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Record W2788267240 · doi:10.7554/elife.30018

Shorter cortical adaptation in dyslexia is broadly distributed in the superior temporal lobe and includes the primary auditory cortex

2018· article· en· W2788267240 on OpenAlexfundno aff
Sagi Jaffe‐Dax, Eva Kimel, Merav Ahissar

Bibliographic record

VenueeLife · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchIsrael Science FoundationGerman-Israeli Foundation for Scientific Research and DevelopmentGatsby Charitable FoundationCanadian Institute for Advanced Research
KeywordsDyslexiaAuditory cortexPsychologyTemporal lobeSensory systemNeuroscienceCortex (anatomy)PerceptionNeural adaptationAdaptation (eye)Cognitive psychologyAudiologyReading (process)Medicine

Abstract

fetched live from OpenAlex

Studies of the performance of individuals with dyslexia in perceptual tasks suggest that their implicit inference of sound statistics is impaired. Previously, using two-tone frequency discrimination, we found that the effect of previous trials' frequencies on the judgments of individuals with dyslexia decays faster than the effect on controls' judgments, and that the adaptation (decrease of neural response to repeated stimuli) of their ERP responses to tones is shorter (<xref ref-type="bibr" rid="bib22">Jaffe-Dax et al., 2017</xref>). Here, we show the cortical distribution of these abnormal dynamics of adaptation using fast-acquisition fMRI. We find that faster decay of adaptation in dyslexia is widespread, although the most significant effects are found in the left superior temporal lobe, including the auditory cortex. This broad distribution suggests that the faster decay of implicit memory of individuals with dyslexia is a general characteristic of their cortical dynamics, which also affects sensory cortices.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.041
GPT teacher head0.290
Teacher spread0.249 · 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

Citations45
Published2018
Admission routes1
Has abstractyes

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