Individual differences in chunk boundary perception

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Abstract Summary

The chunkedness of language is assumed to be rooted in the properties of cognitive processing. Yet, we commonly investigate chunking using data aggregated from a population sample. This paper examines the extent to which individuals rely on different cues when processing chunks in natural speech. 

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AILA287
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When investigating factors at work in language processing and use, we commonly look at aggregated data, that is, data collected across an unbiased sample of individuals. This is reasonable since normally we are interested in what is shared across language speakers rather than their idiosyncrasies. However, modeling language as a complex adaptive system, a promising new approach to language (see Beckner et al. 2009), suggests that individual preferences might conspire to generate a very different pattern of behaviour at the aggregate level (Vetchinnikova 2017). For example, Roy et al. 2017 found that individuals can dramatically differ in the extent to which they rely on different cues in prosody perception. In principle, redundancy as one of the core properties of language should allow individuals to draw on different cues and/or draw on them to a different extent and still have a 'working' version of a language (e.g. Divjak & Arppe 2013). Importantly at the aggregate level such variation will not be visible.

            This study uses behavioral speech processing data collected in the CLUMP project (see Vetchinnikova, Mauranen & Mikušová 2017), where experiment participants were presented with short extracts of natural speech and asked to mark boundaries between chunks intuitively. Project results show that pause length, prosodic boundary strength and clausal syntactic structure independently contribute to predicting boundary perception at the aggregate level. This paper examines individual variation in chunk boundary perception using GLMM and random forests. 

References

Beckner, C., R. Blythe, J. Bybee, M. H. Christiansen, W. Croft, N. C. Ellis, J. Holland, J. Ke, D. Larsen-Freeman & T. Schoenemann. 2009. Language is a complex adaptive system: Position paper. Language learning 59(s1). 1–26.

Divjak, D. & A. Arppe. 2013. Extracting prototypes from exemplars What can corpus data tell us about concept representation? Cognitive Linguistics 24(2). 221–274. 

Roy J., J. Cole & T. Mahrt. 2017. Individual differences and patterns of convergence in prosody perception. Laboratory Phonology 8(1):22. 1-36. 

Vetchinnikova, S. 2017. On the relationship between the cognitive and the communal: A complex systems perspective. In M. Filppula, J. Klemola, A. Mauranen & S. Vetchinnikova (eds.). Changing English: Global and local perspectives, 277-310. Berlin: De Gruyter.

Vetchinnikova, S., A. Mauranen & N. Mikušová. 2017. ChunkitApp: Investigating the relevant units of online speech processing. In Proceedings of INTERSPEECH 2017, 811-812.

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Dr. Yo-An Lee
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