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Record W3049092239 · doi:10.3389/fnhum.2020.00316

Project DyAdd: Non-linguistic Theories of Dyslexia Predict Intelligence

2020· article· en· W3049092239 on OpenAlexaff
Marja Laasonen, Pekka Lahti‐Nuuttila, Sami Leppämäki, Pekka Tani, Jan Wikgren, Hanna Harno, Henna Oksanen-Hennah, Emmanuel M. Pothos, Axel Cleeremans, Matthew Dye, Denis Cousineau, Laura Hokkanen

Bibliographic record

VenueFrontiers in Human Neuroscience · 2020
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Ottawa
FundersEmil Aaltosen Säätiö
KeywordsDyslexiaPsychologyDevelopmental dyslexiaBiological theories of dyslexiaNeuropsychologyCognitive psychologyDevelopmental psychologyReading (process)CognitionNeuroscienceLinguistics

Abstract

fetched live from OpenAlex

Two themes have puzzled the research on developmental and learning disorders for decades. First, some of the risk and protective factors behind developmental challenges are suggested to be shared and some to be specific for a given condition. Second, language-based learning difficulties like dyslexia are suggested to result from or correlate with also nonlinguistic aspects of information processing. In the current study, we investigated how adults with developmental dyslexia and ADHD as well as healthy controls cluster across various dimensions designed to tap the prominent nonlinguistic theories of dyslexia. Participants were 18–55-year-old adults with dyslexia (n = 36), ADHD (n = 22), and controls (n = 35). Nonlinguistic theories investigated with experimental designs included temporal processing impairment, abnormal cerebellar functioning, procedural learning difficulties as well as visual processing and attention deficits. Latent profile analysis (LPA) was used to investigate the emerging groups and patterns of results across these experimental designs. LPA suggested three groups: 1) a large group with average performance in the experimental designs, 2) participants predominantly from the clinical groups but with enhanced conditioning learning, and 3) participants predominantly from the dyslexia group with temporal processing as well as visual processing and attention deficits. Despite the presence of these distinct patterns, participants did not cluster very well based on their original status, nor did the LPA groups differ in their dyslexia or ADHD-related neuropsychological profiles. Remarkably, the LPA groups did differ in their intelligence. These results highlight the continuous and overlapping nature of the observed difficulties and support the multiple deficit model of developmental disorders, which suggests shared risk factors for developmental challenges. It also appears that some of the risk factors suggested by the prominent nonlinguistic theories of dyslexia relate to the general level of functioning in tests of intelligence.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.039
GPT teacher head0.333
Teacher spread0.294 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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