The role of phonology in bilingual word recognition has focused on a phonemic level especially in the recognition of cognates. In this study, we examined differences in metrical structure to test whether first language (L1) metrical structure influences the processing of second language (L2) words. For that, we used words of Romance origin (e.g., reptile , signal ), which both German and English have borrowed extensively. However, the existing metrical patterns are not identical nor are the borrowed vocabularies the same. Rather, those identical words differ systematically in their foot structure. We conducted a cross-modal form fragment priming EEG experiment (auditory–visual) with German native speakers who were highly proficient in English. Both behavioral and ERP results showed an effect of the native phonology and the loan status, that is, whether the loan exists only in the speaker's L2 or is shared across languages. Priming effects (RTs) were largest for nonshared loanwords indicating some interference from German (L1). This was also evident in a reduced N400 but only if the metrical structure aligned with German patterns for Germanic words, that is, two light syllables as in pigeon . If the words exist in both languages, metrical structure also mattered shown by the modulation of different ERP components across conditions. Overall, our study indicates that metrical phonology plays a role in loanword processing. Our data show that the more similar a word is in terms of its metrical phonology across L1 and L2, the more effortful the processing of a word within a priming paradigm indicating interference from the L1 phonology.

A central issue in speech recognition is how contrastive phonemic information is stored in the mental lexicon. The conventional view assumes that this information is closely related to acoustic properties of speech. Considering that no word is ever pronounced alike twice and that the brain has limited capacities to manage information, an opposing view proposes abstract underspecified representations where not all phonemic features are stored. We examined this proposal using event-related brain potentials, in particular mismatch negativity (MMN), an automatic change detection response in the brain that is sensitive to language-specific phoneme representations. In the current study, vowel pairs were presented to subjects, reversed as standard and deviant. Models not assuming underspecification predict equal MMNs for vowel pairs regardless of the reversal. In contrast, enhanced and earlier MMNs were observed for those conditions where the standard is not phonologically underspecified in the mental representation. This provides the first neurobiological evidence for a featurally underspecified mental lexicon.

This study further elucidates determinants of vowel perception in the human auditory cortex. The vowel inventory of a given language can be classified on the basis of phonological features which are closely linked to acoustic properties. A cortical representation of speech sounds based on these phonological features might explain the surprisingly inverse correlation between immense variance in the acoustic signal and high accuracy of speech recognition. We investigated timing and mapping of the N100m elicited by 42 tokens of seven natural German vowels varying along the phonological features tongue height (corresponding to the frequency of the first formant) and place of articulation (corresponding to the frequency of the second and third formants). Auditoryevoked fields were recorded using a 148-channel whole-head magnetometer while subjects performed target vowel detection tasks. Source location differences appeared to be driven by place of articulation: Vowels with mutually exclusive place of articulation features, namely, coronal and dorsal elicited separate centers of activation along the posterior-anterior axis. Additionally, the time course of activation as reflected in the N100m peak latency distinguished between vowel categories especially when the spatial distinctiveness of cortical activation was low. In sum, results suggest that both N100m latency and source location as well as their interaction reflect properties of speech stimuli that correspond to abstract phonological features.