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Record W162802047

When Perceptual Representations Defer to Grammar: Conflicting Linguistic and Perceptual Cues in Cantonese Classifiers

2008· article· en· W162802047 on OpenAlexaff
Cara Tsang, Craig G. Chambers

Bibliographic record

VenueSSRN Electronic Journal · 2008
Typearticle
Languageen
FieldPsychology
TopicCategorization, perception, and language
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNounClassifier (UML)SentencePerceptionLinguisticsNatural language processingComprehensionComputer scienceArtificial intelligenceGazeGrammarProper nounPsychology
DOInot available

Abstract

fetched live from OpenAlex

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.326
Teacher spread0.300 · 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.

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
Published2008
Admission routes1
Has abstractyes

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