Discriminative learning predicts human recognition of English blend sources
Scott Seyfarth, Mark Myslín · eScholarship (California Digital Library) · 2014
Strict compositionality in morphological theory is problem-atic for explaining how language-users comprehend phenom-ena like the partial yet non-decomposable forms in phonaes-themes and in blends like edutainment. An alternative account, based on discriminative learning, proposes that language-users associate linguistic cues (e.g., short segment or letter strings) with multiple simultaneous possible lexical and grammatical meanings. We evaluate this account on off-line human identi-fications of partial word-forms, using English blend words as our test case. We hypothesize that readers ’ ability to parse out source meanings from written blend forms should be corre-lated with how strongly a naı̈ve discriminative reading model associates the cues in each form with the correct source mean-ings. We provide evidence for this claim in two experiments, in which the discriminative learning model reliably predicted participants ’ success rate in guessing the sources of both at-tested and novel blends. This finding supports discriminative learning as a realistic model of how readers parse wordforms and map them to meanings. Further, the result points towards a novel, precise account of blend processing.