Oral communication often takes place in noisy environments, which challenge spoken-word recognition. Previous research has suggested that the presence of background noise extends the number of candidate words competing with the target word for recognition and that this extension affects the time course and accuracy of spoken-word recognition. In this study, we further investigated the temporal dynamics of competition processes in the presence of background noise, and how these vary in listeners with different language proficiency (i.e., native and non-native) using (...) computational modeling. We developed ListenIN (Listen-In-Noise), a neural-network model based on an autoencoder architecture, which learns to map phonological forms onto meanings in two languages and simulates native and non-native spoken-word comprehension. We also examined the model's activation states during online spoken-word recognition. These analyses demonstrated that the presence of background noise increases the number of competitor words, which are engaged in phonological competition and that this happens in similar ways intra and interlinguistically and in native and non-native listening. Taken together, our results support accounts positing a “many-additional-competitors scenario” for the effects of noise on spoken-word recognition. (shrink)

Cognitive Science, Volume 46, Issue 2, February 2022.

A classic problem in cognitive science concerns how listeners perceive and understand speech as comprised of discrete words. We propose a Syllable Inference account of spoken word recognition and segmentation, under which alternative hierarchical models of syllables, words, and phonemes are dynamically posited from cues that include current and past speech rate, with a goal of maximal prediction of sensory input. Three experiments using the Visual World eye‐tracking paradigm provide evidence supporting our proposal.