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Record W2518891083 · doi:10.1177/2329048x16667350

Neural Correlates of Reward Processing in Typical and Atypical Development

2016· article· en· W2518891083 on OpenAlexaff
Emma G. Duerden, Minha Lee, Stephanie Chow, Julie Sato, Kathleen M. Mak‐Fan, Margot J. Taylor

Bibliographic record

VenueChild Neurology Open · 2016
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsSickKids FoundationMental Health Research CanadaHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsFunctional magnetic resonance imagingCognitionPsychologyTypically developingAutism spectrum disorderAutismNeuroscienceTask (project management)Set (abstract data type)Developmental psychologyAudiologyCognitive psychologyMedicine

Abstract

fetched live from OpenAlex

Atypically developing children including those born preterm or who have autism spectrum disorder can display difficulties with evaluating rewarding stimuli, which may result from impaired maturation of reward and cognitive control brain regions. During functional magnetic resonance imaging, 58 typically and atypically developing children (6-12 years) participated in a set-shifting task that included the presentation of monetary reward stimuli. In typically developing children, reward stimuli were associated with age-related increases in activation in cognitive control centers, with weaker changes in reward regions. In atypically developing children, no age-related changes were evident. Maturational disturbances in the frontostriatal regions during atypical development may underlie task-based differences in activation.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.038
GPT teacher head0.308
Teacher spread0.270 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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