[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] Unlocking Potential: The Impact of Growth Mind-Set Interventions on Academic Achievement

Reference

Sisk, V. F., Burgoyne, A. P., Sun, J., Butler, J. L., & Macnamara, B. N. (2018). To What Extent and Under Which Circumstances Are Growth Mind-Sets Important to Academic Achievement? Two Meta-Analyses. Psychological Science, 29(4), 549-571. https://doi.org/10.1177/0956797617739704

Review

In this article, the authors investigated the relationship between growth mindsets and academic achievement. The researchers conducted two meta-analyses to determine the strength of the relationship and the effectiveness of interventions designed to increase growth mindsets on academic achievement.

The first meta-analysis examined the correlation between mindsets and academic achievement, considering potential moderating factors. The second meta-analysis explored the effectiveness of growth mindset interventions on academic achievement, also taking potential moderating factors into account. The overall effects were found to be weak for both meta-analyses. However, the study did provide some support for the theory that students with low socioeconomic status or who are academically at risk could benefit from growth mindset interventions.

This research contributes to our understanding of the importance of growth mindsets in academic achievement. While the overall effects were weak, the findings suggest that targeted interventions for specific groups, such as students with low socioeconomic status or those academically at risk, may yield positive results. Future research should continue to explore the potential benefits of growth mindset interventions and identify additional factors that may influence their effectiveness.