Abstract
While reading times are often used to measure
working memory load, frequency effects (such
as surprisal or n-gram frequencies) also have
strong confounding effects on reading times.
This work uses a naturalistic audio corpus
with magnetoencephalographic (MEG) annotations
to measure working memory load during
sentence processing. Alpha oscillations
in posterior regions of the brain have been
found to correlate with working memory load
in non-linguistic tasks (Jensen et al., 2002),
and the present study extends these findings
to working memory load caused by syntactic
center embeddings. Moreover, this work finds
that frequency effects in naturally-occurring
stimuli do not significantly contribute to neural
oscillations in any frequency band, which
suggests that many modeling claims could be
tested on this sort of data even without controlling
for frequency effects.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 6th Workshop on Cognitive Modelling and Computational Linguistics, CMCL 2015 |
| Publisher | The Association for Computational Linguistics |
| Pages | 79-88 |
| Number of pages | 10 |
| ISBN (Print) | 978-1-5108-0437-1 |
| Publication status | Published - Jun 2015 |
| Event | 2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015) - Denver, United States Duration: 31 May 2015 → 05 Jun 2015 |
Conference
| Conference | 2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015) |
|---|---|
| Country | United States |
| City | Denver |
| Period | 31/05/2015 → 05/06/2015 |
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Cite this
van Schijndel, M., Murphy, B., & Schuler, W. (2015). Evidence of syntactic working memory usage in MEG data. In Proceedings of the 6th Workshop on Cognitive Modelling and Computational Linguistics, CMCL 2015 (pp. 79-88). The Association for Computational Linguistics. https://www.aclweb.org/portal/content/2015-workshop-cognitive-modeling-and-computational-linguistics-cmcl