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Record W4402062956 · doi:10.17691/stm2024.16.4.03

Assessment of Accuracy of Spatial Object Localization by Means of Mono and Stereo Modes of Visual-to-Auditory Sensory Substitution in People with Visual Impairments (a Pilot Study)

2024· article· en· W4402062956 on OpenAlexaff
Anastasia S. Butorovа, Egor A. Koryukin, Nadiya M. Khomenko, Alexander Sergeev

Bibliographic record

VenueSovremennye tehnologii v medicine · 2024
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersUral Federal UniversityUral Branch, Russian Academy of Sciences
KeywordsCube (algebra)Sensory substitutionComputer visionStandard deviationArtificial intelligenceComputer scienceStereopsisAbsolute deviationSensory systemSIGNAL (programming language)MathematicsPsychologyGeometryStatistics

Abstract

fetched live from OpenAlex

is to assess the accuracy of spatial object localization in mono and stereo modes of visual-to-auditory sensory substitution by means of the developed system tested on persons with normal or corrected-to-normal vision. Materials and Methods: on a working surface. The researcher placed the cube in one of 20 positions on the working surface in a pseudo-random order. Results: -axis deviation between modes. On average, participants tended to underestimate the distance to the cube when it was on the left, right, or far side of the working surface, and overestimate the distance to the cube when it was on the near side of the working surface. Conclusion: Tests demonstrated that the accuracy of object localization in stereo mode can be improved by increasing the time for training the participants and by showing them more presentations. The results of the study can be used to develop assistive techniques for people with visual impairments, to manufacture medical equipment, and create brain-computer interfaces.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.035
GPT teacher head0.355
Teacher spread0.319 · 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 designBench or experimental
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

Citations2
Published2024
Admission routes1
Has abstractyes

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