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Record W6981685024

Evaluating methodology for assessing and training attention for youth with neurodevelopmental conditions using multiple object-tracking

2021· dissertation· en· W6981685024 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2021
Typedissertation
Languageen
FieldSocial Sciences
TopicLiterature, Language, and Rhetoric Studies
Canadian institutionsnot available
FundersFonds de Recherche du Québec - Santé
KeywordsTraining (meteorology)Quality (philosophy)CognitionData collection
DOInot available

Abstract

fetched live from OpenAlex

The challenge in measuring and training attention is demonstrated by the cognitive construct’s divergent definitions and hierarchical nature. This program of research argues that a measure of an individual’s ability to complete everyday tasks and learning capability can serve as a functional assessment of attention. Specifically, Attention Resource Capacity (ARC), or an individual’s capability to allocate attentional resources from their limited reserve to relevant information while suppressing the allocation of resources to irrelevant information, is typically associated with competency. Thus, the foundation of this dissertation is based on repurposing the traditional Multiple Object-Tracking (MOT) paradigm to (i) characterize the attention-learning dynamic across deficits in attention, learning, and intelligence as well as (ii) improve attention in youth with neurodevelopmental conditions (NDC). Manuscript 1 (Chapter 2) examines the use of the MOT task as a descriptor of individual differences in ARC and learning for youth diagnosed with an NDC. Parameters within a linear trajectory, consisting of day 1 performance as the intercept and the rate of change across fifteen repeated practice sessions as the slope, were conditioned as a function of diagnosis-specific deficits in attention (i.e., ADHD), learning (i.e., Specific Learning Disorder; SLD), and intelligence (i.e., Intellectual Developmental Delay; IDD). Individuals with an IDD diagnosis demonstrated decreased baseline MOT capability (i.e., decreased ARC), whereas participants with ADHD and/or SLD diagnosis exhibited a decreased rate of change in MOT capability (i.e., decreased learning capability; N = 101). The MOT paradigm’s ability to define and isolate condition-specific differences in ARC and learning demonstrates its potential to train attention. Manuscript 2 (Chapter 3) evaluates the feasibility to conduct a school-based, randomized controlled (RCT) trial assessing MOT’s capability to improve attention in youth diagnosed with an NDC (N = 129). The results suggested that training on a specific, accurate, adaptable and accessible task, such as MOT, is feasible in a school-setting and provides preliminary evidence of efficacy. Here, an improvement on the non-trained, objective, and clinically validated targeted outcome measure (i.e., Conners Continuous Performance Task; CPT-3) was exclusive for those training with MOT compared to passive and active controls. Finally, Manuscript 3 (Chapter 4) evaluates the efficacy of training attention with MOT, using the gold-standard, randomized controlled crossover trial (RCCT) design (N = 96). The results demonstrate an improvement in CPT-3 performance for those training on MOT; that is, before and after the crossover. In addition to replicating the results of the aforementioned feasibility study (Manuscript 2), Manuscript 3 provides evidence of efficacy for MOT training to improve attention. The approach and design used in the study exemplify the significance of experimental and methodological considerations needed to reconcile the inconsistency in the field of cognitive training. The findings from the dissertation advocate for the use of the MOT paradigm as an accessible and effective platform to both assess and improve attention in youth diagnosed with an NDC. Whereas more research is necessitated to further parse the unexplained variance exhibited across the diverse atypically developing population evaluated, a discussion of areas of opportunity to further the current work outlines the need for mechanistic and effectiveness studies. Taken together, the evaluation of viability, feasibility, and efficacy of assessment and intervention-based protocols, conducted in real-world settings, and with a population typically underserved and underrepresented in research, will advance the field of research and offer methods to improve the quality of life for individuals with attention-related challenges

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.212
GPT teacher head0.422
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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