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EASTBIO: Neural mechanisms of predictive processing in language


   School of Psychology

  , Dr Mingjun Zhong  Thursday, December 16, 2021  Competition Funded PhD Project (Students Worldwide)

Aberdeen United Kingdom Bioinformatics Biomedical Engineering Neuroscience Psychology

About the Project

Supervisors:

Dr Joost Rommers - Aberdeen, School of Psychology -

Dr Mingjun Zhong - Aberdeen, Department of Computing Science -

This project uses advanced analyses of electrical brain activity (EEG) to investigate the neural mechanisms of predictive language processing. The brain is sometimes considered to be a ‘prediction machine’ that continuously compares the input against internally generated predictions. Prediction is thought to underpin rapid processing of predictable input on the one hand and learning from unexpected input on the other hand.

Human language, the most advanced communication system that evolution has produced, unfolds on a particularly rapid time scale (around 250 written words or 200 spoken syllables per minute). A wealth of evidence suggests that readers and listeners predict upcoming information, which likely helps them deal with language at the speed at which it arrives. However, it remains unclear exactly how predictions modulate the way words are processed. Are predictable words processed thoroughly, because predicted and actual input converge to yield a strong representation? Or are predictable words instead processed in a shallow fashion, as the brain merely verifies that the prediction was confirmed? And when a prediction gets disconfirmed, what happens to the original prediction: does it linger, or is it suppressed?

To address such questions, this project follows up on recent EEG work that systematically varied word predictability (Rommers & Federmeier, 2018a, 2018b). The project then combines this with machine learning techniques (e.g., Zhong et al., 2008) that use the scalp distribution of EEG signals to decode word-related information present over time and at different frequencies. The results will help advance our understanding of the fundamental mechanisms of predictive processing.

The PhD candidate will apply methods from cognitive neuroscience and artificial intelligence to temporal and spectral aspects of electrophysiological signals. This will involve developing a high level of expertise in digital signal processing and machine learning methodologies to unravel the mechanisms of predictive processing in language. The project is suitable for candidates with a background in psychology, cognitive neuroscience, linguistics, artificial intelligence or computer science with strong interests in language processing and electrophysiology.

It may be possible to undertake this project part-time, in discussion with the lead supervisor, however, please note that part-time study is unavailable to students who require a Student Visa to study within the UK.

Application Procedure:

Please visit this page for full application information: http://www.eastscotbiodtp.ac.uk/how-apply-0

Please send your completed EASTBIO application form, along with academic transcripts to Alison Innes at

Two references should be provided by the deadline using the EASTBIO reference form.

Please advise your referees to return the reference form to

Unfortunately, due to workload constraints, we cannot consider incomplete applications


Funding Notes

This 4 year PhD project is part of a competition funded by EASTBIO BBSRC Doctoral Training Partnership.
This opportunity is open to UK and International students and provides funding to cover stipend and UK level tuition (limited funding is available to provide international tuition fees). Please refer to UKRI website and Annex B of the UKRI Training Grant Terms and Conditions for full eligibility criteria.
Candidates should have (or expect to achieve) a minimum of a 2:1 UK Honours degree, or the equivalent qualifications gained outside the UK, in a relevant subject.

References

Rommers, J., & Federmeier, K. D. (2018a). Lingering expectations: A pseudo-repetition effect for words previously expected but not presented. NeuroImage, 183, 263-272.
Rommers, J., & Federmeier, K. D. (2018b). Predictability’s aftermath: Downstream consequences of word predictability as revealed by repetition effects. Cortex, 101, 16-30.
Zhong, M., Lotte, F., Girolami, M., & Lécuyer, A. (2008). Classifying EEG for brain computer interfaces using Gaussian processes. Pattern Recognition Letters, 29(3), 354-359.

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