Publications

Abstract

Computer models of cultural evolution have shown language properties emerging on interacting agents with a brain that lacks dedicated, nativist language modules. Notably, models using Bayesian agents provide a precise specification of (extra-)liguististic factors (e.g., genetic) that shape language through iterated learning (biases on language), and demonstrate that weak biases get expressed more strongly over time (bias amplification). Other models attempt to lessen assumption on agents’ innate predispositions even more, and emphasize self-organization within agents, highlighting glossogenesis (the development of language from a nonlinguistic state). Ultimately however, one also has to recognize that biology and culture are strongly interacting, forming a coevolving system. As such, computer models show that agents might (biologically) evolve to a state predisposed to language adaptability, where (culturally) stable language features might get assimilated into the genome via Baldwinian niche construction. In summary, while many questions about language evolution remain unanswered, it is clear that it is not to be completely understood from a purely biological, cognitivist perspective. Language should be regarded as (partially) emerging on the social interactions between large populations of speakers. In this context, agent models provide a sound approach to investigate the complex dynamics of genetic biasing on language and speech

Abstract

It has been suggested that some individuals may benefit more from social robots than do others. Using second
language (L2) as an example, the present study examined how individual differences in attitudes toward robots and personality
traits may be related to learning outcomes. Preliminary results with 24 Turkish-speaking adults suggest that negative attitudes
toward robots, more specifically thoughts and anxiety about the negative social impact that robots may have on the society,
predicted how well adults learned L2 words from a social robot. The possible implications of the findings as well as future directions are also discussed

Abstract

A wealth of studies has shown that more proficient monolingual speakers are better at predicting upcoming information during language comprehension. Similarly, prediction skills of adult second language (L2) speakers in their L2 have also been argued to be modulated by their L2 proficiency. How exactly language proficiency and prediction are linked, however, is yet to be systematically investigated. One group of language users which has the potential to provide invaluable insights into this link is bilingual children. In this paper, we compare bilingual children’s prediction skills with those of monolingual children and adult L2 speakers, and show how investigating bilingual children’s prediction skills may contribute to our understanding of how predictive processing works.

Abstract

There is a strong relation between children’s exposure to
spatial terms and their later memory accuracy. In the current
study, we tested whether the production of spatial terms by
children themselves predicts memory accuracy and whether
and how language modality of these encodings modulates
memory accuracy differently. Hearing child speakers of
Turkish and deaf child signers of Turkish Sign Language
described pictures of objects in various spatial relations to each
other and later tested for their memory accuracy of these
pictures in a surprise memory task. We found that having
described the spatial relation between the objects predicted
better memory accuracy. However, the modality of these
descriptions in sign, speech, or speech-plus-gesture did not
reveal differences in memory accuracy. We discuss the
implications of these findings for the relation between spatial
language, memory, and the modality of encoding.

Abstract

We describe a reaction time study in which listeners detected word or nonword syllable targets (e.g. zoo, trel) in sequences consisting of the target plus a consonant or syllable residue (trelsh, trelshek). The pattern of responses differed from an earlier word-spotting study with the same material, in which words were always harder to find if only a consonant residue remained. The earlier results should thus not be viewed in terms of syllabic parsing, but in terms of a universal role for syllables in speech perception; words which are accidentally present in spoken input (e.g. sell in self) can be rejected when they leave a residue of the input which could not itself be a word.

Abstract

In this paper we present a definition of Performance Grammar (PG), a psycholinguistically motivated syntax formalism, in declarative terms. PG aims not only at describing and explaining intuitive judgments and other data concerning the well–formedness of sentences of a language, but also at contributing to accounts of syntactic processing phenomena observable in language comprehension and language production. We highlight two general properties of human sentence generation, incrementality and late linearization,which make special demands on the design of grammar formalisms claiming psychological plausibility. In order to meet these demands, PG generates syntactic structures in a two-stage process. In the first and most important ‘hierarchical’ stage, unordered hierarchical structures (‘mobiles’) are assembled out of lexical building blocks. The key operation at work here is typed feature unification, which also delimits the positional options of the syntactic constituents in terms of so-called topological features. The second, much simpler stage takes care of arranging the branches of the mobile from left to right by ‘reading–out’ one positional option of every constituent. In this paper we concentrate on the structure assembly formalism in PG’s hierarchical component. We provide a declarative definition couched in an HPSG–style notation based on typed feature unification. Our emphasis throughout is on linear order constraints.

Abstract

It is generally assumed that verbs have an ‘argument structure’, which imposes various constraints on the noun phrases that can or must go with the verb, and an ‘event structure’, which characterises the particular temporal characteristics of the ‘event’ which the verb relates to: this event may be a state, a process, an activity, an ‘event in the narrow sense’, and others. In this paper, it is argued that that argument structure and event structure should be brought together. The lexical content of a verb assigns descriptive properties to one or more arguments at one or more times, hence verbs have an ‘argument time-structure’ (AT-structure). Numerous morphological and syntactical operations, such as participle formation or complex verb constructions, modify this AT-structure. This is illustrated with German recipient constructions such as ein Buch geschenkt bekommen or das Fenster geöffnet kriegen.

Abstract

Voice assimilation in Dutch is an optional phonological rule which changes the surface forms of words and in doing so may violate the otherwise obligatory phonological rule of syllablefinal devoicing. We report two experiments examining the influence of voice assimilation on phoneme processing, in lexical compound words and in noun-verb phrases. Processing was not impaired in appropriate assimilation contexts across morpheme boundaries, but was impaired when devoicing was violated (a) in an inappropriate non-assimilatory) context, or (b) across a syntactic boundary.

Abstract

Like Einstein's general theory of relativity is concerned with explaining the basics of an observable experience – i.e., gravity – most people take for granted that Chomsky's theory of generative grammar (GG) is concerned with the basic nature of language. This chapter highlights a mere subset of central constructs in GG, showing how they have featured prominently and thus shaped formal linguistic studies in multilingualism. Because multilingualism includes a wide range of nonmonolingual populations, the constructs are divided across child bilingualism and adult third language for greater coverage. In the case of the former, the chapter examines how poverty of the stimulus has been investigated. Using the nascent field of L3/Ln acquisition as the backdrop, it discusses how the GG constructs of I-language versus E-language sit at the core of debates regarding the very notion of what linguistic transfer and mental representations should be taken to be.

Abstract

Citations play a crucial role in the scientific discourse, in information retrieval, and in bibliometrics. Many initiatives are currently promoting the idea of having free and open citation data. Creation of citation data, however, is not part of the cataloging workflow in libraries nowadays.
In this paper, we present our project Linked Open Citation Database, in which we design distributed processes and a system infrastructure based on linked data technology. The goal is to show that efficiently cataloging citations in libraries using a semi-automatic approach is possible. We specifically describe the current state of the workflow and its implementation. We show that we could significantly improve the automatic reference extraction that is crucial for the subsequent data curation. We further give insights on the curation and linking process and provide evaluation results that not only direct the further development of the project, but also allow us to discuss its overall feasibility.

Abstract

It is widely held that smells and flavors are impossible to put into words. In this paper we test this claim by seeking predictive patterns in wine reviews, which ostensibly aim to provide guides to perceptual content. Wine reviews have previously been critiqued as random and meaningless. We collected an English corpus of wine reviews with their structured metadata, and applied machine learning techniques to automatically predict the wine's color, grape variety, and country of origin. To train the three supervised classifiers, three different information sources were incorporated: lexical bag-of-words features, domain-specific terminology features, and semantic word embedding features. In addition, using regression analysis we investigated basic review properties, i.e., review length, average word length, and their relationship to the scalar values of price and review score. Our results show that wine experts do share a common vocabulary to describe wines and they use this in a consistent way, which makes it possible to automatically predict wine characteristics based on the review text alone. This means that odors and flavors may be more expressible in language than typically acknowledged.

Abstract

We present the Narrative Brain Dataset, an fMRI dataset that was collected during spoken presentation of short excerpts of three
stories in Dutch. Together with the brain imaging data, the dataset contains the written versions of the stimulation texts. The texts are
accompanied with stochastic (perplexity and entropy) and semantic computational linguistic measures. The richness and unconstrained
nature of the data allows the study of language processing in the brain in a more naturalistic setting than is common for fMRI studies.
We hope that by making NBD available we serve the double purpose of providing useful neural data to researchers interested in natural
language processing in the brain and to further stimulate data sharing in the field of neuroscience of language.

Abstract

For (semi-)automated subject indexing systems in digital libraries, it is often more practical to use metadata such as the title of a publication instead of the full-text or the abstract. Therefore, it is desirable to have good text mining and text classification algorithms that operate well already on the title of a publication. So far, the classification performance on titles is not competitive with the performance on the full-texts if the same number of training samples is used for training. However, it is much easier to obtain title data in large quantities and to use it for training than full-text data. In this paper, we investigate the question how models obtained from training on increasing amounts of title training data compare to models from training on a constant number of full-texts. We evaluate this question on a large-scale dataset from the medical domain (PubMed) and from economics (EconBiz). In these datasets, the titles and annotations of millions of publications are available, and they outnumber the available full-texts by a factor of 20 and 15, respectively. To exploit these large amounts of data to their full potential, we develop three strong deep learning classifiers and evaluate their performance on the two datasets. The results are promising. On the EconBiz dataset, all three classifiers outperform their full-text counterparts by a large margin. The best title-based classifier outperforms the best full-text method by 9.4%. On the PubMed dataset, the best title-based method almost reaches the performance of the best full-text classifier, with a difference of only 2.9%.

Abstract

What is the relationship between the language we speak and the way we think? Researchers working at the interface of language and cognition hope to understand the complex interplay between linguistic structures and the way the mind works. This is thorny territory in anthropology and its closely allied disciplines, such as linguistics and psychology.

Abstract

Readers experience a number of sensations during reading. They do
not – or do not only – process words and sentences in a detached, abstract
manner. Instead they “perceive” what they read about. They see descriptions of
scenery, feel what characters feel, and hear the sounds in a story. These sensa-
tions tend to be grouped under the umbrella terms “mental simulation” and
“mental imagery.” This chapter provides an overview of empirical research on
the role of mental simulation during literary reading. Our chapter also discusses
what mental simulation is and how it relates to mental imagery. Moreover, it
explores how mental simulation plays a role in leading models of literary read-
ing and investigates under what circumstances mental simulation occurs dur-
ing literature reading. Finally, the effect of mental simulation on the literary
reader’s experience is discussed, and suggestions and unresolved issues in this
field are formulated.

Abstract

Visual and auditory channels have different affordances and
this is mirrored in what information is available for linguistic
encoding. The visual channel has high spatial acuity, whereas
the auditory channel has better temporal acuity. These
differences may lead to different conceptualizations of events
and affect multimodal language production. Previous studies of
motion events typically present visual input to elicit speech and
gesture. The present study compared events presented as audio-
only, visual-only, or multimodal (visual+audio) input and
assessed speech and co-speech gesture for path and manner of
motion in Turkish. Speakers with audio-only input mentioned
path more and manner less in verbal descriptions, compared to
speakers who had visual input. There was no difference in the
type or frequency of gestures across conditions, and gestures
were dominated by path-only gestures. This suggests that input
modality influences speakers’ encoding of path and manner of
motion events in speech, but not in co-speech gestures.

Abstract

Recurrent neural networks (RNNs) have long been an architecture of interest for computational models of human sentence processing. The recently introduced Transformer architecture outperforms RNNs on many natural language processing tasks but little is known about its ability to model human language processing. We compare Transformer- and RNN-based language models’ ability to account for measures of human reading effort. Our analysis shows Transformers to outperform RNNs in explaining self-paced reading times and neural activity during reading English sentences, challenging the widely held idea that human sentence processing involves recurrent and immediate processing and provides evidence for cue-based retrieval.

Abstract

This study addresses the question whether visually grounded speech recognition (VGS) models learn to capture sentence semantics without access to any prior linguistic knowledge. We produce synthetic and natural spoken versions of a well known semantic textual similarity database and show that our VGS model produces embeddings that correlate well with human semantic similarity judgements. Our results show that a model trained on a small image-caption database outperforms two models trained on much larger databases, indicating that database size is not all that matters. We also investigate the importance of having multiple captions per image and find that this is indeed helpful even if the total number of images is lower, suggesting that paraphrasing is a valuable learning signal. While the general trend in the field is to create ever larger datasets to train models on, our findings indicate other characteristics of the database can just as important.

Abstract

The ultimate goal of our research is to improve an existing speech-based computational model of human speech recognition on the task of simulating the role of fine-grained phonetic information in human speech processing. As part of this work we are investigating articulatory feature classifiers that are able to create reliable and accurate transcriptions of the articulatory behaviour encoded in the acoustic speech signal. Articulatory feature (AF) modelling of speech has received a considerable amount of attention in automatic speech recognition research. Different approaches have been used to build AF classifiers, most notably multi-layer perceptrons. Recently, deep neural networks have been applied to the task of AF classification. This paper aims to improve AF classification by investigating two different approaches: 1) investigating the usefulness of a deep Convolutional neural network (CNN) for AF classification; 2) integrating the Mel filtering operation into the CNN architecture. The results showed a remarkable improvement in classification accuracy of the CNNs over state-of-the-art AF classification results for Dutch, most notably in the minority classes. Integrating the Mel filtering operation into the CNN architecture did not further improve classification performance.

Abstract

Language emergence is characterized by a high degree of lex-
ical variation. It has been suggested that the speed at which
lexical conventionalization occurs depends partially on social
structure. In large communities, individuals receive input from
many sources, creating a pressure for lexical convergence.
In small, insular communities, individuals can remember id-
iolects and share common ground with interlocuters, allow-
ing these communities to retain a high degree of lexical vari-
ation. We look at lexical variation in Kata Kolok, a sign lan-
guage which emerged six generations ago in a Balinese vil-
lage, where women tend to have more tightly-knit social net-
works than men. We test if there are differing degrees of lexical
uniformity between women and men by reanalyzing a picture
description task in Kata Kolok. We find that women’s produc-
tions exhibit less lexical uniformity than men’s. One possible
explanation of this finding is that women’s more tightly-knit
social networks allow for remembering idiolects, alleviating
the pressure for lexical convergence, but social network data
from the Kata Kolok community is needed to support this ex-
planation.

Abstract

Analyzing EEG signals recorded while participants are listening to continuous speech with the purpose of testing linguistic hypotheses is complicated by the fact that the signals simultaneously reflect exogenous acoustic excitation and endogenous linguistic processing. This makes it difficult to trace subtle differences that occur in mid-sentence position. We apply an analysis based on multivariate temporal response functions to uncover subtle mid-sentence effects. This approach is based on a per-stimulus estimate of the response of the neural system to speech input. Analyzing EEG signals predicted on the basis of the response functions might then bring to light conditionspecific differences in the filtered signals. We validate this approach by means of an analysis of EEG signals recorded with isolated word stimuli. Then, we apply the validated method to the analysis of the responses to the same words in the middle of meaningful sentences.

Abstract

Egophoric verbal marking is a typological feature common to Barbacoan languages, but otherwise unknown in the Andean sphere. The verbal systems of three out of the four living Barbacoan languages, Cha’palaa, Tsafiki and Awa Pit, have previously been shown to express egophoric contrasts. The status of Guambiano has, however, remained uncertain. In this chapter, I show that there are in fact two layers of egophoric or egophoric-like marking visible in Guambiano’s grammar. Guambiano patterns with certain other (non-Barbacoan) languages in having ego-categories which function within a broader evidential system. It is additionally possible to detect what is possibly a more archaic layer of egophoric marking in Guambiano’s verbal system. This marking may be inherited from a common Barbacoan system, thus pointing to a potential genealogical basis for the egophoric patterning common to these languages. The multiple formal expressions of egophoricity apparent both within and across the four languages reveal how egophoric contrasts are susceptible to structural renewal, suggesting a pan-Barbacoan preoccupation with the linguistic encoding of self-knowledge.

Abstract

Our ability to use language is multimodal and requires tight coordination between what is expressed in speech and in gesture, such as pointing or iconic gestures that convey semantic, syntactic and pragmatic information related to speakers’ messages. Interestingly, what is expressed in gesture and how it is coordinated with speech differs in speakers of different languages. This paper discusses recent findings on the development of children’s multimodal expressions taking cross-linguistic variation into account. Although some aspects of speech-gesture development show language-specificity from an early age, it might still take children until nine years of age to exhibit fully adult patterns of cross-linguistic variation. These findings reveal insights about how children coordinate different levels of representations given that their development is constrained by patterns that are specific to their languages.

Abstract

Use of language in face-to-face context is multimodal. Production and perception of speech take place in the context of visual articulators such as lips, face, or hand gestures which convey relevant information to what is expressed in speech at different levels of language. While lips convey information at the phonological level, gestures contribute to semantic, pragmatic, and syntactic information, as well as to discourse cohesion. This chapter overviews recent findings showing that speech and gesture (e.g. a drinking gesture as someone says, “Would you like a drink?”) interact during production and comprehension of language at the behavioral, cognitive, and neural levels. Implications of these findings for current psycholinguistic theories and how they can be expanded to consider the multimodal context of language processing are discussed.

Abstract

Gender-(mis)matching pronouns have been studied extensively in experiments. However, a phenomenon common to various languages has thus far been overlooked: the systemic use of non-feminine pronouns when referring to female individuals. The present study is the first to provide experimental insights into the interpretation of such a pronoun: Limburgian zien ‘his/its’ and Dutch zijn ‘his/its’ are grammatically ambiguous between masculine and neuter, but while Limburgian zien can refer to women, the Dutch equivalent zijn cannot. Employing an acceptability judgment task, we presented speakers of Limburgian (N = 51) with recordings of sentences in Limburgian featuring zien, and speakers of Dutch (N = 52) with Dutch translations of these sentences featuring zijn. All sentences featured a potential male or female antecedent embedded in a stereotypically male or female context. We found that ratings were higher for sentences in which the pronoun could refer back to the antecedent. For Limburgians, this extended to sentences mentioning female individuals. Context further modulated sentence appreciation. Possible mechanisms regarding the interpretation of zien as coreferential with a female individual will be discussed.

Abstract

Word count estimation (WCE) from audio recordings has a number of applications, including quantifying the amount of speech that language-learning infants hear in their natural environments, as captured by daylong recordings made with devices worn by infants. To be applicable in a wide range of scenarios and also low-resource domains, WCE tools should be extremely robust against varying signal conditions and require minimal access to labeled training data in the target domain. For this purpose, earlier work has used automatic syllabification of speech, followed by a least-squares-mapping of syllables to word counts. This paper compares a number of previously proposed syllabifiers in the WCE task, including a supervised bi-directional long short-term memory (BLSTM) network that is trained on a language for which high quality syllable annotations are available (a “high resource language”), and reports how the alternative methods compare on different languages and signal conditions. We also explore additive noise and varying-channel data augmentation strategies for BLSTM training, and show how they improve performance in both matching and mismatching languages. Intriguingly, we also find that even though the BLSTM works on languages beyond its training data, the unsupervised algorithms can still outperform it in challenging signal conditions on novel languages.

Abstract

Conversation analysis (CA) is an approach to the study of language and social interaction that puts at center stage its sequential development. The chain of initiating and responding actions that characterizes any interaction is a source of internal evidence for the meaning of social behavior as it exposes the understandings that participants themselves give of what one another is doing. Such an analysis requires the close and repeated inspection of audio and video recordings of naturally occurring interaction, supported by transcripts and other forms of annotation. Distributional regularities are complemented by a demonstration of participants' orientation to deviant behavior. CA has long maintained a constructive dialogue and reciprocal influence with linguistic anthropology. This includes a recent convergence on the cross-linguistic and cross-cultural study of social interaction.

Abstract

For almost two decades, the poor performance observed with the so-called Director task has been interpreted as evidence of limited use of Theory of Mind in communication. Here we propose a probabilistic model of common ground in referential communication that derives three inferences from an utterance: what the speaker is talking about in a visual context, what she knows about the context, and what referential expressions she prefers. We tested our model by comparing its inferences with those made by human participants and found that it closely mirrors their judgments, whereas an alternative model compromising the hearer’s expectations of cooperativeness and efficiency reveals a worse fit to the human data. Rather than assuming that common ground is fixed in a given exchange and may or may not constrain reference resolution, we show how common ground can be inferred as part of the process of reference assignment.

Abstract

While there are many studies on information retrieval models using full-text, there are presently no comparison studies of full-text retrieval vs. retrieval only over the titles of documents. On the one hand, the full-text of documents like scientific papers is not always available due to, e.g., copyright policies of academic publishers. On the other hand, conducting a search based on titles alone has strong limitations. Titles are short and therefore may not contain enough information to yield satisfactory search results. In this paper, we compare different retrieval models regarding their search performance on the full-text vs. only titles of documents. We use different datasets, including the three digital library datasets: EconBiz, IREON, and PubMed. The results show that it is possible to build effective title-based retrieval models that provide competitive results comparable to full-text retrieval. The difference between the average evaluation results of the best title-based retrieval models is only 3% less than those of the best full-text-based retrieval models.

Abstract

In the language Duna (Trans New Guinea), egophoric distributional patterns are a pervasive characteristic of verbal morphology, but do not comprise a single coherent system. Many morphemes, including evidential markers and future time inflections, show strong tendencies to co-occur with ‘informant’ subjects (the speaker in a declarative, the addressee in an interrogative), or alternatively with non-informant subjects. The person sensitivity of the Duna forms is observable in frequency, speaker judgments of sayability, and subject implicatures. Egophoric and non-egophoric distributional patterns are motivated by the individual semantics of the morphemes, their perspective-taking properties, and logical and/or conventionalised expectations of how people experience and talk about events. Distributional tendencies can also be flouted, providing a resource for speakers to convey attitudes towards their own knowledge and experiences, or the knowledge and experiences of others.

Abstract

Languages with egophoric systems require their users to pay special attention to who knows what in the speech situation, providing formal marking of whether the speaker or addressee has personal knowledge of the event being discussed. Such systems have only recently come to be studied in cross-linguistic perspective. This chapter has two aims in regard to contributing to our understanding of egophoric marking. Firstly, it presents relevant data from a relatively under-described and endangered language, Kaluli (aka Bosavi), spoken in Papua New Guinea. Unusually, Kaluli tense inflections appear to show a mix of both egophoric and first vs non-first person-marking features, as well as other contrasts that are broadly relevant to a typology of egophoricity, such as special constructions for the expression of involuntary experience. Secondly, the chapter makes a preliminary foray into issues concerning egophoric marking and child language, drawing on a naturalistic corpus of child-caregiver interactions. Questions for future investigation raised by the Kaluli data concern, for example, the potentially challenging nature of mastering inflections that are sensitive to both person and speech act type, the possible role of question-answer pairs in children’s acquisition of egophoric morphology, and whether there are special features of epistemic access and authority that relate particularly to child-adult interactions.

Abstract

Fine-Tracker is a speech-based model of human speech
recognition. While previous work has shown that Fine-Tracker
is successful at modelling aspects of human spoken-word
recognition, its speech recognition performance is not
comparable to that of human performance, possibly due to
suboptimal intermediate articulatory feature (AF)
representations. This study investigates the effect of improved
AF representations, obtained using a state-of-the-art deep
convolutional network, on Fine-Tracker’s simulation and
recognition performance: Although the improved AF quality
resulted in improved speech recognition; it, surprisingly, did
not lead to an improvement in Fine-Tracker’s simulation power.

Abstract

The current version of the psycholinguistic model of human word recognition Shortlist suffers from two unrealistic constraints. First, the input of Shortlist must consist of a single string of phoneme symbols. Second, the current version of the search in Shortlist makes it difficult to deal with insertions and deletions in the input phoneme string. This research attempts to fully automatically derive a phoneme string from the acoustic signal that is as close as possible to the number of phonemes in the lexical representation of the word. We optimised an Automatic Phone Recogniser (APR) using two approaches, viz. varying the value of the mismatch parameter and optimising the APR output strings on the output of Shortlist. The approaches show that it will be very difficult to satisfy the input requirements of the present version of Shortlist with a phoneme string generated by an APR.

Abstract

Due to pronunciation variation, many insertions and deletions of phones occur in spontaneous speech. The psycholinguistic model of human speech recognition Shortlist is not well able to deal with phone insertions and deletions and is therefore not well suited for dealing with real-life input. The research presented in this paper explains how Shortlist can benefit from pronunciation variation modelling in dealing with real-life input. Pronunciation variation was modelled by including variants into the lexicon of Shortlist. A series of experiments was carried out to find the optimal acoustic model set for transcribing the training material that was used as basis for the generation of the variants. The Shortlist experiments clearly showed that Shortlist benefits from pronunciation variation modelling. However, the performance of Shortlist stays far behind the performance of other, more conventional speech recognisers.

Abstract

This chapter describes neurolinguistic aspects of morphology, morphological theory, and especially morphological processing. It briefly mentions the main processing models in the literature and how they deal with morphological issues, i.e. full-listing models (all morphologically related words are listed separately in the lexicon and are processed individually), full-parsing or decompositional models (morphologically related words are not listed in the lexicon but are decomposed into their constituent morphemes, each of which is listed in the lexicon), and hybrid, so-called dual route, models (regular morphologically related words are decomposed, irregular words are listed). The chapter also summarizes some important findings from the literature that bear on neurolinguistic aspects of morphological processing, from both language comprehension and language production, taking into consideration neuropsychological patient studies as well as studies employing neuroimaging methods.

Abstract

The time course of metrical encoding, i.e. stress, during speech production is investigated. In a first experiment, participants were presented with pictures whose bisyllabic Dutch names had initial or final stress (KAno 'canoe' vs. kaNON 'cannon'; capital letters indicate stressed syllables). Picture names were matched for frequency and object recognition latencies. When participants were asked to judge whether picture names had stress on the first or second syllable, they showed significantly faster decision times for initially stressed targets than for targets with final stress. Experiment 2 replicated this effect with trisyllabic picture names (faster RTs for penultimate stress than for ultimate stress). In our view, these results reflect the incremental phonological encoding process. Wheeldon and Levelt (1995) found that segmental encoding is a process running from the beginning to the end of words. Here, we present evidence that the metrical pattern of words, i.e. stress, is also encoded incrementally.