International Journal of Engineering and Information Systems (IJEAIS)   Year: 2022 | Volume: 6 | Issue: 1 | Page No.: 1-25 Cross-Linguistic Frequency and the Learnability of Semantics: Artificial Language Learning Studies of Evidentiality Download PDF Nigora Abdiyeva, Durdona Khasanova Abstract: It is often assumed that cross-linguistically more prevalent distinctions are easier to learn (Typological Prevalence Hypothesis; TPH). Prior work supports this idea in phonology, morphology and syntax but has not addressed semantics. Using Artificial Language Learning experiments with adults, we test predictions made by the TPH about the relative learnability of semantic distinctions in the domain of evidentiality, i.e., the linguistic encoding of information source. As the TPH predicted, when exposed to miniature evidential morphological systems, adult speakers of English whose language does not encode evidentiality grammatically learned the typologically most prevalent system (marking indirect, reportative information) better compared to less-attested systems (Experiments 1-2). Similar patterns were observed when non-linguistic symbols were used to encode evidential distinctions (Experiment 3). Our data support the conjecture that some semantic distinctions are marked pre-ferentially and acquired more easily compared to others in both language and other symbolic systems.