ArticleMemory & cognition2021
When statistics collide: The use of transitional and phonotactic probability cues to word boundaries.
Article in Memory & cognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Speech Segmentation and Cross-Situational Word Learning in Parallel.Open mind : discoveries in cognitive science · 2023Article
- Combining statistics: the role of phonotactics on cross-situational word learning.Psicologia, reflexao e critica : revista semestral do Departamento de Psicologia da UFRGS · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
Statistical regularities in linguistic input, such as transitional probability and phonotactic probability, have been shown to promote speech segmentation. It remains unclear, however, whether or how the combination of transitional probabilities and subtle phonotactic probabilities influence segmentation. The present study provides a fine-grained investigation of the effects of such combined statistics. Adults (N = 81) were tested in one of two conditions. In the Anchor condition, they heard a continuous stream of words with small differences in phonotactic probabilities. In the Uniform condition, all words had comparable phonotactic probabilities. In both conditions, transitional probability was stronger in words than in part-words. Only participants from the Anchor condition preferred words at test, indicating that the combination of transitional probabilities and subtle phonotactic probabilities may facilitate speech segmentation. We discuss the methodological implications of our findings, which demonstrate that even small phonotactic variations should be accounted for when investigating statistical speech segmentation.
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