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Record W2946205809

Treatment of perseveration speech in a young adult with Autism Spectrum Disorder

2017· article· en· W2946205809 on OpenAlexaff
Chantel Ritter

Bibliographic record

VenueStudent Research Proceedings · 2017
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsMacEwan University
Fundersnot available
KeywordsPerseverationPsychologyAutism spectrum disorderReinforcementExtinction (optical mineralogy)Cognitive psychologyAudiologyAutismDevelopmental psychologyCognitionSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

This study was implemented to evaluate a treatment approach to reduce perseverative speech in a 21 year old adult with Autism Spectrum Disorder (ASD). This study is a brief replication of previous literature that shows differential reinforcement (DR) and extinction can be used to increase appropriate speech and decrease perseverative speech. This also intends to extend the previous literature that shows DR and extinction can be used to increase appropriate speech selected by the listener, and decrease perseverative speech. To promote appropriate speech, a turn-taking format was used during sessions while DR of non-perseverative speech and DR of on-topic speech was implemented. The turn taking sessions were presented in a multiple schedule with a discriminative stimulus that signaled the contingencies, and who was to choose the topic. Both treatments reduced perseverative speech, but only DR of on-topic speech increased appropriate turn taking during conversations. There was a rapid increasing trend of appropriate speech contingent on reinforcement, and a rapidly decreasing trend of perseverative speech. The results show that perseverative speech is sensitive to contingent attention as reinforcement, and DR and extinction can markedly reduce this speech.   Discipline: Psychology Honours Faculty Mentor: Miranda  Macauley

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.076
GPT teacher head0.409
Teacher spread0.333 · 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 teacher head, 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
Published2017
Admission routes1
Has abstractyes

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