Publications

Abstract

Acquiring the sounds of a language involves learning to recognize distributional patterns present in the input. We show that among adult learners, this distributional learning of auditory categories (which are conceived of here as probability density functions in a multidimensional space) is constrained by the salience of the dimensions that form the axes of this perceptual space. Only with a particular ratio of variation in the perceptual dimensions was category learning driven by the distributional properties of the input.

Abstract

Three groups of monolingual listeners, with Standard Chinese, Dutch and Hungarian as their native language, judged pairs of trisyllabic stimuli which differed only in their itch pattern. The segmental structure of the stimuli was made up by the experimenters and presented to subjects as being taken from a little-known language spoken on a South Pacific island. Pitch patterns consisted of a single rise-fall located on or near the second syllable. By and large, listeners selected the stimulus with the higher peak, the later eak, and the higher end rise as the one that signalled a question, regardless of language group. The result is argued to reflect innate, non-linguistic knowledge of the meaning of pitch variation, notably Ohala’s Frequency Code. A significant difference between groups is explained as due to the influence of the mother tongue.

Abstract

Child songs are a great source for linguistic learning. Here we explore whether children can acquire novel words in a second language by playing a game featuring child songs in a playhouse. We present data from three studies that serve as scientific proof for the functionality of one game of the playhouse: the Energy Center. For this game, three hand-bikes were mounted on a panel. When children start moving the hand-bikes, child songs start playing simultaneously. Once the children produce enough energy with the hand-bikes, the songs are additionally accompanied with the sounds of musical instruments. In our studies, children executed a picture-selection task to evaluate whether they acquired new vocabulary from the songs presented during the game. Two of our studies were run in the field, one at a Dutch and one at an Indian pre-school. The third study features data from a more controlled laboratory setting. Our results partly confirm that the Energy Center is a successful means to support vocabulary acquisition in a second language. More research with larger sample sizes and longer access to the Energy Center is needed to evaluate the overall functionality of the game. Based on informal observations at our test sites, however, we are certain that children do pick up linguistic content from the songs during play, as many of the children repeat words and phrases from songs they heard. We will pick up upon these promising observations during future studies

Abstract

This paper investigates the time and space complexity of word order computation in the psycholinguistically motivated grammar formalism of Performance Grammar (PG). In PG, the first stage of syntax assembly yields an unordered tree ('mobile') consisting of a hierarchy of lexical frames (lexically anchored elementary trees). Associated with each lexica l frame is a linearizer—a Finite-State Automaton that locally computes the left-to-right order of the branches of the frame. Linearization takes place after the promotion component may have raised certain constituents (e.g. Wh- or focused phrases) into the domain of lexical frames higher up in the syntactic mobile. We show that the worst-case time and space complexity of analyzing input strings of length n is O(n5) and O(n4), respectively. This result compares favorably with the time complexity of word-order computations in Tree Adjoining Grammar (TAG). A comparison with Head-Driven Phrase Structure Grammar (HPSG) reveals that PG yields a more declarative linearization method, provided that the FSA is rewritten as an equivalent regular expression.

Abstract

Many current sentence generators lack the ability to compute elliptical versions of coordinated clauses in accordance with the rules for Gapping, Forward and Backward Conjunction Reduction, and SGF (Subject Gap in clauses with Finite/ Fronted verb). We describe a module (implemented in JAVA, with German and Dutch as target languages) that takes non-elliptical coordinated clauses as input and returns all reduced versions licensed by coordinative ellipsis. It is loosely based on a new psycholinguistic theory of coordinative ellipsis proposed by Kempen. In this theory, coordinative ellipsis is not supposed to result from the application of declarative grammar rules for clause formation but from a procedural component that interacts with the sentence generator and may block the overt expression of certain constituents.

Abstract

We describe a generation-oriented workbench for the Performance Grammar (PG) formalism, highlighting the treatment of certain word order and movement constraints in Dutch and German. PG enables a simple and uniform treatment of a heterogeneous collection of linear order phenomena in the domain of verb constructions (variably known as Cross-serial Dependencies, Verb Raising, Clause Union, Extraposition, Third Construction, Particle Hopping, etc.). The central data structures enabling this feature are clausal “topologies”: one-dimensional arrays associated with clauses, whose cells (“slots”) provide landing sites for the constituents of the clause. Movement operations are enabled by unification of lateral slots of topologies at adjacent levels of the clause hierarchy. The PGW generator assists the grammar developer in testing whether the implemented syntactic knowledge allows all and only the well-formed permutations of constituents.

Abstract

Prediction in language has traditionally been studied using
simple designs in which neural responses to expected
and unexpected words are compared in a categorical
fashion. However, these designs have been contested
as being ‘prediction encouraging’, potentially exaggerating
the importance of prediction in language understanding.
A few recent studies have begun to address
these worries by using model-based approaches to probe
the effects of linguistic predictability in naturalistic stimuli
(e.g. continuous narrative). However, these studies
so far only looked at very local forms of prediction, using
models that take no more than the prior two words into
account when computing a word’s predictability. Here,
we extend this approach using a state-of-the-art neural
language model that can take roughly 500 times longer
linguistic contexts into account. Predictability estimates
fromthe neural network offer amuch better fit to EEG data
from subjects listening to naturalistic narrative than simpler
models, and reveal strong surprise responses akin to
the P200 and N400. These results show that predictability
effects in language are not a side-effect of simple designs,
and demonstrate the practical use of recent advances
in AI for the cognitive neuroscience of language.

Abstract

Longitudinally collected VOT data from an early English-Italian bilingual who became increasingly English-dominant was analyzed. Stops in English were always produced with significantly longer VOT than in Italian. However, the speaker did not show any significant change in the VOT production in either language over time, despite the clear dominance of English in his every day language use later in his life. The results indicate that – unlike L2 learners – early bilinguals may remain unaffected by language use with respect to phonetic realization.

Abstract

What makes some words harder to learn than others in a second language? Although some robust factors have been identified based on small scale experimental studies, many relevant factors are difficult to study in such experiments due to the amount of data necessary to test them. Here, we investigate what factors affect the ease of learning of a word in a second language using a large data set of users learning English as a second language through the Duolingo mobile app. In a regression analysis, we test and confirm the well-studied effect of cognate status on word learning accuracy. Furthermore, we find significant effects for both cross-linguistic semantic alignment and English semantic density, two novel predictors derived from large scale distributional models of lexical semantics. Finally, we provide data on several other psycholinguistically plausible word level predictors. We conclude with a discussion of the limits, benefits and future research potential of using big data for investigating second language learning.

Abstract

A common assumption in the cognitive sciences is that artificial and natural language learning rely on shared mechanisms. However, attempts to bridge the two have yielded ambiguous results. We suggest that an empirical disconnect between the computations employed during learning and the methods employed at test may explain these mixed results. Further, we propose statistically-based chunking as a potential computational link between artificial and natural language learning. We compare the acquisition of non-adjacent dependencies to that of natural language structure using two types of tasks: reflection-based 2AFC measures, and processing-based recall measures, the latter being more computationally analogous to the processes used during language acquisition. Our results demonstrate that task-type significantly influences the correlations observed between artificial and natural language acquisition, with reflection-based and processing-based measures correlating within – but not across – task-type. These findings have fundamental implications for artificial-to-natural language comparisons, both methodologically and theoretically.

Abstract

Attempts to connect individual differences in statistical learning with broader aspects of cognition have received considerable attention, but have yielded mixed results. A possible explanation is that statistical learning is typically tested using the two-alternative forced choice (2AFC) task. As a meta-cognitive task relying on explicit familiarity judgments, 2AFC may not accurately capture implicitly formed statistical computations. In this paper, we adapt the classic serial-recall memory paradigm to implicitly test statistical learning in a statistically-induced chunking recall (SICR) task. We hypothesized that artificial language exposure would lead subjects to chunk recurring statistical patterns, facilitating recall of words from the input. Experiment 1 demonstrates that SICR offers more fine-grained insights into individual differences in statistical learning than 2AFC. Experiment 2 shows that SICR has higher test-retest reliability than that reported for 2AFC. Thus, SICR offers a more sensitive measure of individual differences, suggesting that basic chunking abilities may explain statistical learning.

Abstract

n this study we investigated the durational correlates of lexical stress and pitch accent at normal and fast speech rate in Dutch. Previous literature on English shows that durations of lexically unstressed vowels are reduced more than stressed vowels when speakers increase their speech rate. We found that the same holds for Dutch, irrespective of whether the unstressed vowel is schwa or a "full" vowel. In the same line, we expected that vowels in words without a pitch accent would be shortened relatively more than vowels in words with a pitch accent. This was not the case: if anything, the accented vowels were shortened relatively more than the unaccented vowels. We conclude that duration is an important cue for lexical stress, but not for pitch accent.

Abstract

Studies on fast speech have shown that word-level timing of fast speech differs from that of normal rate speech in that unstressed syllables are shortened more than stressed syllables as speech rate increases. An earlier experiment showed that the intelligibility of time-compressed speech could not be improved by making its temporal organisation closer to natural fast speech. To test the hypothesis that segmental intelligibility is more important than prosodic timing in listening to timecompressed speech, the intelligibility of bisyllabic words was tested in three time-compression conditions: either stressed and unstressed syllable were compressed to the same degree, or the stressed syllable was compressed more than the unstressed syllable, or the reverse. As was found before, imitating wordlevel timing of fast speech did not improve intelligibility over linear compression. However, the results did not confirm the hypothesis either: there was no difference in intelligibility between the three compression conditions. We conclude that segmental intelligibility plays an important role, but further research is necessary to decide between the contributions of prosody and segmental intelligibility to the word-level intelligibility of time-compressed speech.

Abstract

Mutations of the epidermal growth factor receptor (EGFR) gene are found at a relatively high frequency in glioma, with the most common being the de2-7 EGFR (or EGFRvIII). This mutation arises from an in-frame deletion of exons 2-7, which removes 267 amino acids from the extracellular domain of the receptor. Despite being unable to bind ligand, the de2-7 EGFR is constitutively active at a low level. Transfection of human glioma cells with the de2-7 EGFR has little effect in vitro, but when grown as tumor xenografts this mutated receptor imparts a dramatic growth advantage. We mapped the phosphorylation pattern of de2-7 EGFR, both in vivo and in vitro, using a panel of antibodies specific for different phosphorylated tyrosine residues. Phosphorylation of de2-7 EGFR was detected constitutively at all tyrosine sites surveyed in vitro and in vivo, including tyrosine 845, a known target in the wild-type EGFR for src kinase. There was a substantial upregulation of phosphorylation at every yrosine residue of the de2-7 EGFR when cells were grown in vivo compared to the receptor isolated from cells cultured in vitro. Upregulation of phosphorylation at tyrosine 845 could be stimulated in vitro by the addition of specific components of the ECM via an integrindependent mechanism. These observations may partially explain why the growth enhancement mediated by de2-7 EGFR is largely restricted to the in vivo environment

Abstract

This study investigates effects of prosodic factors (prominence, boundary) on coarticulatory Vnasalization in Australian English (AusE) in CVN and NVC in comparison to those in American English
(AmE). As in AmE, prominence was found to
lengthen N, but to reduce V-nasalization, enhancing N’s nasality and V’s orality, respectively (paradigmatic contrast enhancement). But the prominence effect in CVN was more robust than that in AmE. Again similar to findings in AmE, boundary
induced a reduction of N-duration and V-nasalization phrase-initially (syntagmatic contrast enhancement), and increased the nasality of both C and V phrasefinally.
But AusE showed some differences in terms
of the magnitude of V nasalization and N duration. The results suggest that the linguistic contrast enhancements underlie prosodic-structure modulation of coarticulatory V-nasalization in
comparable ways across dialects, while the fine phonetic detail indicates that the phonetics-prosody interplay is internalized in the individual dialect’s phonetic grammar.

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

In production, frequently used words are preferentially extended to new, though related meanings. In comprehension, frequent exposure to a word instead makes the learner confident that all of the word’s legitimate uses have been experienced, resulting in an entrenched form-meaning mapping between the word and its experienced meaning(s). This results in a perception-production dissociation, where the forms speakers are most likely to map onto a novel meaning are precisely the forms that they believe can never be used that way. At first glance, this result challenges the idea of bidirectional form-meaning mappings, assumed by all current approaches to linguistic theory. In this paper, we show that bidirectional form-meaning mappings are not in fact challenged by this production-perception dissociation. We show that the production-perception dissociation is expected even if learners of the lexicon acquire simple symmetrical form-meaning associations through simple Hebbian learning.

Abstract

Deaf children born to hearing parents are exposed to language input quite late, which has long-lasting effects on language production. Previous studies with deaf individuals mostly focused on linguistic expressions of motion events, which have several event components. We do not know if similar effects emerge in simple events such as descriptions of spatial configurations of objects. Moreover, previous data mainly come from late adult signers. There is not much known about language development of late signing children soon after learning sign language. We compared simple event descriptions of late signers of Turkish Sign Language (adults, children) to age-matched native signers. Our results indicate that while late signers in both age groups are native-like in frequency of expressing a relational encoding, they lag behind native signers in using morphologically complex linguistic forms compared to other simple forms. Late signing children perform similar to adults and thus showed no development over time.

Abstract

English and German have similar prosody, but their speakers realize some pitch falls (not rises) in subtly different ways. We here test for asymmetry in perception. An ABX discrimination task requiring F0 slope or duration judgements on isolated vowels revealed no cross-language difference in duration or F0 fall discrimination, but discrimination of rises (realized similarly in each language) was less accurate for English than for German listeners. This unexpected finding may reflect greater sensitivity to rising patterns by German listeners, or reduced sensitivity by English listeners as a result of extensive exposure to phrase-final rises (“uptalk”) in their language

Abstract

Human speakers often produce sentences incrementally. They can start speaking having in mind only a fragmentary idea of what they want to say, and while saying this they refine the contents underlying subsequent parts of the utterance. This capability imposes a number of constraints on the design of a syntactic processor. This paper explores these constraints and evaluates some recent computational sentence generators from the perspective of incremental production.

Abstract

The psychological process of producing sentences includes conceptualization (selecting to-beexpressed conceptual content) and formulation (translating conceptual content into syntactic structures of a language). There is ample evidence, both intuitive and experimental, that the conceptualizing and formulating processes often proceed concurrently, not strictly serially. James Lindsley (Cognitive Psych.,1975, 7, 1-19; J.Psycholinguistic Res., 1976, 5, 331-354) has developed a concurrent model which proved succesful in an experimental situation where simple English Subject-Verb (SV) sentences such as “The boy is greeting”,”The girl is kicking” were produced as descriptions of pictures which showed actor and action. The measurements were reaction times defined as the interval between the moment a picture appeared on a screen and the onset of the vocal utterance by the speaker. Lindsley could show, among other things, that the formulation process for an SV sentence doesn’t start immediately after the actor of a picture (that is, the conceptual content underlying the surface Subject phrase) has been identified, but is somewhat delayed. The delay was needed, according to Lindsley, in order to prevent dysfluencies (hesitations) between surface Subject and verb. We replicated Lindsley’s data for Dutch. However, his model proved inadequate when we added Dutch Verb-Subject (VS) constructions which are obligatory in certain syntactic contexts but synonymous with their SV counterparts. A sentence production theory which is being developed by the first author is able to provide an accurate account of the data. The abovementioned delay is attributed to certain precautions the sentence generator has to take in case of SV but not of VS sentences. These precautions are related to the goal of attaining syntactic coherence of the utterance as a whole, not to the prevention of dysfluencies.

Abstract

The previous literature has suggested that the hand movement in co-speech gestures and signs consists of a series of phases with qualitatively different dynamic characteristics. In this paper, we propose a syntagmatic rule system for movement phases that applies to both co-speech gestures and signs. Descriptive criteria for the rule system were developed for the analysis video-recorded continuous production of signs and gesture. It involves segmenting a stream of body movement into phases and identifying different phase types. Two human coders used the criteria to analyze signs and cospeech gestures that are produced in natural discourse. It was found that the criteria yielded good inter-coder reliability. These criteria can be used for the technology of automatic recognition of signs and co-speech gestures in order to segment continuous production and identify the potentially meaningbearing phase.

Abstract

In German, word-initial lax fricatives may be produced with substantially reduced glottal vibration after voiceless obstruents. This assimilation occurs more frequently and to a larger extent across prosodic word boundaries than across phrase boundaries. Assimilatory devoicing makes the fricatives more similar to their tense counterparts and could thus hinder word recognition. The present study investigates how listeners cope with assimilatory devoicing. Results of a cross-modal priming experiment indicate that listeners compensate for assimilation in appropriate contexts. Prosodic structure moderates compensation for assimilation: Compensation occurs especially after phrase boundaries, where devoiced fricatives are sufficiently long to be confused with their tense counterparts.

Abstract

Prosodic structure has long been known to constrain phonological processes [1]. More recently, it has also been recognized as a source of fine-grained phonetic variation of speech sounds. In particular, segments in domain-initial position undergo prosodic strengthening [2, 3], which also implies more resistance to coarticulation in higher prosodic domains [5]. The present study investigates the combined effects of prosodic strengthening and assimilatory devoicing on word-initial fricatives in German, the functional implication of both processes for cues to the fortis-lenis contrast, and the influence of prosodic structure on listeners’ compensation for assimilation. Results indicate that 1. Prosodic structure modulates duration and the degree of assimilatory devoicing, 2. Phonological contrasts are maintained by speakers, but differ in phonetic detail across prosodic domains, and 3. Compensation for assimilation in perception is moderated by prosodic structure and lexical constraints.

Abstract

A recurrently connected attractor neural network with a Hebbian learning rule is currently our best ANN analogy for a piece cortex. Functionally biological memory operates on a spectrum of time scales with regard to induction and retention, and it is modulated in complex ways by sub-cortical neuromodulatory systems. Moreover, biological memory networks are commonly believed to be highly distributed and engage many co-operating cortical areas. Here we focus on the temporal aspects of induction and retention of memory in a connectionist type attractor memory model of a piece of cortex. A continuous time, forgetful Bayesian-Hebbian learning rule is described and compared to the characteristics of LTP and LTD seen experimentally. More generally, an attractor network implementing this learning rule can operate as a long-term, intermediate-term, or short-term memory. Modulation of the print-now signal of the learning rule replicates some experimental memory phenomena, like e.g. the von Restorff effect.

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

Using real-time eye-movement measures, we asked how a fantastical discourse context competes with stored representations of semantic and world knowledge to influence children's and adults' moment-by-moment interpretation of a story. Seven-year- olds were less effective at bypassing stored semantic and world knowledge during real-time interpretation than adults. Nevertheless, an effect of discourse context on comprehension was still apparent.

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

Previous research has shown that repeated interactions can cause iconicity in signals to reduce. However, data from several recent studies has shown the opposite trend: an increase in iconicity as the result of repeated interactions. Here, we discuss whether signals may become less or more iconic as a result of the modality used to produce them. We review several recent experimental results before presenting new data from multi-modal signals, where visual input creates audio feedback. Our results show that the growth in iconicity present in the audio information may come at a cost to iconicity in the visual information. Our results have implications for how we think about and measure iconicity in artificial signalling experiments. Further, we discuss how iconicity in real world speech may stem from auditory, kinetic or visual information, but iconicity in these different modalities may conflict.

Abstract

Verb learning is important for young children. While most
previous research has focused on linguistic and conceptual
challenges in early verb learning (e.g. Gentner, 1982, 2006),
the present paper examined early verb learning at the
attentional level and quantified the input for early verb learning
by measuring verb-action co-occurrence statistics in parent-
child interaction from the learner’s perspective. To do so, we
used head-mounted eye tracking to record fine-grained
multimodal behaviors during parent-infant joint play, and
analyzed parent speech, parent and infant action, and infant
attention at the moments when parents produced verb labels.
Our results show great variability across different action verbs,
in terms of frequency of verb utterances, frequency of
corresponding actions related to verb meanings, and infants’
attention to verbs and actions, which provide new insights on
why some verbs are harder to learn than others.

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

Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to its strong capabilities to encode word content, CBOW embeddings perform well on a wide range of downstream tasks while being efficient to compute. However, CBOW is not capable of capturing the word order. The reason is that the computation of CBOW's word embeddings is commutative, i.e., embeddings of XYZ and ZYX are the same. In order to address this shortcoming, we propose a
learning algorithm for the Continuous Matrix Space Model, which we call Continual Multiplication of Words (CMOW). Our algorithm is an adaptation of word2vec, so that it can be trained on large quantities of unlabeled text. We empirically show that CMOW better captures linguistic properties, but it is inferior to CBOW in memorizing word content. Motivated by these findings, we propose a hybrid model that combines the strengths of CBOW and CMOW. Our results show that the hybrid CBOW-CMOW-model retains CBOW's strong ability to memorize word content while at the same time substantially improving its ability to encode other linguistic information by 8%. As a result, the hybrid also performs better on 8 out of 11 supervised downstream tasks with an average improvement of 1.2%.

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

The human body is unique: it is both an object of perception and the source of human experience. Its universality makes it a perfect resource for asking questions about how cognition, brain and typology relate to one another. For example, we can ask how speakers of different languages segment and categorize the human body. A dominant view is that body parts are “given” by visual perceptual discontinuities, and that words are merely labels for these visually determined parts (e.g., Andersen, 1978; Brown, 1976; Lakoff, 1987). However, there are problems with this view. First it ignores other perceptual information, such as somatosensory and motoric representations. By looking at the neural representations of sesnsory representations, we can test how much of the categorization of the human body can be done through perception alone. Second, we can look at language typology to see how much universality and variation there is in body-part categories. A comparison of a range of typologically, genetically and areally diverse languages shows that the perceptual view has only limited applicability (Majid, Enfield & van Staden, in press). For example, using a “coloring-in” task, where speakers of seven different languages were given a line drawing of a human body and asked to color in various body parts, Majid & van Staden (in prep) show that languages vary substantially in body part segmentation. For example, Jahai (Mon-Khmer) makes a lexical distinction between upper arm, lower arm, and hand, but Lavukaleve (Papuan Isolate) has just one word to refer to arm, hand, and leg. This shows that body part categorization is not a straightforward mapping of words to visually determined perceptual parts.

Abstract

Many studies in cognitive science address how people categorize objects, but there has been comparatively little research on event categorization. This study investigated the categorization of events involving material destruction, such as “cutting” and “breaking”. Speakers of 28 typologically, genetically, and areally diverse languages described events shown in a set of video-clips. There was considerable cross-linguistic agreement in the dimensions along which the events were distinguished, but there was variation in the number of categories and the placement of their boundaries.

Abstract

Studies have claimed that blind people’s spatial representations are different from sighted people, and blind people display superior auditory processing. Due to the nature of auditory and haptic information, it has been proposed that blind people have spatial representations that are more sequential than sighted people. Even the temporary loss of sight—such as through blindfolding—can affect spatial representations, but not much research has been done on this topic. We compared blindfolded and sighted people’s linguistic spatial expressions and non-linguistic localization accuracy to test how blindfolding affects the representation of path in auditory motion events. We found that blindfolded people were as good as sighted people when localizing simple sounds, but they outperformed sighted people when localizing auditory motion events. Blindfolded people’s path related speech also included more sequential, and less holistic elements. Our results indicate that even temporary loss of sight influences spatial representations of auditory motion events

Abstract

Lombard speech, speech produced in noise, is acoustically different from speech produced in quiet (plain speech) in several ways, including having a higher and wider F0 range (pitch). Extensive research on native Lombard speech does not consider that non-natives experience a higher cognitive load while producing
speech and that the native language may influence the non-native speech. We investigated pitch range in plain and Lombard speech in native and non-natives.
Dutch and American-English speakers read contrastive question-answer pairs in quiet and in noise in English, while the Dutch also read Dutch sentence pairs. We found that Lombard speech is characterized by a wider pitch range than plain speech, for all speakers (native English, non-native English, and native Dutch).
This shows that non-natives also widen their pitch range in Lombard speech. In sentences with early-focus, we see the same increase in pitch range when going from plain to Lombard speech in native and non-native English, but a smaller increase in native Dutch. In sentences with late-focus, we see the biggest increase for the native English, followed by non-native English and then native Dutch. Together these results indicate an effect of the native language on non-native Lombard speech.

Abstract

Lombard speech, speech produced in noise, is
typically produced with a higher fundamental
frequency (F0, pitch) compared to speech in quiet. This paper examined the potential differences in native and non-native Lombard speech by analyzing median pitch in sentences with early- or late-focus produced in quiet and noise. We found an increase in pitch in late-focus sentences in noise for Dutch speakers in both English and Dutch, and for American-English speakers in English. These results
show that non-native speakers produce Lombard speech, despite their higher cognitive load. For the early-focus sentences, we found a difference between the Dutch and the American-English speakers. Whereas the Dutch showed an increased F0 in noise
in English and Dutch, the American-English speakers did not in English. Together, these results suggest that some acoustic characteristics of Lombard speech, such as pitch, may be language-specific, potentially
resulting in the native language influencing the non-native Lombard speech.

Abstract

Speech rate is known to modulate perception of temporally ambiguous speech sounds. For instance, a vowel may be perceived as short when the immediate speech context is slow, but as long when the context is fast. Yet, effects of long-term tracking of speech rate are largely unexplored. Two experiments tested whether long-term tracking of rate influences perception of the temporal Dutch vowel contrast /ɑ/-/a:/. In Experiment 1, one low-rate group listened to 'neutral' rate speech from talker A and to slow speech from talker B. Another high-rate group was exposed to the same neutral speech from A, but to fast speech from B. Between-group comparison of the 'neutral' trials revealed that the low-rate group reported a higher proportion of /a:/ in A's 'neutral' speech, indicating that A sounded faster when B was slow. Experiment 2 tested whether one's own speech rate also contributes to effects of long-term tracking of rate. Here, talker B's speech was replaced by playback of participants' own fast or slow speech. No evidence was found that one's own voice affected perception of talker A in larger speech contexts. These results carry implications for our understanding of the mechanisms involved in rate-dependent speech perception and of dialogue.

Abstract

The results of a word-spotting experiment are presented in which Dutch listeners tried to spot different types of bisyllabic Dutch words embedded in different types of nonsense contexts. Embedded verbs were not reliably harder to spot than embedded nouns; this suggests that nouns and verbs are recognised via the same basic processes. Iambic words were no harder to spot than trochaic words, suggesting that trochaic words are not in principle easier to recognise than iambic words. Words were harder to spot in consonantal contexts (i.e., contexts which themselves could not be words) than in longer contexts which contained at least one vowel (i.e., contexts which, though not words, were possible words of Dutch). A control experiment showed that this difference was not due to acoustic differences between the words in each context. The results support the claim that spoken-word recognition is sensitive to the viability of sound sequences as possible words.

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

This paper presents an analysis of semantic association norms for German nouns. In contrast to prior studies, we not only collected associations elicited by written representations of target objects but also by their pictorial representations. In a first analysis, we identified systematic differences in the type and distribution of associate responses for the two presentation forms. In a second analysis, we applied a soft cluster analysis to the collected target-response pairs. We subsequently used the clustering to predict noun ambiguity and to discriminate senses in our target nouns.

Abstract

Humans learn language by interaction with their environment and listening to other humans. It should also be possible for computational models to learn language directly from speech but so far most approaches require text. We improve on existing neural network approaches to create visually grounded embeddings for spoken utterances. Using a combination of a multi-layer GRU, importance sampling, cyclic learning rates, ensembling and vectorial self-attention our results show a remarkable increase in image-caption retrieval performance over previous work. Furthermore, we investigate which layers in the model learn to recognise words in the input. We find that deeper network layers are better at encoding word presence, although the final layer has slightly lower performance. This shows that our visually grounded sentence encoder learns to recognise words from the input even though it is not explicitly trained for word recognition.

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

The anterior lordosis of the cervical spine is thought
to contribute to pitch (fo) production by influencing
cricoid rotation as a function of larynx height. This
study examines the matter of inter-individual
variation in cervical spine shape and whether this has
an influence on how fo is produced along increasing
or decreasing scales, using the ArtiVarK dataset,
which contains real-time MRI pitch production data.
We find that the cervical spine actively participates in
fo production, but the amount of displacement
depends on individual shape. In general, anterior
spine motion (tending toward cervical lordosis)
occurs for low fo, while posterior movement (tending
towards cervical kyphosis) occurs for high fo.

Abstract

Learning how words refer to aspects of the environment is a complex task, but one that is supported by numerous cues within the environment which constrain the possibilities for matching words to their intended referents. In this paper we tested the predictions of a computational model of multiple cue integration for word learning, that predicted variation in the presence of cues provides an optimal learning situation. In a cross-situational learning task with adult participants, we varied the reliability of presence of distributional, prosodic, and gestural cues. We found that the best learning occurred when cues were often present, but not always. The effect of variability increased the salience of individual cues for the learner, but resulted in robust learning that was not vulnerable to individual cues’ presence or absence. Thus, variability of multiple cues in the language-learning environment provided the optimal circumstances for word learning.

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

In order to study the cognitive processes underlying speech comprehension, neuro-physiological measures (e.g., EEG and MEG), or behavioural measures (e.g., reaction times and response accuracy) can be applied. Compared to behavioural measures, EEG signals can provide a more fine-grained and complementary view of the processes that take place during the unfolding of an auditory stimulus.

EEG signals are often analysed after having chosen specific time windows, which are usually based on the temporal structure of ERP components expected to be sensitive to the experimental manipulation. However, as the timing of ERP components may vary between experiments, trials, and participants, such a-priori defined analysis time windows may significantly hamper the exploratory power of the analysis of components of interest. In this paper, we explore a wide-window analysis method applied to EEG signals collected in an auditory repetition priming experiment.

This approach is based on a bank of temporal filters arranged along the time axis in combination with linear mixed effects modelling. Crucially, it permits a temporal decomposition of effects in a single comprehensive statistical model which captures the entire EEG trace.

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).

Abstract

Sign languages stand out in that there is high prevalence of
conventionalised linguistic forms that map directly to their
referent (i.e., iconic). Hearing adults show low performance
when asked to guess the meaning of iconic signs suggesting
that their iconic features are largely inaccessible to them.
However, it has not been investigated whether speakers’
gestures, which also share the property of iconicity, may
assist non-signers in guessing the meaning of signs. Results
from a pantomime generation task (Study 1) show that
speakers’ gestures exhibit a high degree of systematicity, and
share different degrees of form overlap with signs (full,
partial, and no overlap). Study 2 shows that signs with full
and partial overlap are more accurately guessed and are
assigned higher iconicity ratings than signs with no overlap.
Deaf and hearing adults converge in their iconic depictions
for some concepts due to the shared conceptual knowledge
and manual-visual modality.

Abstract

A database is presented of relative familiarity ratings for 24 sets of Japanese words, each set comprising words overlapping in the initial portions. These ratings are useful for the generation of material sets for research in the recognition of spoken words.

Abstract

In this paper we try to contribute to the body of knowledge about the acquisition of English epistemic modal verbs (e.g. Mary may/has to be at school). Semantically, these verbs encode possibility or necessity with respect to available evidence. Pragmatically, the use of epistemic modals often gives rise to scalar conversational inferences (Mary may be at school -> Mary doesn’t have to be at school). The acquisition of epistemic modals is challenging for children on both these levels. In this paper, we present findings from two studies which were conducted with 5-year-old children and adults. Our findings, unlike previous work, show that 5-yr-olds have mastered epistemic modal semantics, including the notions of necessity and possibility. However, they are still in the process of acquiring epistemic modal pragmatics.

Abstract

-* indicates shared first authorship -
The present study investigated the effects of selective attention on the processing of morphosyntactic errors in unattended parts of speech. Two groups of German native (L1) speakers participated in the present study. Participants listened to sentences in which irregular verbs were manipulated in three different conditions (correct, incorrect but attested ablaut pattern, incorrect and crosslinguistically unattested ablaut pattern). In order to track fast dynamic neural reactions to the stimuli, electroencephalography was used. After each sentence, participants in Experiment 1 performed a semantic judgement task, which deliberately distracted the participants from the syntactic manipulations and directed their attention to the semantic content of the sentence. In Experiment 2, participants carried out a syntactic judgement task, which put their attention on the critical stimuli. The use of two different attentional tasks allowed for investigating the impact of selective attention on speech processing and whether morphosyntactic processing steps are performed automatically. In Experiment 2, the incorrect attested condition elicited a larger N400 component compared to the correct condition, whereas in Experiment 1 no differences between conditions were found. These results suggest that the processing of morphosyntactic violations in irregular verbs is not entirely automatic but seems to be strongly affected by selective attention.

Abstract

En este artículo se desarrolla un método automático de segmentación de fonemas no supervisado. Este método utiliza el algoritmo de agrupación de máximo margen [1] para realizar segmentación de fonemas sobre habla continua sin necesidad de información a priori para el entrenamiento del sistema.

Abstract

This paper examines the use of iconic words in early conversations between children and caregivers. The longitudinal data include a span of six observations of 35 children-parent dyads in the same semi-structured activity. Our findings show that children’s speech initially has a high proportion of iconic words, and over time, these words become diluted by an increase of arbitrary words. Parents’ speech is also initially high in iconic words, with a decrease in the proportion of iconic words over time – in this case driven by the use of fewer iconic words. The level and development of iconicity are related to individual differences in the children’s cognitive skills. Our findings fit with the hypothesis that iconicity facilitates early word learning and may play an important role in learning to produce new words.

Abstract

This study investigates whether the acoustic durations of derivational affixes in Dutch are affected by the frequency of the word they occur in. In a word naming experiment, subjects were presented with a large number of words containing one of the affixes ge-, ver-, ont, or -lijk. Their responses were recorded on DAT tapes, and the durations of the affixes were measured using Automatic Speech Recognition technology. To investigate whether frequency also affected durations when speech rate was high, the presentation rate of the stimuli was varied. The results show that a higher frequency of the word as a whole led to shorter acoustic realizations for all affixes. Furthermore, affixes became shorter as the presentation rate of the stimuli increased. There was no interaction between word frequency and presentation rate, suggesting that the frequency effect also applies in situations in which the speed of articulation is very high.