[Article Review] Unraveling Brain and Cognitive Changes: A Deep Dive into GALAMMs

Reference

Sørensen, Ø., Fjell, A. M., & Walhovd, K. B. (2023). Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models. Psychometrika, 88(2), 456-486. https://doi.org/10.1007/s11336-023-09910-z

Review

In their 2023 study, Sørensen, Fjell, and Walhovd introduced generalized additive latent and mixed models (GALAMMs) to analyze clustered data. They developed these models primarily to address applications in cognitive neuroscience. Their method leverages a scalable maximum likelihood estimation algorithm, utilizing advanced computational techniques like the Laplace approximation, sparse matrix computation, and automatic differentiation. Crucially, this approach allows for a variety of mixed response types, heteroscedasticity, and crossed random effects.

The authors further illustrated the applicability of GALAMMs by presenting two case studies. The first highlighted how these models could comprehensively capture lifespan trajectories of various cognitive abilities, including episodic memory, working memory, and executive function. Such findings were drawn from widely used cognitive tests like the California Verbal Learning Test, digit span tests, and Stroop tests. In their second case, the researchers explored the impact of socioeconomic status on brain structure, specifically delving into the relationship between educational and income levels with hippocampal volumes, gauged via magnetic resonance imaging (MRI). Their results posited that by integrating semiparametric estimation with latent variable modeling, GALAMMs can offer a more nuanced depiction of how both the brain and cognition evolve throughout an individual's life.

Overall, this study presents a promising tool for the analysis of complex data structures, especially in the realm of cognitive neuroscience. While the authors provided solid evidence from their case studies, it would be beneficial to see how GALAMMs fare in a broader range of applications. Moreover, the efficacy of these models in different sample sizes, beyond moderate ones, remains a question worth exploring in future research.

[Article Review] The Link Between Dysphoria and Memory: A Deeper Look

Reference

Hubbard, N. A., Hutchison, J. L., Turner, M., Montroy, J., Bowles, R. P., & Rypma, B. (2015). Depressive thoughts limit working memory capacity in dysphoria. Cognition & Emotion, 30(2), 193-209. doi:10.1080/02699931.2014.991694

Review

Hubbard et al. (2015) investigated the correlation between dysphoria - a state of unease or dissatisfaction - and working memory capacity. Their study aimed to decipher if the extended attention span of dysphoric individuals (DIs) on mood-congruent information affects their working memory (WM). In the initial study, both DIs and non-DIs displayed similar WM capacities. However, when depressive information was intertwined within a WM task in the second study, DIs exhibited a notable decrease in WM capacity for goal-focused data. The third study not only supported findings from the first two but also revealed a more significant relationship for DIs between processing speed and recall on the modified WM task. The researchers proposed that a DI’s WM capacity is undermined in the presence of depressive thoughts. Consequently, these findings suggest a potential reason behind daily memory and concentration issues correlated with a depressed mood.

The methodological approach was systematic, with each subsequent study building on the previous findings, providing a comprehensive understanding. Nevertheless, the research would have benefited from a more diverse sample, as generalizing results to broader populations might be premature. Additionally, a deeper exploration into the degree or severity of dysphoria might offer nuanced insights into its impact on WM.

Hubbard and colleagues provide an essential foundation for comprehending the relationship between dysphoria and working memory. Their work underscores the need to further investigate the cognitive repercussions of mental health disorders and offers a stepping stone for subsequent researchers.

Exploring the Dynamics of Speed and Intelligence at Cogn-IQ.org

In this article, Chew delves into the intriguing connection between the speed of information processing and intelligence, utilizing elementary cognitive tasks (ECTs) to gauge processing speed. Findings over the decades consistently show a negative correlation between reaction times to ECTs and intelligence levels, with more complex tasks amplifying these correlations. 

However, it's crucial to distinguish between processing speed and test-taking speed, as the latter relates more to personality traits. As we examine this relationship further, the role of working memory and task complexity emerges as vital in understanding the link between processing speed and intelligence, highlighting that as tasks become more demanding, the influence of processing speed grows. 

Additionally, the relationship between speed and intelligence is nuanced, influenced by item difficulty and individual capability. Challenging tasks can exhibit positive correlations with ability, adding complexity to this intricate relationship. Despite these findings, IQ tests remain a reliable metric for cognitive capability, emphasizing the need for a holistic interpretation of speed and intelligence. 

This article contributes to the broader understanding of human intelligence, showcasing the multifaceted nature of the relationship between speed and intelligence, shaped by working memory, task complexity, and individual capacity. 

Link to Full Article: Chew, M. (2011). Speed & Intelligence: Correlations And Implications https://www.cogn-iq.org/articles/speed-intelligence-correlations.html