When Perceptual Representations Defer to Grammar: Conflicting Linguistic and Perceptual Cues in Cantonese Classifiers
Bibliographic record
Abstract
A growing body of evidence suggests semantic representations for linguistic expressions are grounded in perception, and that these representations guide online sentence processing. However, semantic distinctions that superficially seem grounded in perception are sometimes partially or wholly grammaticalized. To date, these cases have not been investigated in studies of real-time comprehension. We consider the case of shape classifiers in Cantonese Chinese - prenominal expressions that arguably encode size/shape characteristics of their associated nouns, but whose occurrence with nouns is determined by grammar. E.g., the classifier tiu typically precedes nouns denoting long-narrow-flexible things (e.g., ropes, snakes...). However, tiu occurs with the noun for [goldfish] even though goldfishes are not prototypically long-narrow-flexible. Conversely, although a stocking is normally long-narrow-flexible, this noun cannot occur with tiu. Thus, grammatical rules governing classifier-noun pairings can conflict with perceptually-based meanings of classifiers. An offline test of Cantonese speakers' intuitions showed they considered classifier-noun pairings to be rule-based 97.6% of the time, rather than being dependent on prototypical size/shape features of noun referents. However, perceptual information encoded by classifiers might be computed unconsciously during online comprehension, somewhat like the processing of fictive motion [1]. To assess this possibility, we employed an eye-tracking methodology known to be sensitive to perceptual representations associated with words [2]. Cantonese listeners followed spoken instructions containing a classifier-noun pairing that named a target object (e.g., tiu [snake]). Displays also contained a item that either (i) matched both the classifier's grammatical and perceptual parameters (e.g., [rope]), (ii) matched the grammatical but not the canonical perceptual parameters (e.g., [goldfish]); or (iii) matched the perceptual but not the grammatical parameters (e.g., [stocking]). Of interest was how often the competitor attracted fixations prior to eventual fixation on the target. Our findings indicated that only grammatically-legitimate competitors attracted attention as the noun phrase unfolded; the match/mismatch with classifier-denoted perceptual parameters had no effect. Thus, perceptual information encoded by classifiers did not seem to be computed during online interpretation. A second eye-tracking experiment tested whether perceptually-grounded meanings might become more apparent when target objects do not possess the canonical perceptual features conveyed by their associated classifier (e.g., a goldfish for tiu), potentially highlighting the relevance of perceptual features. Competitor pictures either matched the perceptual but not the grammatical parameters of the classifier (e.g., [stocking]), or matched both the perceptual and the grammatical parameters (e.g., [snake]). As before, the latter type of competitor attracted fixations during the initial stages of processing. However, unlike the earlier experiment, grammatically illegitimate competitors matching the classifier's perceptual parameters were now observed to attract attention. Overall, the results show perceptual features of classifiers are computed at a relatively shallow level during comprehension, and that these computations are normally dominated by rule-like grammatical constraints. Implications for theories claiming a perceptual/embodied basis for linguistic representations will be discussed. References Richardson & Matlock (2005). The integration of figurative language and static depictions. Cognition, 102, 129-138. Huettig & Altmann (2007). Visual-shape competition and control of eye fixation during the processing of words. Visual Cognition, 15, 985-1018.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".