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Record W2322951376 · doi:10.1080/17470218.2016.1172094

Increased Distractibility in Schizotypy: Independent of Individual Differences in Working Memory Capacity?

2016· article· en· W2322951376 on OpenAlexaff
John E. Marsh, François Vachon, Patrik Sörqvist

Bibliographic record

VenueQuarterly Journal of Experimental Psychology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversité Laval
FundersVetenskapsrådet
KeywordsSchizotypyPsychologyWorking memoryDevelopmental psychologyRecallCognitive psychologySchizophrenia (object-oriented programming)Memory spanAudiologyShort-term memoryCognition

Abstract

fetched live from OpenAlex

Individuals with schizophrenia typically show increased levels of distractibility. This has been attributed to impaired working memory capacity (WMC), since lower WMC is typically associated with higher distractibility, and schizophrenia is typically associated with impoverished WMC. Here, participants performed verbal and spatial serial recall tasks that were accompanied by to-be-ignored speech tokens. For the few trials wherein one speech token was replaced with a different token, impairment was produced to task scores (a deviation effect). Participants subsequently completed a schizotypy questionnaire and a WMC measure. Higher schizotypy scores were associated with lower WMC (as measured with operation span, OSPAN), but WMC and schizotypy scores explained unique variance in relation to the mean magnitude of the deviation effect. These results suggest that schizotypy is associated with heightened domain-general distractibility, but that this is independent of its relationship with WMC.

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)0.003
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.090
GPT teacher head0.345
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

Citations29
Published2016
Admission routes1
Has abstractyes

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