PreSpeech · Predicting speech: what and when does the brain predict during language comprehension?
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2018-09-10 → 2020-12-30
- EU contribution
- €170,122
- Participants
- 1
- Scheme
- MSCA-IF-EF-ST
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Results in brief
Predicting speech: what and when does the brain predict during language comprehension?
We rely on spoken communication every day. Our speed and accuracy of language comprehension is remarkable. For an individual the breakdown or impediment of either of these two components results in life-changing disabilities. Understanding cognitive mechanisms of speech and accuracy in word recognition and how they are enhanced by context is key to improving language disorder diagnoses and treatment techniques. Despite this there is little understanding of how such fluency is achieved. New cognitive neuroscience theories propose that the human brain achieves speech processing by using context and world knowledge to allow cortical circuits to probabilistically predict and pre-activate upcoming sounds, words and even larger phrases. Many questions regarding this promising approach, however, remain unclear. The most important are: (a) what are the neurobiological mechanisms of predictive processing of speech? and (b) what is their impact on natural speech comprehension in populations with speech, language or reading disorders? The main scientific objective of PreSpeech was to address these questions in the context of spoken sentence processing in typical and dyslexic readers. The reason to focus on the dyslexic population is the potential of predictive processing to be a compensation strategy for phonological deficits in dyslexia. Dyslexic readers, compared to normal readers, have impaired cortical entrainment to low frequency auditory speech features (words’ envelopes) and this may be the critical element in their phonological deficit. Using context to generate low-level word-form and higher-level semantic predictions about upcoming words can reduce the burden on the bottom-up analysis of the input and reliance on the entrainment to the prosodic speech contours. This can constitute a compensation strategy for aspects of speech processing in dyslexia. To achieve our goals we assembled a state-of-the-art analysis pipeline that included oscillatory and multivariate techniques applied to time-resolved measures of brain activity. Below we outline the main to-date findings of this project.
Data: CORDIS, © European Union
Project objective
The ability to recognise spoken words relies on extracting phonological features from the acoustic input that distinguish a word from its cohort competitors. Neuronal circuits were suggested to use context to predict and pre-activate specific speech features. Bottom-up feature recognition is enabled by phase and amplitude coupling between cortical oscillations and acoustic signals and this process is facilitated by top-down predictive processing. Predictions may be critical for the speed, accuracy and noise resistance that distinguish fluent speech recognition and were related to specific cortical oscillatory patterns (theta/delta synchronisation, beta increase). Despite evidence, the nature and the timing of these predictions remains unclear. This project will address these key questions using MEG and EEG that provide good spatiotemporal resolution. Increasing predictability of a word should have two effects: A) Neuronal populations encoding its phonological form will become active before this word is uniquely identifiable from its potential competitors by bottom-up analysis (uniqueness point UP). Using spatiotemporal multivariate pattern analysis we will test if the latency of phonological feature detection, used for word identification, is modulated by word's contextual predictability. If specific predictions are made, divergence should occur before the UP and should be supported by changes in oscillatory activity; B) Generating predictions should decrease bottom-up feature processing demands. We expect predictability to reduce the phase-amplitude coupling between the speech envelope and the gamma oscillations (a neuronal measure of phonological processing). In summary we aim to identify whether and how context enables predictions of incoming words’ form before it can be acoustically established. This will be critical for understanding the cortical architecture of speech processing with practical applications in artificial speech recognition.
Original text from CORDIS.
Participants
- BCBL BASQUE CENTER ON COGNITION BRAIN AND LANGUAGE · San SebastianCoordinatorSpain
Links
Data: CORDIS, © European Union