Counting ‘uhm’s: How tracking the distribution of native and non-native disfluencies influences online language comprehension

Abstract

A B S T R A C TDisfluencies, like uh, have been shown to help listeners anticipate reference to low-frequency words. The associative account of this 'disfluency bias' proposes that listeners learn to associate disfluency with low-frequency referents based on prior exposure to non-arbitrary disfluency distributions (i.e., greater probability of low-frequency words after disfluencies). However, there is limited evidence for listeners actually tracking disfluency distributions online. The present experiments are the first to show that adult listeners, exposed to a typical or more atypical disfluency distribution (i.e., hearing a talker unexpectedly say uh before high-frequency words), flexibly adjust their predictive strategies to the disfluency distribution at hand (e.g., learn to predict high-frequency referents after disfluency). However, when listeners were presented with the same atypical disfluency distribution but produced by a non-native speaker, no adjustment was observed. This suggests pragmatic inferences can modulate distributional learning, revealing the flexibility of, and constraints on, distributional learning in incremental language comprehension.

Prediction of upcoming speech under fluent and disfluent conditions: eye tracking evidence from immersive virtual reality

“…Earlier work suggests that listeners may flexibly adapt their predictive behaviour to distributional aspects of the linguistic input they receive (Bosker et al, 2019;Heyselaar et al, 2018). When comparing the paired critical verb conditions in Experiment 2, we observed that the model was improved when a random smooth for trial number was included.…”

“…The authors proposed that participants learned that predicting the upcoming speech was no longer beneficial. Furthermore, Bosker et al (2019) recently demonstrated that people quickly adapt to the distribution of disfluencies in speech within the present context. Increasing the proportion of disfluencies ("uh") that occurred before highly frequent words increased anticipatory fixations towards highly frequent referents.…”

“…The latter proposition suggests that listeners learn from the non-arbitrary distribution of disfluencies in speech, such as the tendency of disfluencies to occur when the speaker is under higher cognitive load, speaking about topics with more expressive flexibility, or when trying to retrieve low frequency words (Bortfeld et al, 2001;Fraundorf & Watson, 2014;Schachter, Christenfeld, Ravina, & Bilous, 1991). Listeners are assumed to apply knowledge about when disfluencies typically occur to anticipate upcoming speech and prepare for comprehension to be demanding (Bosker, 2014;Bosker, van Os, Does, & van Bergen, 2019). As such, under natural circumstances, listeners could use subtle disfluencies in perceived speech to inform their predictions.…”

Discourse Markers Activate Their, Like, Cohort Competitors

“…We do not believe that this is a principal distinction between DM like and other temporal delays in speech, such as disfluencies. In fact, other studies have also failed to substantiate the Temporal Delay Hypothesis for disfluencies, such as filled pauses (Bosker et al, 2014(Bosker et al, , 2019Fox Tree, 2001;Wester et al, 2015). Although the present study was not designed for this purpose, future work could further assess and compare the processing of such "collateral signals" as like and disfluencies by testing, for instance, whether DM like also triggers the prediction of more complex referents, much like filled pauses do (Bosker et al, 2014(Bosker et al, , 2019.…”

“…Although these types of disfluencies have a negative effect on metalinguistic ratings of fluency (Bosker et al, 2013;van Os et al, 2020), they have been argued to actually facilitate online speech processing (Bailey & Ferreira, 2007). For instance, filled pauses can help listeners predict that the following referent is difficult to name, as evidenced by anticipatory looks to unknown or lowfrequency referents in eye-tracking studies (Arnold et al, 2007;Barr & Seyfeddinipur, 2010;Bosker et al, 2014Bosker et al, , 2019Heller et al, 2014). They have also been argued to enhance listeners' attention to the following word based on EEG data (Collard et al, 2008).…”