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Record W7119236535

Learning to reach with error amplification post-stroke : the contributions of explicit and implicit adaptation processes

2025· other· en· W7119236535 on OpenAlexaff
Beverley C. Larssen

Bibliographic record

VenuecIRcle (University of British Columbia) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMotor learningCompetence (human resources)Knowledge of resultsDreyfus model of skill acquisitionVisual feedbackImplicit learningPsychological interventionAdaptation (eye)
DOInot available

Abstract

fetched live from OpenAlex

Error augmentation (EA) is a practice technique that uses visual feedback to amplify errors during reaching movements. EA has been used to improve motor skill acquisition in controls and arm motor control post-stroke. However, there is uncertainty about the technique’s effectiveness and underlying learning processes. In this dissertation I tested the efficacy of EA for improving adaptation of a novel visuomotor skill and investigated the learning processes engaged when practicing with EA. Given mixed findings about EA’s effectiveness, an aim was to identify individual difference factors associated with response to EA interventions including age, spatial working memory, and structural differences in brain networks resulting from stroke lesions. I also tested how EA affects motivation, to see if exaggerating errors makes practice feel more challenging and less enjoyable. In Experiments 1 and 2, participants learned to adapt reaches to rotated cursor feedback with and without EA. EA enhanced detection of rotated cursor feedback and promoted explicit strategy use. Despite increasing error awareness, EA did not improve performance or learning for younger or older adults. Meanwhile, EA benefited practice performance but not learning for individuals with stroke. Older adults showed less explicit and overall adaptation to rotated feedback than younger adults (Experiment 1). Compared to older adults, individuals with stroke achieved even less explicit and overall adaptation, with many showing impaired ability to develop an explicit strategy (Experiment 2). Across both experiments, mental rotation abilities were important for explicit adaptation. EA made performance appear worse and negatively impacted perceived competence for all participants, but only younger adults rated practice with EA as less enjoyable than practice without. In Experiment 3, I tested if stroke participants with explicit adaptation impairments could apply a provided strategy. Relative to older adults, a subgroup of stroke participants had impaired adaptation performance when using a strategy, which was associated with lesion-driven structural disconnection in the right pre-central, post-central, and supramarginal gyri. Overall, this dissertation advances our understanding of EA’s effect on adaptation learning processes, how stroke impacts explicit adaptation, and brain regions that may be critical for integrating strategies when adapting reaching movements in novel feedback environments.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.201
Teacher spread0.193 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2025
Admission routes1
Has abstractyes

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