Asymmetries in Accessing Vowel Representations Are Driven by Phonological and Acoustic Properties: Neural and Behavioral Evidence From Natural German Minimal Pairs

Abstract

In vowel discrimination, commonly found discrimination patterns are directional asymmetries where discrimination is faster (or easier) if differing vowels are presented in a certain sequence compared to the reversed sequence. Different models of speech sound processing try to account for these asymmetries based on either phonetic or phonological properties. In this study, we tested and compared two of those often-discussed models, namely the Featurally Underspecified Lexicon (FUL) model (Lahiri and Reetz, 2002) and the Natural Referent Vowel (NRV) framework (Polka and Bohn, 2011). While most studies presented isolated vowels, we investigated a large stimulus set of German vowels in a more naturalistic setting within minimal pairs. We conducted an mismatch negativity (MMN) study in a passive and a reaction time study in an active oddball paradigm. In both data sets, we found directional asymmetries that can be explained by either phonological or phonetic theories. While behaviorally, the vowel discrimination was based on phonological properties, both tested models failed to explain the found neural patterns comprehensively. Therefore, we additionally examined the influence of a variety of articulatory, acoustical, and lexical factors (e.g., formant structure, intensity, duration, and frequency of occurrence) but also the influence of factors beyond the well-known (perceived loudness of vowels, degree of openness) in depth via multiple regression analyses. The analyses revealed that the perceptual factor of perceived loudness has a greater impact than considered in the literature and should be taken stronger into consideration when analyzing preattentive natural vowel processing.

Keywords: mismatch negativity (MMN); multiple regression analysis; perceived loudness; reaction time (RT); vowel discrimination.

Figure 1

Acoustic characteristic of the stimuli. Mean values of the first (F1) and the second (F2) formant are given per word category in Hertz.

Figure 2

Identity mismatch negativity (MMN) effects per condition. MMN waveforms for all word pairs, in both presentation orders, are shown.

Figure 3

Clusterstatistics. In an electrode × time cluster, deviants elicited more negative responses than standards in the time window between 130 and 200 ms post vowel onset.

Figure 4

Reaction time results per condition. Reaction time results are given as log values per presentation direction of words with whiskers indicating the variance of the data and small dots representing outliers (but not extreme values) which were beneath the ±2 SD cut-off.

Figure 5

Results of the perceived loudness rating. The results are plotted for each presentation direction (x-axis) in relation to the frequency of the given responses (y-axis).

Figure 6

Scatterplot for the regression analysis of the given iMMN data from Experiment 1. MMN difference values of each participant (y-axis) per vowel contrast (x-axis) in relation to implicit loudness. Increasing loudness (deviant louder than standard) is shown as a blue triangle, decreasing loudness (standard louder than deviant) as a red triangle, and equal perceived loudness as a green dot. MMN difference values are scaled with perceiving the stimuli as equally loud (most clearly seen in vowel contrasts StiegSteg and StielStuhl).

Figure 7

Scatterplot for the regression of the obtained reaction time (RT) data from Experiment 2. Mean log RTs of each subject (y-axis) are depicted per vowel contrast (x-axis) in relation to implicit loudness. RT results (log-values) are not scaled by the perceived loudness of the stimuli.