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Record W7043228662

Sensory substitution learning using auditory input: Behavioral and neural correlates

2011· dissertation· en· W7043228662 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2011
Typedissertation
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsMontreal Neurological Institute and Hospital
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsSensory substitutionCrossmodalSensory systemFunctional magnetic resonance imagingPerceptionPerceptual learningTask (project management)Neural correlates of consciousness
DOInot available

Abstract

fetched live from OpenAlex

Sensory substitution refers to the replacement of one sensory input with another. This concept, originally developed to aid the blind, presents a scientific opportunity to study crossmodal perceptual learning and neural plasticity. Using a technique that translates vision into sound, the present dissertation examined sensory substitution learning. Four studies tested the hypotheses that mental representations of spatial information such as shape are abstract, and that they are based on involvement of common brain regions independently of sensory modality. Study 1 aimed to develop a training paradigm in auditory vision substitution. We examined the minimum amount of learning necessary to identify visual images using sound, and the effects of more extensive training on a wide range of stimuli to test the hypothesis that sensory substitution would be based on generalized crossmodal rule learning. Study 2 was a functional magnetic resonance imaging (fMRI) adaptation of study 1. Subjects were scanned before and after training during a task in which shape-coded sound was to be matched to visually presented shape. It was predicted that training would lead to sound-induced visual recruitment. Study 3 examined auditory touch substitution learning. Blindfolded sighted subjects were trained to recognize tactile shapes using shape-coded sounds and tested on a matching task. We also tested post-training transfer to vision. It was predicted that shape could be conveyed across sensory modalities. Study 4 was an fMRI adaptation of Study 3. Subjects were scanned before and after training during a task in which shape-coded sound was matched to tactually presented shape. Visual recruitment driven by non-visual inputs was predicted. Results showed that sighted people learned to extract visual or tactile patterns from auditory input. This learning was generalizable across stimuli within and across modalities, suggesting an abstract mental representation of shape. Auditory shape learning was associated with change in the functional network between the auditory cortex and the lateral occipital complex (LOC), a region known for visual shape processing. The auditory access to the LOC supports the notion that sensory specificity of the brain is not determined by the nature of the stimuli but rather by the task demand of the information to be processed.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.327
Teacher spread0.257 · 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

Citations0
Published2011
Admission routes2
Has abstractyes

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