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When does a gradual learning rule translate into gradual learning performance? This paper studies a gradient-ascent Maximum Entropy phonotactic learner, as applied to two- alternative forced-choice performance expressed as log-odds. The main result is that slow initial performance cannot accelerate later if the initial weights are near zero, but can if they are not. Stated another way, abrupt- ness in this learner is an effect of transfer, either from Universal Grammar in the form of an initial weighting, or from previous learning in the form of an acquired weighting.
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Can you judge what you don’t hear? Perception as a source of gradient wordlikeness judgements
A key observation about wordlikeness judgements, going back to some of the earliest work on the topic is that they are gradient in the sense that nonce words tend to form a cline of acceptability. In recent years, such gradience has been modelled as stemming from a gradient phonotactic grammar or from a lexical similarity effect. In this article, we present two experiments that suggest that at least some of the observed gradience stems from gradience in perception. More generally, the results raise the possibility that the gradience observed in wordlikeness tasks may not come from a gradient phonotactic/phonological grammar.
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- PAR ID:
- 10575009
- Publisher / Repository:
- Glossa
- Date Published:
- Journal Name:
- Glossa: a journal of general linguistics
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2397-1835
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Should phonotactic knowledge be modeled as categorical or gradient? In this paper, I present new data from a Turkish acceptability judgment study that addresses some limitations of previous work on this question. This study shows that gradient models account for the variability in acceptability ratings better than categorical ones. However, I suggest that the distinction between gradient and categorical models is somewhat superficial when we think of models in a mathematically general way. I propose on this basis that both categorical and gradient models have a role to play in linguistic research.more » « less
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Abstract Recent studies have documented substantial variability among typical listeners in how gradiently they categorize speech sounds, and this variability in categorization gradience may link to how listeners weight different cues in the incoming signal. The present study tested the relationship between categorization gradience and cue weighting across two sets of English contrasts, each varying orthogonally in two acoustic dimensions. Participants performed a four‐alternative forced‐choice identification task in a visual world paradigm while their eye movements were monitored. We found that (a) greater categorization gradience derived from behavioral identification responses corresponds to larger secondary cue weights derived from eye movements; (b) the relationship between categorization gradience and secondary cue weighting is observed across cues and contrasts, suggesting that categorization gradience may be a consistent within‐individual property in speech perception; and (c) listeners who showed greater categorization gradience tend to adopt a buffered processing strategy, especially when cues arrive asynchronously in time.more » « less
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To ascertain the importance of phonetic information in the form of phonological distinctive features for the purpose of segment-level phonotactic acquisition, we compare the performance of two recurrent neural network models of phonotactic learning: one that has access to distinctive features at the start of the learning process, and one that does not. Though the predictions of both models are significantly correlated with human judgments of non-words, the feature-naive model significantly outperforms the feature-aware one in terms of probability assigned to a held-out test set of English words, suggesting that distinctive features are not obligatory for learning phonotactic patterns at the segment level.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.16995/glossa.9333
