For over a century, the structure of intelligence has been dominated by factor analytic methods that presume tests are indicators of latent entities (e.g., general intelligence or g). Recently, psychometric network methods and theories (e.g., process overlap theory; dynamic mutualism) have provided alternatives to g-centric factor models. However, few studies have investigated contemporary cognitive measures using network methods. We apply a Gaussian graphical network model to the age 9–19 standardization sample of the Woodcock–Johnson Tests of Cognitive Ability—Fourth Edition. Results support the primary broad abilities from the Cattell–Horn–Carroll (CHC) theory and suggest that the working memory–attentional control complex may be central to understanding a CHC network model of intelligence. Supplementary multidimensional scaling analyses indicate the existence of possible higher-order dimensions (PPIK; triadic theory; System I-II cognitive processing) as well as separate learning and retrieval aspects of long-term memory. Overall, the network approach offers a viable alternative to factor models with a g-centric bias (i.e., bifactor models) that have led to erroneous conclusions regarding the utility of broad CHC scores in test interpretation beyond the full-scale IQ, g.

Keywords: 

CHC; Cattell–Horn–Cattell theory; intelligence; cognitive abilities; psychometric network analysis; process overlap theory; dynamic mutualism; factor analysis; general intelligence

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Research Byte: A special contribution from #spatial ability to #math word problem solving: Evidence from #SEM and #networkanalysis

 

Click here to see journal page.

Abstract

There is a growing body of research into the factors contributing to math word problem solving. However, these studies usually use limited number of potential predictors (precluding assessing of their contribution in comparison with other factors or “g” general intelligence) and some predictors (such as analogical and hypothetical reasoning) are largely omitted. Thus, the aim of the current study was to explore contributions of different types of reasoning to math word problem solving and whether these contributions have added value compared with each other and general cognitive ability. Chinese schoolchildren in Grades 3 (N = 199; Mage = 102.4 months), 4 (N = 162; Mage = 114.6), 5 (N = 174; Mage = 126.1) and 6 (N = 180; Mage = 138.6) completed 8 tasks tapping into spatial, mechanical, verbal, mathematic, hypothetical and analogical reasoning. Our data showed that when 6 general cognitive factors load onto General cognitive ability factor in a Structural Equation Model (SEM), only spatial visualization has additional contribution to Word problem solving factor. Gaussian Graphical models (GGMs) showed that 2 verbal tasks and spatial visualization showed stable (present in at least 3 out of 4 grades) contributions to both word problem solving tasks. Analogical reasoning showed contribution to process of word problem solving only. To sum up, both SEM and GGMs converged on the importance of spatial ability for math word problems solving. Our results call for verbal and spatial ability to be routinely assessed and targeted by educational interventions within math curriculum.

A Psychometric Network Analysis of CHC Intelligence Measures: Implications for Research, Theory, and Interpretation of Broad CHC Scores "Beyond g"

(Note.  I’ve made several similar posts with a similar message on several social media outlets over the last 1.5 years)

Yes.  This may be seen as a brag post (I plead the fifth). But, I really want (need?) to share this recent publication (January 2023).  Why? Because, after 40 years of scholarship, I consider this article (which is open access and can be downloaded and read freely) to be one of my 5 top peer-reviewed research publications. The article is part of a special issue (Assessment of Human Intelligence-State of the Art in the 2020s) of the Journal of Intelligence, edited by Alan Kaufman et al. Warning—it is a long article. The article is the result of collaboration with Joel Schneider, Scott Decker and Okan Bulut. 

The content of the article pushes the “edge of the envelop” regarding intelligence theories and testing via the use of exploratory psychometric network analysis (PNA) within the context of network non-g (i.e., psychometric g) models of intelligence. This approach represents an emerging paradigm shift for thinking about intelligence theories and testing. As stated by Savi et al. (2021) "factor analysis models dominated the 20th century of intelligence research, but network models will dominate the 21st."  I believe Savi et al. are more-or-less correct. I believe PNA and non-g network models can move intelligence theories and testing forward—as they have become stagnant via the repeated use of "common cause" descriptive and taxonomic-generating factor analysis methods.  Used in isolation, factor analysis-based intelligence test and theory models constrain school psychologists and other assessment professionals from moving forward (as described in the paper).  For far too long, especially in school psychology, we have been "stuck on g" factor analysis based models of test interpretation.

As stated in our article, "newer non-g emergent property theories of intelligence might lead to better intervention research for individuals who have been marginalized by society. Holden and Hart (2021) suggest that network-based non-g theories, particularly those that feature Gwm-AC mechanisms [the working memory-attentional control complex] (process overlap theory in particular) may hold promise as a vehicle for improving, and not harming, social justice and equity practices and valued outcomes for individuals in marginalized groups" (McGrew et al., 2023).  Read the original Holden and Hart article if you are interested in the social justice implications of a new way of thinking about intelligence grounded in modern network non-g conceptualizations of intelligence.

Even if the methodological material is not your cup of tea, much of the McGrew et al. (2023) introduction is relevant to assessment practitioners. Also, several sections in the discussion deal with practical implications for understanding new insights into intelligence theories, broad cluster test interpretation in general, and some strengths and weaknesses of the WJ IV CHC test and cluster scores. 

If you are not familiar with the Journal of Intelligence (JOI), I would suggest SPs take a look. It is not the Intelligence journal from ISIR. It is the "new kid on the block" and has quickly become a prestigious open access publication outlet with a top notch editorial board. Since it is open access, all articles can be downloaded, read, and shared freely—an awesome free source of emerging thinking in the field of intelligence. JOI is publishing interesting articles from a wide variety of perspectives by a diversity of scholars interested in intelligence, cognition, and related topics. It has become one of my favorite journals the past few years. 

Finally, exploratory hierarchical psychometric network analysis methods (along with traditional structural analysis methods) were applied to the WJ V norm data—these results will be in the WJ V Technical Manual (LaForte, Dailey, McGrew, 2025).

 My WJ IV conflict of interest (COI) is included in the linked PDF article.  My WJ V COI and additional COI information can be found at the MindHub web portal.