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Annalise A. LaPlume

Annalise A. LaPlume
Baycrest Health Sciences · Rotman Research Institute

BSc (Hons), MA, PhD

About

10
Publications
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Citations
Introduction
I am a postdoctoral researcher at the Rotman Research Institute, Baycrest, under an Alzheimer Society of Canada fellowship, supervised by Drs. Nicole Anderson, Brian Levine, and Angela Troyer. I study how cognitive abilities (executive functions, episodic memory, intraindividual variability) change with age, and how individual factors influence aging trajectories. For additional information, visit www.annaliselaplume.com

Publications

Publications (10)
Article
Full-text available
Objectives Age-related differences in cognition are typically assessed by comparing groups of older to younger participants, but little is known about the continuous trajectory of cognitive changes across age, or when a shift to older adulthood occurs. We examined the pattern of mean age differences and variability on episodic memory and executive...
Article
Full-text available
Musical training is popularly believed to improve children’s cognitive ability. Early research evidence, mostly correlational, suggested that musicians outperform nonmusicians on many cognitive abilities. However, recent experimental evidence has failed to replicate most benefits, leaving it unclear whether previously demonstrated effects were a di...
Article
Full-text available
The current study investigated whether long-term experience in music or a second language is associated with enhanced cognitive functioning. Early studies suggested the possibility of a cognitive advantage from musical training and bilingualism but have failed to be replicated by recent findings. Further, each form of expertise has been independent...
Article
Full-text available
Introduction: More women than men develop Alzheimer's disease, yet women perform better and show less decline on episodic memory measures, a contradiction that may be accounted for by modifiable risk factors for dementia. Methods: Associations among age, sex, modifiable dementia risk factors, and cognition were measured in a cross-sectional onli...
Article
Full-text available
Background: Reversible lifestyle behaviors (modifiable risk factors) can reduce dementia risk by 40%, but their prevalence and association with cognition throughout the adult lifespan is less well understood. Methods: The associations between the number of modifiable risk factors for dementia (low education, hypertension, hearing loss, traumatic...
Code
Fully reproducible Rmarkdown code templates to create graphs using the using the ggplot2 package in R. Templates are completely annotated and easy to use, even for beginners to R. Many individuals are attracted to the elegance and customization of graphs in R, but may not have the time to deal with the steep learning curve of R. I provide simple a...
Article
Full-text available
Objectives The present study explores the effect of visual art training on people with dementia, utilizing a randomized control trial design, in order to investigate the effects of an 8-week visual art training program on cognition. In particular, the study examines overall cognition, delayed recall, and working memory, which show deficits in peopl...
Article
Full-text available
Spacing effects during retention of verbal information are easily obtained, and the effect size is large. Relatively little evidence exists on whether motor skill retention benefits from distributed practice, with even less evidence on complex motor skills. We taught a 17-note musical sequence on a piano to individuals without prior formal training...
Article
Full-text available
Yoga, an ancient Indian healing tradition, has been shown to provide a wide range of physical, psychological, and emotional benefits to general and clinical populations. Recent research suggests that yoga may also enhance cognitive health, in particular, sustained attention abilities (Hogasandra & Ganapat, 2013). The effect of yoga on other cogniti...

Questions

Question (1)
Question
I ran a hierarchical regression comparing different models, in order of complexity. The first step was not significant (the simplest model vs a more complex model). However the next two steps were significant (a more complex model vs an even more complex model). Since the first step was not significant, I think I should stop here and select the simplest model (Model 1).
Below are the results from the hierarchical regression for model comparison
Res.Df RSS Df Sum of Sq F Pr(>F)
1 45768 311819838
2 45766 311802160 2 17678 1.2981 0.2730717
3 45764 311696461 2 105699 7.7612 0.0004265 ***
4 45762 311613774 2 82687 6.0715 0.0023096 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
I also looked at the AIC and BIC values. The AIC follows the pattern for the p-values: Model 2 is not better than Model 1, but Models 3 and 4 are better (the change in AIC for each step is 1.4, -11.5, -8.1). The BIC suggests that Model 1 is the best (the change in BIC for each step is 18.9, 5.9, 9.3). According to the BIC, I also think I will select Model 1.
Can someone please explain to me why the more complex models show up as significantly better when the earlier model was not?
For reference, the models being tested are segmented regression models as follows
Model 1: A linear model (one slope); int_r ~ age + sex + education
Model 2: A model with one breakpoint in a predictor variable (one psi estimate)
int_r ~ age + sex + education + year + U1.age + psi1.age
Model 3: A model with two breakpoints (two psi)
int_r ~ age + sex + education + year + U1.age + U2.age + psi1.age +
psi2.age
Model 4: A model with three breakpoints
int_r ~ age + sex + education + year + U1.age + U2.age + U3.age +
psi1.age + psi2.age + psi3.age
The hierarchical regression measured
Step 1: Model 1 vs Model 2
Step 2: Model 2 vs Model 3
Step 3: Model 3 vs Model 4

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