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Record W1996191334 · doi:10.1167/13.9.1304

The components and modality-specificity of word representations in the human visual system: an adaptation study

2013· article· en· W1996191334 on OpenAlexaff
Dongkyu Choi, Hina Hanif, Catherine Hills, Jason J.S. Barton

Bibliographic record

VenueJournal of Vision · 2013
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyMorphemeWord (group theory)Speech recognitionAdaptation (eye)CommunicationComputer scienceArtificial intelligenceLinguistics

Abstract

fetched live from OpenAlex

Background: While many studies have used adaptation to probe the neural representation of faces, few have used this to examine how words are represented in the human visual system. Last year we established that word aftereffects exist and are invariant for script style (Hanif et al, J Vis 2012: 12(9): 1060). Objective: Our goals were, first, to use adaptation to examine the contribution of components of words to the word aftereffect and second, to determine if there was cross-modal transfer of aftereffects. Methods: 30 subjects participated in Experiment 1. Two pairs of compound words of equal length were chosen as base stimuli, in upper case Arial font. Ambiguous probe stimuli were created by merging different degrees of transparencies of the pairs together in an overlay, with added Gaussian noise. The 5-second adapting stimuli were either the original words, words with the component morphemes re-arranged, or a rearrangement of the original words’ letters into a meaningless string. 12 subjects participated in Experiment 2. Two pairs of words were chosen. Probe stimuli were generated by either the same method, or a morphing procedure, for comparison. In the visual condition, the original words were presented as 5-second adapting stimuli, while in the auditory condition, the adaptor was a 4.8-second tape of different individuals saying the original word every 800ms. Results: Experiment 1 generated a 17% aftereffect for whole words, while the re-arranged morphemes generated a small 5% aftereffect, and letter strings generated no aftereffect. Experiment 2 generated a 10% aftereffect for whole visual words, irrespective of probe type, but no aftereffect from auditory words. Conclusion: Visual words have a strong representation at the whole-word level, and a minor grapheme component. As found previously for face expression and age aftereffects, there was no cross-modal transfer from the auditory sense. Meeting abstract presented at VSS 2013

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0030.000

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 head0.124
GPT teacher head0.394
Teacher spread0.270 · 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
Published2013
Admission routes1
Has abstractyes

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