Figure 4.
Auditory and linguistic neural representations in Dutch listeners when listening to an English story. Each swarm plot shows the change in predictive power for held-out EEG responses when removing a specific set of predictors (each dot represents the change in predictive power, averaged across sensors, for one participant). Predictive power is expressed in percent of the variance explained by the English model (Eq. 6) averaged across subjects. Stars indicate significance based on a one-tailed related measures t test. Topographic maps show corresponding sensor-specific data, with predictive power expressed as percent of model Equation 6 at the best sensor. The marked sensors form significant clusters in a cluster-based permutation test based on one-tailed t tests. A, Auditory predictors contribute a large proportion of the explained variance. The measure is based on the difference in predictive power between the English model Equation 6 and a model missing auditory predictors (acoustic onset and auditory spectrogram). B, All three linguistic models significantly contributed to the predictive power of the English model, in both stories. Note that predictive power can be negative, indicating that adding the given predictor made cross-validated predictions worse. C, A sublexical Dutch model, reflecting phoneme sequence statistics in Dutch (sublexicalD), significantly improved predictions even after controlling for English phoneme sequence statistics (sublexicalE), suggesting that Dutch listeners create expectations for phoneme sequences that would be appropriate in Dutch even when listening to English. The English sublexical model remained significant after adding the Dutch sublexical model. D, Addition of Dutch word forms suggests word recognition with competition from a combined lexicon: adding a word-form model using only Dutch pronunciations (word-formD) does not improve predictions (left column; comparison, model Eq. 8 > Eq. 7), suggesting that native language word recognition does not proceed in parallel. In contrast, replacing the English word-form model word-formE with a merged word-form model word-formED, which combines English and Dutch word forms, leads to improved predictions of EEG responses to Dutch-accented speech (right column; comparison, Eq. 9 > Eq. 7). *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001.