Is repairing speech errors an automatic or a controlled process? Insights from the relationship between error and repair probabilities in English and Spanish

Abstract

Speakers can correct their speech errors, but the mechanisms behind repairs are still unclear. Some findings, such as the speed of repairs and speakers' occasional unawareness of them, point to an automatic repair process. This paper reports a finding that challenges a purely automatic repair process. Specifically, we show that as error rate increases, so does the proportion of repairs. Twenty highlyproficient English-Spanish bilinguals described dynamic visual events in real time (e.g., "The blue bottle disappears behind the brown curtain") in English and Spanish blocks. Both error rates and proportion of corrected errors were higher on (a) noun phrase (NP)2 vs. NP1, and (b) word1 (adjective in English and noun in Spanish) vs. word2 within the NP. These results show a consistent relationship between error and repair probabilities, disentangled from position, compatible with a model in which greater control is recruited in error-prone situations to enhance the effectiveness of repair.

Language control in bilingual production: Insights from error rate and error type in sentence production

“…We ran a logistic multilevel mixed model for errors on nouns, including NP position as the fixed effect, and the random intercepts of subjects and items, as well as the random slope for NP position over subjects as the random effect structure. The main effect of NP position was significant ( z = 2.613, p = .009), revealing that participants made more errors on NP2 (.031 ±.02) compared to NP1 (.025 ±.02) (as previously reported also in Nozari et al, 2019). We also ran a logistic multilevel mixed model for errors on verbs, including Ambiguity as the fixed effect, the random intercept of subjects and the random slope for Ambiguity over subjects as the random effect structure.…”

“…The data analyzed in this study is a subset of the dataset analyzed in Nozari et al (2019). In Nozari et al (2019), our goal was to investigate the role of control processes on error detection.…”

“…The data analyzed in this study is a subset of the dataset analyzed in Nozari et al (2019). In Nozari et al (2019), our goal was to investigate the role of control processes on error detection. To this end, we explored the link between error rates and proportion of corrected errors, as a function of the position in the noun phrase (NP) and in the sentence (NP1 versus NP2).…”

“…The difficulty in noun production was manipulated by using four thematically related nouns per block which had to be repeatedly produced in NP1 and NP2 positions in each sentence. In another paper using the same paradigm and data (Nozari, Martin & McCloskey, 2019), we have established that this manipulation caused semantic interference (i.e., slower naming responses for pictures that are semantically related versus unrelated; Belke, Meyer & Damian, 2005; Nozari et al, 2016; Schnur, Schwartz, Brecher & Hodgson, 2006; Schnur, Schwartz, Kimberg, Hirshorn, Coslett & Thompson-Schill, 2009) at the sentence level. In fact, we showed that when producing sentences with nouns in NP1 and NP2 being thematically related, participants tend to perform worse on NP2.…”

Characterizing multi‐word speech production using event‐related potentials

“…As outlined in the introduction, multi‐word production requires efficient, fast, and adaptable monitoring processes as speakers are more likely to make errors when producing word sequences compared to an isolated word. The involvement of these highly efficient monitoring processes has been evidenced through the increased proportion of corrected errors as a function of error probability on error‐prone words throughout the sentence (Nozari et al., 2019). The ERN provides a useful electrophysiological index to study how monitoring processes are engaged throughout sentence production.…”