A Meta-analysis of Syntactic Satiation in Extraction from Islands
Jiayi Lu, Michael Frank, Judith Degen
February 2024 

Sentence acceptability judgments are often affected by a pervasive phenomenon called satiation: native speakers give increasingly higher ratings to initially degraded sentences after repeated exposure. Various studies have investigated the satiation effect experimentally, the vast majority of which focused on different types of island-violating sentences in English (sentences with illicit long-distance syntactic movements). However, mixed findings are reported regarding which types of island violations are affected by satiation and which ones are not. This paper presents a meta-analysis of past experimental studies on the satiation of island effects in English, with the aim to provide accurate estimates of the rate of satiation for each type of island, test whether different island effects show different rates of satiation, explore potential factors that contributed to the heterogeneity in past results, and spot possible publication bias. The meta-analysis shows reliable satiation for adjunct islands, the Complex NP Constraint (CNPC), subject islands, the that-trace effect, the want-for construction, and whether-islands undergo satiation, albeit at different rates. No evidence for satiation is found for the Left Branch Condition (LBC). Whether context sentences were presented in the original acceptability judgment experiments predicts the differences in the rates of satiation reported across studies. Potential publication bias is found among studies testing the CNPC and whether-islands. These meta-analytic results can be used to inform debates regarding the nature of island effects, and serve as a proof of concept that meta-analysis can be a valuable tool for linguistic research.

Format:	[ pdf ]
Reference:	lingbuzz/007198 (please use that when you cite this article)
Published in:	To appear in Glossa Psycholinguistics
keywords:	satiation, island, meta-analysis, syntax
previous versions:	v3 [October 2023] v2 [June 2023] v1 [March 2023]
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