Publications

Abstract

Individual differences in using language are prevalent in our daily lives. Language skills are often assessed in vocational (predominantly written language) and diagnostic contexts. Not much is known, however, about individual differences in spoken language skills. The lack of research is in part due to the lack of suitable test instruments. We introduce the Individual Differences in Language Skills (IDLaS-NL) test battery, a set of 31 behavioural tests that can be used to capture variability in language and relevant general cognitive skills in adult speakers of Dutch. The battery was designed to measure word and sentence production and comprehension skills, linguistic knowledge, nonverbal processing speed, working memory, and nonverbal reasoning. The present article outlines the structure of the battery, describes the materials and procedure of each test, and evaluates the battery’s factor structure based on the results of a sample of 748 Dutch adults, aged between 18 and 30 years, most of them students. The analyses demonstrate that the battery has good construct validity and can be reliably administered both in the lab and via the internet. We therefore recommend the battery as a valuable new tool to assess individual differences in language knowledge and skills; this future work may include linking language skills to other aspects of human cognition and life outcomes.

Abstract

In reply to Magnuson, Crinnion, Luthra, Gaston, and Grubb (2023), we challenge their conclusion that on-line activation feedback improves word recognition. This type of feedback is instantiated in the TRACE model (McClelland & Elman, 1986) as top-down spread of activation from lexical to phoneme nodes. We give two main reasons why Magnuson et al.'s demonstration that activation feedback speeds up word recognition in TRACE is not informative about whether activation feedback helps humans recognize words. First, the same speed-up could be achieved by changing other parameters in TRACE. Second, more fundamentally, there is room for improvement in TRACE's performance only because the model, unlike Bayesian models, is suboptimal. We also challenge Magnuson et al.'s claim that the available empirical data support activation feedback. The data they base this claim on are open to alternative explanations and there are data against activation feedback that they do not discuss. We argue, therefore, that there are no computational or empirical grounds to conclude that activation feedback benefits human spoken-word recognition and indeed no theoretical grounds why activation feedback would exist. Other types of feedback, for example feedback to support perceptual learning, likely do exist, precisely because they can help listeners recognize words.

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

Prosodic influences on phonetic realizations of four Dutch consonants (/t d s z/) were examined. Sentences were constructed containing these consonants in word-initial position; the factors lexical stress, phrasal accent and prosodic boundary were manipulated between sentences. Eleven Dutch speakers read these sentences aloud. The patterns found in acoustic measurements of these utterances (e.g., voice onset time (VOT), consonant duration, voicing during closure, spectral center of gravity, burst energy) indicate that the low-level phonetic implementation of all four consonants is modulated by prosodic structure. Boundary effects on domain-initial segments were observed in stressed and unstressed syllables, extending previous findings which have been on stressed syllables alone. Three aspects of the data are highlighted. First, shorter VOTs were found for /t/ in prosodically stronger locations (stressed, accented and domain-initial), as opposed to longer VOTs in these positions in English. This suggests that prosodically driven phonetic realization is bounded by language-specific constraints on how phonetic features are specified with phonetic content: Shortened VOT in Dutch reflects enhancement of the phonetic feature {−spread glottis}, while lengthened VOT in English reflects enhancement of {+spread glottis}. Prosodic strengthening therefore appears to operate primarily at the phonetic level, such that prosodically driven enhancement of phonological contrast is determined by phonetic implementation of these (language-specific) phonetic features. Second, an accent effect was observed in stressed and unstressed syllables, and was independent of prosodic boundary size. The domain of accentuation in Dutch is thus larger than the foot. Third, within a prosodic category consisting of those utterances with a boundary tone but no pause, tokens with syntactically defined Phonological Phrase boundaries could be differentiated from the other tokens. This syntactic influence on prosodic phrasing implies the existence of an intermediate-level phrase in the prosodic hierarchy of Dutch.

Abstract

We conducted four experiments to investigate the specificity of perceptual adjustments made to unusual speech sounds. Dutch listeners heard a female talker produce an ambiguous fricative [?] (between [f] and [s]) in [f]- or [s]-biased lexical contexts. Listeners with [f]-biased exposure (e.g., [witlo?]; from witlof, “chicory”; witlos is meaningless) subsequently categorized more sounds on an [εf]–[εs] continuum as [f] than did listeners with [s]-biased exposure. This occurred when the continuum was based on the exposure talker's speech (Experiment 1), and when the same test fricatives appeared after vowels spoken by novel female and male talkers (Experiments 1 and 2). When the continuum was made entirely from a novel talker's speech, there was no exposure effect (Experiment 3) unless fricatives from that talker had been spliced into the exposure talker's speech during exposure (Experiment 4). We conclude that perceptual learning about idiosyncratic speech is applied at a segmental level and is, under these exposure conditions, talker specific.

Abstract

In a phonological priming experiment using spoken Dutch words, Dutch listeners were taught varying expectancies and relatedness relations about the phonological form of target words, given particular primes. They learned to expect that, after a particular prime, if the target was a word, it would be from a specific phonological category. The expectancy either involved phonological overlap (e.g., honk-vonk, “base-spark”; expected related) or did not (e.g., nest-galm, “nest-boom”; expected unrelated, where the learned expectation after hearing nest was a word rhyming in -alm). Targets were occasionally inconsistent with expectations. In these inconsistent expectancy trials, targets were either unrelated (e.g., honk-mest, “base-manure”; unexpected unrelated), where the listener was expecting a related target, or related (e.g., nest-pest, “nest-plague”; unexpected related), where the listener was expecting an unrelated target. Participant expectations and phonological relatedness were thus manipulated factorially for three types of phonological overlap (rhyme, one onset phoneme, and three onset phonemes) at three interstimulus intervals (ISIs; 50, 500, and 2,000 msec). Lexical decisions to targets revealed evidence of expectancy-based strategies for all three types of overlap (e.g., faster responses to expected than to unexpected targets, irrespective of phonological relatedness) and evidence of automatic phonological processes, but only for the rhyme and three-phoneme onset overlap conditions and, most strongly, at the shortest ISI (e.g., faster responses to related than to unrelated targets, irrespective of expectations). Although phonological priming thus has both automatic and strategic components, it is possible to cleave them apart.

Abstract

Although researchers studying human speech recognition (HSR) and automatic speech recognition (ASR) share a common interest in how information processing systems (human or machine) recognize spoken language, there is little communication between the two disciplines. We suggest that this lack of communication follows largely from the fact that research in these related fields has focused on the mechanics of how speech can be recognized. In Marr's (1982) terms, emphasis has been on the algorithmic and implementational levels rather than on the computational level. In this article, we provide a computational-level analysis of the task of speech recognition, which reveals the close parallels between research concerned with HSR and ASR. We illustrate this relation by presenting a new computational model of human spoken-word recognition, built using techniques from the field of ASR that, in contrast to current existing models of HSR, recognizes words from real speech input.

Abstract

We report detailed analyses of a very large database on timing of speech perception collected by Smits et al. (Smits, R., Warner, N., McQueen, J.M., Cutler, A., 2003. Unfolding of phonetic information over time: A database of Dutch diphone perception. J. Acoust. Soc. Am. 113, 563–574). Eighteen listeners heard all possible diphones of Dutch, gated in portions of varying size and presented without background noise. The present report analyzes listeners’ responses across gates in terms of phonological features (voicing, place, and manner for consonants; height, backness, and length for vowels). The resulting patterns for feature perception differ from patterns reported when speech is presented in noise. The data are also analyzed for effects of stress and of phonological context (neighboring vowel vs. consonant); effects of these factors are observed to be surprisingly limited. Finally, statistical effects, such as overall phoneme frequency and transitional probabilities, along with response biases, are examined; these too exercise only limited effects on response patterns. The results suggest highly accurate speech perception on the basis of acoustic information alone.