Units of word recognition
Bibliographic record
Abstract
Last year at VSS, we showed that the word feature asymmetry, a high concentration of features leading to recognition in the upper half of words (Huey, 1908; Blais et al., 2009; Perea et al., 2012), cannot be accounted for by the distribution of features used in letter recognition and, thus, that words are not read by letters. We also reported a significantly smaller asymmetry towards the upper half of nonword trigrams. We hypothesised that constructs common to words and nonword trigrams were driving this asymmetry. Preliminary analyses identified two candidates: lexical trigrams and syllables. Our aim, here, was to investigate further these possible units of word recognition. Twenty observers were presented with two- and three-letter syllables (as defined in Lexique 3, New et al., 2001), as well as lexical bigrams and lexical trigrams (sequences of two or three letters found within words from Lexique 3 that are not syllables), and their non-lexical counterparts. Stimuli were blocked and presented in Arial lowercase, partially masked. The mask originated from the bottom or top and hid from 1/6 to 5/6 of the stimulus. We found that both syllabic stimuli, as well as the lexical bigrams and trigrams presented an asymmetry towards the upper half. We also found that none of these could be accounted for using the single letter data. Finally, syllables presented a significantly greater asymmetry than the lexical bigrams and trigrams. We will be discussing the implications of these findings for the units of word recognition. Meeting abstract presented at VSS 2013
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".