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Record W4383301930 · doi:10.1111/ejn.16075

Deafness induces complete crossmodal plasticity in a belt region of dorsal auditory cortex

2023· article· en· W4383301930 on OpenAlexafffund
Yaser Merrikhi, Ali Mirzaei, Melanie A. Kok, M. Alex Meredith, Stephen G. Lomber

Bibliographic record

VenueEuropean Journal of Neuroscience · 2023
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsWestern UniversityMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada Foundation for Innovation
KeywordsCrossmodalSomatosensory systemNeuroscienceSensory systemAuditory cortexPsychologyVisual cortexNeuroplasticitySensory deprivationPerceptionSensory stimulation therapyAudiologyVisual perceptionMedicine

Abstract

fetched live from OpenAlex

Many neural areas, where patterned activity is lost following deafness, have the capacity to become activated by the remaining sensory systems. This crossmodal plasticity can be measured at perceptual/behavioural as well as physiological levels. The dorsal zone (DZ) of auditory cortex of deaf cats is involved in supranormal visual motion detection, but its physiological level of crossmodal reorganisation is not well understood. The present study of early-deaf DZ (and hearing controls) used multiple single-channel recording methods to examine neuronal responses to visual, auditory, somatosensory and combined stimulation. In early-deaf DZ, no auditory activation was observed, but 100% of the neurons were responsive to visual cues of which 21% were also influenced by somatosensory stimulation. Visual and somatosensory responses were not anatomically organised as they are in hearing cats, and fewer multisensory neurons were present in the deaf condition. These crossmodal physiological results closely correspond with and support the perceptual/behavioural enhancements that occur following hearing loss.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.137
GPT teacher head0.356
Teacher spread0.219 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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