No abstract

For much of its history, categorical perception was treated as a foundational theory of speech perception, which suggested that quasi-discrete categorization was a goal of speech perception. This had a profound impact on bilingualism research which adopted similar tasks to use as measures of nativeness or native-like processing, implicitly assuming that any deviation from discreteness was a deficit. This is particularly problematic for listeners like heritage speakers whose language proficiency, both in their heritage language and their majority language, is questioned. However, we now know that in the monolingual listener, speech perception is gradient and listeners use this gradiency to adjust subphonetic details, recover from ambiguity, and aid learning and adaptation. This calls for new theoretical and methodological approaches to bilingualism. We present the Visual Analogue Scaling task which avoids the discrete and binary assumptions of categorical perception and can capture gradiency more precisely than other measures. Our goal is to provide bilingualism researchers new conceptual and empirical tools that can help examine speech categorization in different bilingual communities without the necessity of forcing their speech categorization into discrete units and without assuming a deficit model.

Listeners can account for systematic variability between talkers, which is learned over exposure to multiple talkers. Previous research suggests that listeners can both generalize prior knowledge of phoneme categories from a familiar to a novel talker (Eisner & McQueen, 2005; Kraljic & Samuel, 2006, 2007) and individuate talkers, preventing generalization (Luthra, Mechtenberg, & Myers, 2021; Tamminga, Wilder, Lai, & Wade, 2020). It is unclear how listeners balance these competing demands. Participants (n = 160) learned two novel talkers (one male and one female voice) with a unique voice onset time (VOT) boundary across two days. On each day, participants were passively exposed to a bimodal distribution of VOTs from one talker, then tested on a second talker (uniform distribution). Day 1 assessed how listeners generalize to a novel talker while Day 2 assessed how the talker that was learned on Day 1 is individuated from the new talker. We found evidence for generalization after Day 1 but little evidence of learning after learning both talkers on Day 2. Two follow-up experiments using interleaved designs and supervised learning also showed little evidence for individuation. This suggests that listeners are likely accounting for variability by shifting their VOT boundary to match current input.