HL2C Seminar: Elma Kerz (Aachen), New insights into the role of statistical learning abilities in second language learning

Our next HL2C seminar will take place on Wednesday 15th December from 12 noon to 1pm GMT (Dublin, Edinburgh, Lisbon, London). This talk is a joint initiative with Lancaster’s SLLAT Research Group.

Presenters:

Elma Kerz (Aachen)

Title:

New insights into the role of statistical learning abilities in second language learning

How to join:

Our seminars are free to attend. Simply sign up to the HL2C Mailing List to receive the link to join us via Microsoft Teams link. You do not need a Teams account to access the talk.

Abstract:

One of the major advances in the language sciences across theoretical orientations has been in recognizing that natural languages consist of complex, variable patterns occurring in sequence, and as such can be described in terms of statistical regularities or distributional properties among language units (Christiansen & Chater, 2016; Gibson, 2019). Learning a language thus heavily depends on figuring out these complex structured patterns inherent in the input and there is a growing recognition that such accumulated statistical knowledge constitutes an essential part of our language knowledge (Rebuschat, 2013; Ellis, 2019). This is supported by extensive empirical evidence from the literature on statistical learning (henceforth SL). SL is succinctly defined as a powerful mechanism for perceiving and assimilating the range of regularities in the input, thereby shaping fundamental aspects of human cognition and behavior (Armstrong et al., 2017; Sherman et al., 2020).

A number of previous studies based on within-subject designs have examined the relationship between individual differences in SL ability and variations in language learning and processing, in both child and adult populations and in adult second-language learner populations. The main assumption underlying these studies is that individuals can be divided into ‘good’ and ‘bad’ statistical learners, with the expectation that ‘good’ statistical learners will show better performance across a wide range of language domains and population groups, such as early language acquisition (Lany et al., 2018), word predictability (Kaufman et al. 2010), reading (Arciuli, 2018), processing of complex syntactic structures in children and adults (Kidd & Arciuli, 2017; Misyak & Christiansen, 2012) and online processing of multiword combinations in second-language learners (Kerz & Wiechmann, 2019). However, this assumption has recently been challenged and there is now increasing recognition of the need to consider a broader ecological perspective on the diversity of statistics that must be accommodated and the challenges associated with the theoretical construct of good statistical learners (Bogaerts et al., 2021).

In this talk, I will present my recent studies aimed at addressing this ecological perspective and advancing our understanding of the role of SL in language learning and processing. I will show how this line of research can benefit from synthesizing experimental studies based on within-subject designs with natural language processing and computational techniques (see Rebuschat et al. (2017) for background reading).

1 References

1. Armstrong, B. C., Frost, R., Christiansen, M. H. (2017). The long road of statistical learning research: Past, present and future.. Philos Trans R Soc Lond B Biol Sci. 2017;372(1711):20160047.

2. Arciuli, J. (2018). Reading as statistical learning. Language, Speech, and Hearing Services in Schools, 49(3S), 634-643.

3. Bogaerts, L., Siegelman, N., Christiansen, M. H., & Frost, R. (2021). Is there such a thing as a ‘good statistical learner’?. Trends in Cognitive Sciences.

4. Christiansen, M. H., & Chater, N. (2016). Creating language: Integrating evolution, acquisition, and processing. MIT Press.

5. Ellis, N. C. (2019). Essentials of a theory of language cognition. The Modern Language Journal, 103, 39-60.

6. Gibson, E., Futrell, R., Piandadosi, S. T., Dautriche, I., Mahowald, K., Bergen, L., & Levy, R. (2019). How efficiency shapes human language. Trends in Cognitive Sciences. 5. 389-407.

7. Kaufman, S. B., DeYoung, C. G., Gray, J. R., Jim´enez, L., Brown, J., & Mackintosh, N. (2010). Implicit learning as an ability. Cognition, 116(3), 321-340.

8. Kerz, E., & Wiechmann, D. (2019). Effects of statistical learning ability on the second language processing of multiword sequences. In International Conference on Computational and CorpusBased Phraseology (pp. 200-214). Springer, Cham.

9. Kidd, E., & Arciuli, J. (2016). Individual differences in statistical learning predict children’s comprehension of syntax. Child Development, 87(1), 184-193.

10. Lany, J., Shoaib, A., Thompson, A., & Estes, K. G. (2018). Infant statistical-learning ability is related to real-time language processing. Journal of child language, 45(2), 368-391.

11. Misyak, J. B., & Christiansen, M. H. (2012). Statistical learning and language: An individual differences study. Language Learning, 62(1), 302-331.

12. Rebuschat P (2013) Statistical learning. In: Robinson P (ed.) The Routledge encyclopedia of second language acquisition. London: Routledge, pp. 612–15.

13. Rebuschat, P. E., Detmar, M., & McEnery, T. (2017). Language learning research at the intersection of experimental, computational and corpus-based approaches. Language Learning, 67(S1), 6-13.

14. Sherman, B. E., Graves, K. N., & Turk-Browne, N. B. (2020). The prevalence and importance of statistical learning in human cognition and behavior. Current opinion in behavioral sciences, 32, 15-20.