Second language oral fluency research has suggested that collocations in speech (e.g., kill time) are linked to efficient speech production, that is, utterance fluency (UF)(Tavakoli & Uchihara, 2019). Collocations appear to be processed as wholes, taking advantage of linguistic encoding in a speech production (Kormos, 2006). Since Segalowitz's (2010) framework of the subcomponents of oral fluency, scholars in this domain have sought the interrelationship between them. Recent studies highlight what underlying linguistic knowledge and processing (cognitive fluency: CF) lead to UF (e.g., Suzuki & Kormos, 2022), and found that vocabulary knowledge and processing speed greatly contribute to speed and breakdown fluency. What is missing, however, is the inclusion of collocational processing into CF measures. The only study examining the link between collocational processing and UF is Koizumi and In'nami (2013) but they did not examine breakdown fluency.
To examine the connection between collocational processing speed and UF, Japanese undergraduate students (N = 105) performed a phrasal acceptability judgement task where reaction times in judgement of collocations using binary responses were measured. The collocation items consisted of 42 critical stimuli with the three frequency bins (high, mid, and low) and 42 filler stimuli. The adjective + noun collocations were pooled from Ackermann and Chen's (2013) Academic Collocation List. The mutual information score, length, congruency, adjective and noun frequencies of items in the three frequency bins were all closely matched. Speech samples were elicited by an argumentative speech task, and UF measures were computed based on Tavakoli and Skehan's (2005) framework of speed (articulation rate), breakdown (mid/end-clause silent pause ratio/duration; filled pause ratio), and repair fluency (disfluency ratio). Correlation analyses were performed to examine the relationship between collocational processing speed (reaction times) and UF measures.
The result showed that reaction times in high-frequency collocations had the largest coefficient among the three frequency levels for articulation rate (rho = -.287), mid-clause silent pause ratio (rho = .345), and end-clause silent pause ratio (rho = .334). The result indicated a potential contribution of collocational processing speed to efficient speech production.
Ackermann, K., & Chen, Y. H. (2013). Developing the Academic Collocation List (ACL) - A corpus-driven and expert-judged approach. Journal of English for Academic Purposes, 12(4), 235–247. https://doi.org/10.1016/j.jeap.2013.08.002
Koizumi, R., & In'nami, Y. (2013). Vocabulary knowledge and speaking proficiency among second language learners from novice to intermediate levels. Journal of Language Teaching and Research, 4(5), 900–913. https://doi.org/10.4304/jltr.4.5.900-913
Kormos, J. (2006). Speech production and second language acquisition. Lawrence Erlbaum.
Segalowitz, N. (2010). Cognitive bases of second language fluency. Routledge.
Suzuki, S., & Kormos, J. (2022). The multidimensionality of second language oral fluency: Interfacing cognitive fluency and utterance fluency. Studies in Second Language Acquisition, 1–27. https://doi.org/10.1017/S0272263121000899
Tavakoli, P., & Skehan, P. (2005). Strategic planning, task structure and performance testing. In R. Ellis (Ed.), Planning and task performance in a second language (pp. 239–277). John Benjamins.
Tavakoli, P., & Uchihara, T. (2019). To what extent are multiword sequences associated with oral fluency? Language Learning, 70(2), 506–547. https://doi.org/10.1111/lang.12384
