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Record W2061769654 · doi:10.1167/7.9.786

Prism adaptation reduces the 'disengage deficit' in right brain damage patients

2010· article· en· W2061769654 on OpenAlexaff
Christopher L. Striemer, James Danckert

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldNeuroscience
TopicSpatial Neglect and Hemispheric Dysfunction
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPrism adaptationNeglectPrismAdaptation (eye)PsychologyCovertNeuroscienceCognitionCognitive psychologyAttentional biasDevelopmental psychologyAudiologyMedicinePsychiatryOptics

Abstract

fetched live from OpenAlex

Recent research has shown that prism adaptation alleviates some of the symptoms of neglect. Although prism adaptation can aid patients with neglect, the mechanisms underlying these benefits remain largely unknown. One way in which prisms may work is by altering attentional orienting mechanisms which are known to be impaired in neglect. To investigate this hypothesis we tested four right brain damaged patients (two with neglect) on a reflexive covert attention task before and after rightward prism adaptation and compared them to a group of healthy controls (N=26) who underwent sham prism adaptation. Results demonstrated that rightward prism adaptation reduced both the rightward attentional bias, and the disengage deficit in patients with right brain damage irrespective of the presence of neglect. This provides for the first time, a cognitive mechanism by which prism adaptation may aid patients with neglect. Furthermore, the current results suggest that prism adaptation may also be useful for rehabilitating spatial deficits in right brain damage patients without neglect.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.269
Teacher spread0.255 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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