Adjectival polarity and the processing of scalar inferences
What it is about
Bott and Noveck (2004) found that judging "some" as false (the scalar inference "not all") takes longer than judging it true. This is the B&N effect, usually read as a processing cost of scalar inference. Later studies found it for some scales and not others. The authors test the polarity hypothesis: only scalar words that are positive give the effect, and negative words do not. They use 16 adjectival scales in a sentence-picture verification task with 50 Mechanical Turk participants; three were removed for accuracy below 80%. Each sentence-picture pair appeared three times, giving 144 trials. Polarity is not a yes/no label. It is a continuous score: the first principal component of four corpus and psychological diagnostics, which explains 47% of the variance. The diagnostics are "how" questions, "twice as" ratios, valence ratings and overall frequency. The model shows a three-way interaction of condition, response and polarity (b = 0.09, SE = 0.01, t = 6.00, p < .001). Of the seven scales with a B&N effect, six were positive; "youthful" is the exception. The authors conclude that the cost lies in verifying negative information, not in deriving the inference.
Ideas
- I1.
en/src/morph.rsand 15. Adjectives and adverbs: a deterministic polarity score for Mova's adjectives, built from four counts (how-question neutrality, ratio-phrase counts, frequency, a valence list) and combined as a first principal component, with lexicon order fixed by BTreeMap. Check the licence of the valence and corpus data before use. - I2.
prag/src/infer.rs: when the weak scalar word is negative (low, scarce), keep its scalar inference in positive form ("scarce but present") rather than as a double negation. Test on the implicature items inworld/src/knowledge.rs. - I3. Negative control: pairs with the same picture and different polarity (warm and cool in the paper) should give different implicature rates in Mova's rule output. A shuffled polarity table must score worse.
- I4. Count negations per frame in
prag/src/frames.rsand log the number of rule steps for each implicature. If the step count tracks negation rather than inference, the cost is in the negation, as the paper argues.
For Mova
The chapters are 15. Adjectives and adverbs (scalar adjectives and antonyms), 27. Negation (double negation) and 33. Implicature and presupposition. The code is en/src/morph.rs for antonym pairs, prag/src/infer.rs for scalar inference and prag/src/frames.rs for negation counts. The measure: agreement of the polarity score with the paper's ordering of the 16 scales, run twice for determinism.
Cite
@article{tiel2021adjectival,
title = {{Adjectival polarity and the processing of scalar inferences}},
author = {Tiel, Bob van and Pankratz, Elizabeth},
journal = {Glossa: a journal of general linguistics},
year = {2021},
doi = {10.5334/gjgl.1457},
url = {https://doi.org/10.5334/gjgl.1457}
}References
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- TO CITE THIS ARTICLE: van Tiel, Bob and Elizabeth Pankratz. 2021. Adjectival polarity and the processing of scalar inferences. Glossa: a journal of general linguistics 6(1): 32. 1–21. DOI: https:// doi.org/10.5334/gjgl.1457 ↗ our card
- Submitted: 02 October 2020 Accepted: 07 February 2021 Published: 31 March 2021 find
- COPYRIGHT: © 2021 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http:// creativecommons.org/ licenses/by/4.0/. Glossa: a journal of general linguistics is a peer-reviewed open access journal published by Ubiquity Press. ↗
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