Our brains 'time-stamp' sounds to process the words we hear NatureComms
, in order to relate speech processing to the human cognition more broadly. Furthermore, our understanding would benefit from testing whether the target representations are lexical or morphological in nature, as well as orthogonalising lexical entropy and word-internal disambiguation points to more precisely quantify the temporal relationship with information maintenance.
Twenty-one native English participants were recruited from the NYU Abu Dhabi community . All provided their informed consent and were compensated for their time at a rate of 55 Dirhams per hour. Participants reported having normal hearing and no history of neurological disorders. Each participant participated in the experiment twice. Time between sessions ranged from 1 day to 2 months.
We used a temporal receptive field model to regress from the raw MEG data responses that were sensitive to fluctuations in the pitch and envelope of the acoustic speech signal. We used the ReceptiveField function from MNE-Python, using ridge regression as the estimator. We tested ten lambda regularisation parameters, log-spaced between 1–6 and 1 + 6, and picked the model with the highest predictive performance averaged across sensors.
We also tried using 50 linearly spaced frequency bands, and this did not change the interpretation of our acoustic analyses.We investigated whether single-trial sensor-level responses varied as a function of fourteen binary phonetic features, as derived from the multi-value feature system reported in.
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