Publications

Abstract

Listening to speech is difficult in noisy environments, and is even harder when the interfering noise consists of intelligible speech as compared to unintelligible sounds. This suggests that the competing linguistic information interferes with the neural processing of target speech. Interference could either arise from a degradation of the neural representation of the target speech, or from increased representation of distracting speech that enters in competition with the target speech. We tested these alternative hypotheses using magnetoencephalography (MEG) while participants listened to a target clear speech in the presence of distracting noise-vocoded speech. Crucially, the distractors were initially unintelligible but became more intelligible after a short training session. Results showed that the comprehension of the target speech was poorer after training than before training. The neural tracking of target speech in the delta range (1–4 Hz) reduced in strength in the presence of a more intelligible distractor. In contrast, the neural tracking of distracting signals was not significantly modulated by intelligibility. These results suggest that the presence of distracting speech signals degrades the linguistic representation of target speech carried by delta oscillations.

Abstract

Humans differ greatly in their ability to use language. Contemporary psycholinguistic theories assume that individual differences in language skills arise from variability in linguistic experience and in general cognitive skills. While much previous research has tested the involvement of select verbal and non-verbal variables in select domains of linguistic processing, comprehensive characterizations of the relationships among the skills underlying language use are rare. We contribute to such a research program by re-analyzing a publicly available set of data from 112 young adults tested on 35 behavioral tests. The tests assessed nine key constructs reflecting linguistic processing skills, linguistic experience and general cognitive skills. Correlation and hierarchical clustering analyses of the test scores showed that most of the tests assumed to measure the same construct correlated moderately to strongly and largely clustered together. Furthermore, the results suggest important roles of processing speed in comprehension, and of linguistic experience in production.

Abstract

Background

While it is well established that second language (L2) learning success changes with age and across individuals, the underlying neural mechanisms responsible for this developmental shift and these individual differences are largely unknown. We will study the behavioral and neural factors that subserve new grammar and word learning in a large cross-sectional developmental sample. This study falls under the NWO (Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Dutch Research Council]) Language in Interaction consortium (website: https://www.languageininteraction.nl/).
Methods

We will sample 360 healthy individuals across a broad age range between 8 and 25 years. In this paper, we describe the study design and protocol, which involves multiple study visits covering a comprehensive behavioral battery and extensive magnetic resonance imaging (MRI) protocols. On the basis of these measures, we will create behavioral and neural fingerprints that capture age-based and individual variability in new language learning. The behavioral fingerprint will be based on first and second language proficiency, memory systems, and executive functioning. We will map the neural fingerprint for each participant using the following MRI modalities: T1‐weighted, diffusion-weighted, resting-state functional MRI, and multiple functional-MRI paradigms. With respect to the functional MRI measures, half of the sample will learn grammatical features and half will learn words of a new language. Combining all individual fingerprints allows us to explore the neural maturation effects on grammar and word learning.
Discussion

This will be one of the largest neuroimaging studies to date that investigates the developmental shift in L2 learning covering preadolescence to adulthood. Our comprehensive approach of combining behavioral and neuroimaging data will contribute to the understanding of the mechanisms influencing this developmental shift and individual differences in new language learning. We aim to answer: (I) do these fingerprints differ according to age and can these explain the age-related differences observed in new language learning? And (II) which aspects of the behavioral and neural fingerprints explain individual differences (across and within ages) in grammar and word learning? The results of this study provide a unique opportunity to understand how the development of brain structure and function influence new language learning success.

Abstract

The present study examined two acoustic cues in the production
of lexical stress in Dutch: spectral tilt and overall intensity.
Sluijter and Van Heuven (1996) reported that spectral tilt is a
more reliable cue to stress than intensity. However, that study
included only a small number of talkers (10) and only syllables
with the vowels /aː/ and /ɔ/.
The present study re-examined this issue in a larger and
more variable dataset. We recorded 38 native speakers of Dutch
(20 females) producing 744 tokens of Dutch segmentally
overlapping words (e.g., VOORnaam vs. voorNAAM, “first
name” vs. “respectable”), targeting 10 different vowels, in
variable sentence contexts. For each syllable, we measured
overall intensity and spectral tilt following Sluijter and Van
Heuven (1996).
Results from Linear Discriminant Analyses showed that,
for the vowel /aː/ alone, spectral tilt showed an advantage over
intensity, as evidenced by higher stressed/unstressed syllable
classification accuracy scores for spectral tilt. However, when
all vowels were included in the analysis, the advantage
disappeared.
These findings confirm that spectral tilt plays a larger role
in signaling stress in Dutch /aː/ but show that, for a larger
sample of Dutch vowels, overall intensity and spectral tilt are
equally important.

Abstract

Successful spoken-word recognition relies on an interplay between lexical and sublexical processing. Previous research demonstrated that listeners readily shift between more lexically-biased and more sublexically-biased modes of processing in response to the situational context in which language comprehension takes place. Recognizing words in the presence of background noise reduces the perceptual evidence for the speech signal and – compared to the clear – results in greater uncertainty. It has been proposed that, when dealing with greater uncertainty, listeners rely more strongly on sublexical processing. The present study tested this proposal using behavioral and electroencephalography (EEG) measures. We reasoned that such an adjustment would be reflected in changes in the effects of variables predicting recognition performance with loci at lexical and sublexical levels, respectively. We presented native speakers of Dutch with words featuring substantial variability in (1) word frequency (locus at lexical level), (2) phonological neighborhood density (loci at lexical and sublexical levels) and (3) phonotactic probability (locus at sublexical level). Each participant heard each word in noise (presented at one of three signal-to-noise ratios) and in the clear and performed a two-stage lexical decision and transcription task while EEG was recorded. Using linear mixed-effects analyses, we observed behavioral evidence that listeners relied more strongly on sublexical processing when speech quality decreased. Mixed-effects modelling of the EEG signal in the clear condition showed that sublexical effects were reflected in early modulations of ERP components (e.g., within the first 300 ms post word onset). In noise, EEG effects occurred later and involved multiple regions activated in parallel. Taken together, we found evidence – especially in the behavioral data – supporting previous accounts that the presence of background noise induces a stronger reliance on sublexical processing.

Abstract

The social significance of recognizing the person who talks to us is obvious, but the neural mechanisms that mediate talker identification are unclear. Regions along the bilateral superior temporal sulcus (STS) and the inferior frontal cortex (IFC) of the human brain are selective for voices, and they are sensitive to rapid voice changes. Although it has been proposed that voice recognition is supported by prototype-centered voice representations, the involvement of these category-selective cortical regions in the neural coding of such "mean voices" has not previously been demonstrated. Using fMRI in combination with a voice identity learning paradigm, we show that voice-selective regions are involved in the mean-based coding of voice identities. Voice typicality is encoded on a supra-individual level in the right STS along a stimulus-dependent, identity-independent (i.e., voice-acoustic) dimension, and on an intra-individual level in the right IFC along a stimulus-independent, identity-dependent (i.e., voice identity) dimension. Voice recognition therefore entails at least two anatomically separable stages, each characterized by neural mechanisms that reference the central tendencies of voice categories.

Abstract

Are there bi-directional influences between speech perception and music perception? An answer to this question is essential for understanding the extent to which the speech and music that we hear are processed by domain-general auditory processes and/or by distinct neural auditory mechanisms. This review summarizes a large body of behavioral and neuroscientific findings which suggest that the musical experience of trained musicians does modulate speech processing, and a sparser set of data, largely on pitch processing, which suggest in addition that linguistic experience, in particular learning a tone language, modulates music processing. Although research has focused mostly on music on speech effects, we argue that both directions of influence need to be studied, and conclude that the picture which thus emerges is one of mutual interaction across domains. In particular, it is not simply that experience with spoken language has some effects on music perception, and vice versa, but that because of shared domain-general subcortical and cortical networks, experiences in both domains influence behavior in both domains.

Abstract

Multivariate pattern classification methods are increasingly applied to neuroimaging data in the context of both fundamental research and in brain-computer interfacing approaches. Such methods provide a framework for interpreting measurements made at the single-trial level with respect to a set of two or more distinct mental states. Here, we define an approach in which the output of a binary classifier trained on data from an auditory mismatch paradigm can be used for online tracking of perception and as a neurofeedback signal. The auditory mismatch paradigm is known to induce distinct perceptual states related to the presentation of high- and low-probability stimuli, which are reflected in event-related potential (ERP) components such as the mismatch negativity (MMN). The first part of this paper illustrates how pattern classification methods can be applied to data collected in an MMN paradigm, including discussion of the optimization of preprocessing steps, the interpretation of features and how the performance of these methods generalizes across individual participants and measurement sessions. We then go on to show that the output of these decoding methods can be used in online settings as a continuous index of single-trial brain activation underlying perceptual discrimination. We conclude by discussing several potential domains of application, including neurofeedback, cognitive monitoring and passive brain-computer interfaces

Abstract

Brain-computer interfaces (BCIs) are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification methods were applied to single-trial EEG data collected during speech perception by native and non-native speakers. Two principal questions were asked: 1) Can differences in the perceived categories of pairs of phonemes be decoded at the single-trial level? 2) Can these same categorical differences be decoded across participants, within or between native-language groups? Results indicated that classification performance progressively increased with respect to the categorical status (within, boundary or across) of the stimulus contrast, and was also influenced by the native language of individual participants. Classifier performance showed strong relationships with traditional event-related potential measures and behavioral responses. The results of the cross-participant analysis indicated an overall increase in average classifier performance when trained on data from all participants (native and non-native). A second cross-participant classifier trained only on data from native speakers led to an overall improvement in performance for native speakers, but a reduction in performance for non-native speakers. We also found that the native language of a given participant could be decoded on the basis of EEG data with accuracy above 80%. These results indicate that electrophysiological responses underlying speech perception can be decoded at the single-trial level, and that decoding performance systematically reflects graded changes in the responses related to the phonological status of the stimuli. This approach could be used in extensions of the BCI paradigm to support perceptual learning during second language acquisition

Abstract

What is the relative salience of different aspects of word meaning in the developing lexicon? The current study examines the time-course of retrieval of semantic and color knowledge associated with words during toddler word recognition: at what point do toddlers orient towards an image of a yellow cup upon hearing color-matching words such as “banana” (typically yellow) relative to unrelated words (e.g., “house”)? Do children orient faster to semantic matching images relative to color matching images, e.g., orient faster to an image of a cookie relative to a yellow cup upon hearing the word “banana”? The results strongly suggest a prioritization of semantic information over color information in children’s word-referent mappings. This indicates that, even for natural objects (e.g., food, animals that are more likely to have a prototypical color), semantic knowledge is a more salient aspect of toddler's word meaning than color knowledge. For 24-month-old Dutch toddlers, bananas are thus more edible than they are yellow.

Abstract

Recent evidence shows that listeners use abstract prelexical units in speech perception. Using the phenomenon of lexical retuning in speech processing, we ask whether those units are necessarily phonemic. Dutch listeners were exposed to a Dutch speaker producing ambiguous phones between the Dutch syllable-final allophones approximant [r] and dark [l]. These ambiguous phones replaced either final /r/ or final /l/ in words in a lexical-decision task. This differential exposure affected perception of ambiguous stimuli on the same allophone continuum in a subsequent phonetic-categorization test: Listeners exposed to ambiguous phones in /r/-final words were more likely to perceive test stimuli as /r/ than listeners with exposure in /l/-final words. This effect was not found for test stimuli on continua using other allophones of /r/ and /l/. These results confirm that listeners use phonological abstraction in speech perception. They also show that context-sensitive allophones can play a role in this process, and hence that context-insensitive phonemes are not necessary. We suggest there may be no one unit of perception

Abstract

This study reports effects of a high-variability training procedure on nonnative learning of a Japanese geminate-singleton fricative contrast. Thirty native speakers of Dutch took part in a 5-day training procedure in which they identified geminate and singleton variants of the Japanese fricative /s/. Participants were trained with either many repetitions of a limited set of words recorded by a single speaker (low-variability training) or with fewer repetitions of a more variable set of words recorded by multiple speakers (high-variability training). Both types of training enhanced identification of speech but not of nonspeech materials, indicating that learning was domain specific. High-variability training led to superior performance in identification but not in discrimination tests, and supported better generalization of learning as shown by transfer from the trained fricatives to the identification of untrained stops and affricates. Variability thus helps nonnative listeners to form abstract categories rather than to enhance early acoustic analysis.

Abstract

Three experiments investigated whether extrinsic vowel normalization takes place largely at a categorical or a precategorical level of processing. Traditional vowel normalization effects in categorization were replicated in Experiment 1: Vowels taken from an [ɪ]-[ε] continuum were more often interpreted as /ɪ/ (which has a low first formant, F (1)) when the vowels were heard in contexts that had a raised F (1) than when the contexts had a lowered F (1). This was established with contexts that consisted of only two syllables. These short contexts were necessary for Experiment 2, a discrimination task that encouraged listeners to focus on the perceptual properties of vowels at a precategorical level. Vowel normalization was again found: Ambiguous vowels were more easily discriminated from an endpoint [ε] than from an endpoint [ɪ] in a high-F (1) context, whereas the opposite was true in a low-F (1) context. Experiment 3 measured discriminability between pairs of steps along the [ɪ]-[ε] continuum. Contextual influences were again found, but without discrimination peaks, contrary to what was predicted from the same participants' categorization behavior. Extrinsic vowel normalization therefore appears to be a process that takes place at least in part at a precategorical processing level.

Abstract

We investigated how the strength of a foreign accent and varying types of experience with foreign-accented speech influence the recognition of accented words. In Experiment 1, native Dutch listeners with limited or extensive prior experience with German-accented Dutch completed a cross-modal priming experiment with strongly, medium, and weakly accented words. Participants with limited experience were primed by the medium and weakly accented words, but not by the strongly accented words. Participants with extensive experience were primed by all accent types. In Experiments 2 and 3, Dutch listeners with limited experience listened to a short story before doing the cross-modal priming task. In Experiment 2, the story was spoken by the priming task speaker and either contained strongly accented words or did not. Strongly accented exposure led to immediate priming by novel strongly accented words, while exposure to the speaker without strongly accented tokens led to priming only in the experiment’s second half. In Experiment 3, listeners listened to the story with strongly accented words spoken by a different German-accented speaker. Listeners were primed by the strongly accented words, but again only in the experiment’s second half. Together, these results show that adaptation to foreign-accented speech is rapid but depends on accent strength and on listener familiarity with those strongly accented words.

Abstract

Simulations explored the inability of the TRACE model of spoken-word recognition to model the effects on human listening of acoustic-phonetic mismatches in word forms. The source of TRACE's failure lay not in its interactive connectivity, not in the presence of interword competition, and not in the use of phonemic representations, but in the need for continuously optimised interpretation of the input. When an analogue of TRACE was allowed to cycle to asymptote on every slice of input, an acceptable simulation of the subcategorical mismatch data was achieved. Even then, however, the simulation was not as close as that produced by the Merge model.

Abstract

Lexical knowledge influences how human listeners make decisions about speech sounds. Positive lexical effects (faster responses to target sounds in words than in nonwords) are robust across several laboratory tasks, while negative effects (slower responses to targets in more word-like nonwords than in less word-like nonwords) have been found in phonetic decision tasks but not phoneme monitoring tasks. The present experiments tested whether negative lexical effects are therefore a task-specific consequence of the forced choice required in phonetic decision. We compared phoneme monitoring and phonetic decision performance using the same Dutch materials in each task. In both experiments there were positive lexical effects, but no negative lexical effects. We observe that in all studies showing negative lexical effects, the materials were made by cross-splicing, which meant that they contained perceptual evidence supporting the lexically-consistent phonemes. Lexical knowledge seems to influence phonemic decision-making only when there is evidence for the lexically-consistent phoneme in the speech signal.

Abstract

We briefly describe the Merge model of phonemic decision-making, and, in the light of general arguments about the possible role of feedback in spoken-word recognition, defend Merge's feedforward structure. Merge not only accounts adequately for the data, without invoking feedback connections, but does so in a parsimonious manner.

Abstract

The central thesis of the target article was that feedback is never necessary in spoken word recognition. The commentaries present no new data and no new theoretical arguments which lead us to revise this position. In this response we begin by clarifying some terminological issues which have lead to a number of significant misunderstandings. We provide some new arguments to support our case that the feedforward model Merge is indeed more parsimonious than the interactive alternatives, and that it provides a more convincing account of the data than alternative models. Finally, we extend the arguments to deal with new issues raised by the commentators such as infant speech perception and neural architecture.

Abstract

Top-down feedback does not benefit speech recognition; on the contrary, it can hinder it. No experimental data imply that feedback loops are required for speech recognition. Feedback is accordingly unnecessary and spoken word recognition is modular. To defend this thesis, we analyse lexical involvement in phonemic decision making. TRACE (McClelland & Elman 1986), a model with feedback from the lexicon to prelexical processes, is unable to account for all the available data on phonemic decision making. The modular Race model (Cutler & Norris 1979) is likewise challenged by some recent results, however. We therefore present a new modular model of phonemic decision making, the Merge model. In Merge, information flows from prelexical processes to the lexicon without feedback. Because phonemic decisions are based on the merging of prelexical and lexical information, Merge correctly predicts lexical involvement in phonemic decisions in both words and nonwords. Computer simulations show how Merge is able to account for the data through a process of competition between lexical hypotheses. We discuss the issue of feedback in other areas of language processing and conclude that modular models are particularly well suited to the problems and constraints of speech recognition.

Abstract

Two word-spotting experiments are reported that examine whether the Possible-Word Constraint (PWC) [1] is a language-specific or language-universal strategy for the segmentation of continuous speech. The PWC disfavours parses which leave an impossible residue between the end of a candidate word and a known boundary. The experiments examined cases where the residue was either a CV syllable with a lax vowel, or a CVC syllable with a schwa. Although neither syllable context is a possible word in English, word-spotting in both contexts was easier than with a context consisting of a single consonant. The PWC appears to be language-universal rather than language-specific.

Abstract

Simulations explored the inability of the TRACE model of spoken-word recognition to model the effects on human listening of subcategorical mismatch in word forms. The source of TRACE's failure lay not in interactive connectivity, not in the presence of inter-word competition, and not in the use of phonemic representations, but in the need for continuously optimised interpretation of the input. When an analogue of TRACE was allowed to cycle to asymptote on every slice of input, an acceptable simulation of the subcategorical mismatch data was achieved. Even then, however, the simulation was not as close as that produced by the Merge model, which has inter-word competition, phonemic representations and continuous optimisation (but no interactive connectivity).