Abstract

An ongoing debate in phonology concerns the treatment of cumulative constraint interactions, or ‘gang effects’, and by extension the question of which phonological frameworks are suitable models of the grammar. This paper uses a series of artificial grammar learning experiments to examine the inferences that learners draw about cumulative constraint violations in phonotactics in the absence of a confounding natural-language lexicon. I find that learners consistently infer linear counting and ganging cumulativity across a range of phonotactic violations.

Highlights

  • The treatment of CUMULATIVE CONSTRAINT INTERACTIONS is the subject of ongoing debate in phonology

  • Nasal harmony backness and nasal harmony phonotactic violation profile backness harmony was associated with a significant decrease in log-odds of endorsement (β = ―0.504, SE = 0.226, z = ―2.233, p = 0.0.026), as was the violation of nasal harmony (β = ―1.562, SE = 2.227, z = ―6.900, p < 0.001); the interaction between the two was not significant (β = ―0.486, SE = 0.363, z = ―1.292, p = 0.196)

  • In Experiment 4, I examined the other type of constraint interaction predicted by Harmonic Grammar (HG) but not by Optimality Theory (OT), counting cumulativity

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Summary

Introduction

The treatment of CUMULATIVE CONSTRAINT INTERACTIONS is the subject of ongoing debate in phonology. 552 Canaan Breiss manipulation of violations with an artificial grammar learning paradigm which imposes a ‘sandbox’ environment on the learner, where the statistics of the language being learned can be carefully controlled Note that the use of an artificial language does not render the experimental results impervious to the influence of whatever non-linguistic cognitive factors may be at play in acceptability judgements We expect such effects (though I do not model them here explicitly), but do not anticipate that they will exert an asymmetrical effect on different items in the experiments, so the within-experiment comparisons which are the focus of this paper should be unbiased

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