Early reading abilities are widely considered to derive in part from statistical learning of regularities between letters and sounds. Although there is substantial evidence from laboratory work to support this, how it occurs in the classroom setting has not been extensively explored; there are few investigations of how statistics among letters and sounds influence how children actually learn to read or what principles of statistical learning may improve learning. We examined 2 conflicting principles that may apply to learning grapheme-phoneme-correspondence (GPC) regularities for vowels: (a) variability in irrelevant units may help children derive invariant relationships and (b) similarity between words may force children to use a deeper analysis of lexical structure. We trained 224 first-grade students on a small set of GPC regularities for vowels, embedded in words with either high or low consonant similarity, and tested their generalization to novel tasks and words. Variability offered a consistent benefit over similarity for trained and new words in both trained and new tasks.
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Background: Pivotal Alzheimer's Disease (AD) trials typically require thousands of participants, resulting in long enrollment timelines and substantial costs. We leverage deep learning predictive models to create prognostic scores (forecasted control outcome) of trial participants and in combination with a linear statistical model to increase statistical power in randomized clinical trials (RCT). This is a straightforward extension of the traditional RCT analysis, allowing for ease of use in any clinical program.
View Article and Find Full Text PDFBackground: In Alzheimer's Disease (AD) trials, clinical scales are used to assess treatment effect in patients. Minimizing statistical uncertainty of trial outcomes is an important consideration to increase statistical power. Machine learning models can leverage baseline data to create AI-generated digital twins - individualized predictions (or prognostic scores) of how each patient's clinical outcomes may change during a trial assuming they received placebo.
View Article and Find Full Text PDFBackground: Lecanemab is a humanized IgG1 monoclonal antibody that binds with high affinity to Aβ soluble protofibrils. In two clinical study evaluations of lecanemab, Clarity AD (NCT03887455) and lecanemab phase 2 study (Study 201, NCT01767311), the drug showed statistically significant reduction in disease progression during 18 months of treatment relative to placebo. Anti-amyloid immunotherapy can result in higher rates of "pseudo-atrophy" (ie, whole brain volume loss or ventricular enlargement) relative to disease progression observed in placebo-treated subjects.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
UCL, institute Of Neurology, London, United Kingdom, United Kingdom.
Background: Proper name anomia is a common experience that can become amplified in patients with a diagnosis of dementia (PWD). The Gotcha! app aims to provide practice-based therapy for PWD to relearn the names of key people in their lives. It has been developed according to the principles of errorless learning, which have previously been shown to improve the remembering the familiar people's names and benefit the relationship between the PWD and their loved ones.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of Toronto, Toronto, ON, Canada.
Background: Since October, 2022 the Driving and Dementia Roadmap (DDR) (www.drivinganddementia.ca) - an online resource to support people with dementia (PWD), family/friend carers (FCs) and healthcare providers (HCPs) as they navigate the challenges of driving cessation - has been accessed by over 34,000 users.
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