Sensory substitution learning using auditory input: Behavioral and neural correlates
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".