Research alert: Executive functions and psychopathology: A transdiagnostic network analysis - #networkanalysis #executivefunctions #Gwm #AC #workingmemory #attentionalcontrol #psychopathology #schoolpsychologists #schoolpsychology #AC-Gwmcomplex

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Following up on my post the other day that suggests that the AC-Gwm (attentional control-working memory) complex is key to cognitive functioning, a new article implicating the same complex in preadolescent psychopathology.  It is becoming more clear that the AC-Gwm complex is central key for understanding a variety of human behaviors.  

Open access article available here.

Abstract

Mental health research is shifting toward dimensional, transdiagnostic frameworks, yet the role of executive functions (EFs) across psychopathological domains remains unclear. In this study, we examined transdiagnostic associations and potential directional pathways linking EFs with psychopathology in a large sample of preado-lescents (N = 9,119) from the Adolescent Brain Cognitive Development (ABCD) study. We employed a Gaussian graphical model (GGM) to estimate partial correlations and a directed acyclic graph (DAG) to infer potential directional influences between EFs and psychopathology. Modest associations were observed among the EFs and psy-chopathology. Working memory emerged as a central node, showing positive asso-ciations with attention problems, social problems, and rule-breaking behavior, and negative associations with anxious/depressed and somatic complaints. These results were mirrored in the DAG, which identified working memory and attention problems as key converging hubs. Sex-stratified analyses revealed notable differences in net-work structure. Our findings reveal a core transdiagnostic role for working memory in preadolescent psychopathology.

Research alert: The network architecture of general intelligence in the human connectome - #g #intelligence #cognitive #cognitive #NNT #brainnetworks #schoolpsychology #schoolpsychologists

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“In press” article available here.  As per the old Verizon cell phone ad - “its the network.”

ABSTRACT 

Advances in network neuroscience challenge the view that general intelligence (g) emerges from a primary brain region or network. Network Neuroscience Theory (NNT) proposes that g arises from coordinated activity across the brain's global network architecture. We tested predictions from NNT in 831 healthy young adults from the Human Connectome Project. We jointly modeled the brain's structural topology and intrinsic functional covariation patterns to capture its global topological 

organization. Our investigation provided evidence that g (1) engages multiple networks, supporting the principle of distributed processing; (2) relies on weak, long-range connections, emphasizing an efficient and globally coordinated network; (3) recruits regions that orchestrate network interactions, supporting the role of modal control in driving global activity; and (4) depends on a small-world architecture for system-wide communication. These results support a shift in perspective from prevailing localist models to a theory that grounds intelligence in the global topology of the human connectome. 

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.

Research alert: Toward Complementary Intelligence: Integrating Cognitive and Machine AI - #AI #machineAI #cognitiveAI #cognition #intelligence #schoolpsychologiy

In Current Directions in Psychological Science 

Unfortunately not open access.

Abstract

This article calls for complementary human-AI intelligence. Rather than redefining intelligence to fit machine capabilities, we argue for designing AI that complements and extends human cognition. We distinguish between cognitive AI, which is grounded in cognitive science to model human perception, learning, and decision-making, and machine AI, which achieves large-scale performance through data-driven optimization. Building on advances in machine learning alignment and human-AI complementarity, we propose an integrative framework that connects cognitive and machine AI across four routes: embedding integration, aligning human and machine representations; instruction encoding, using machine AI to translate goals into cognitive AI; training agents, using cognitive AI to guide and train machine AI through human-like data; and coevolving agents, enabling cognitive and machine AI to coadapt and improve together over time. These integration routes provide a foundation for complementary intelligence: systems that combine human interpretability with machine scalability and precision to enhance trust, adaptability, and human agency in complex sociotechnical environments.