@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix ns1: <https://osf.io/vocab/2022/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
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    dcterms:description "Recent work suggests when listeners adapt to novel phonotactic constraints in perception, they make inferences based on their previous experience with phonotactic variation: that phonotactics vary widely between languages, but very little between individual speakers of a single dialect. In a previous set of perception experiments (see osf.io/a6pjv/ for details), listeners were exposed to talker-dependent phonotactic patterns (e.g., for Speaker A /p,t,k/ is restricted to onset while /s, ʃ, f/ is restricted to coda; Speaker B exhibits the reverse constraint). Listeners showed greater adaptation when talkers differed in their language backgrounds (e.g., one English and one French talker) vs. when talkers shared a language background (e.g., two English talkers), suggesting listeners infer that speakers who do not share a language background are unlikely to share a phonotactic grammar, based on their previous experience (and vice-versa). However, adaptation in production may differ from perception; previous work suggests production may utilize simple associative learning mechanisms that may not take high-level features, such as language background, into account. We examine this hypothesis by looking at speech errors during repetition of tongue twister syllable sequences. Participants slowly repeat twisters after model talkers, each talker exhibiting a different phonotactic constraint. The language background of these model talkers is manipulated, either sharing a language background (e.g., two English speakers) or differing in their language background (e.g., one German and one English speaker). Participants then repeat the tongue twisters quickly, with orthographic support. A simple associative model predicts error patterns will be insensitive to language background; a model including indexical features predicts results will mirror perception. "^^rdf:string ;
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