Publications

Abstract

This chapter sketches in very general terms the cognitive architecture of both language comprehension and production, as well as the neurobiological infrastructure that makes the human brain ready for language. Focus is on spoken language, since that compares most directly to processing music. It is worth bearing in mind that humans can also interface with language as a cognitive system using sign and text (visual) as well as Braille (tactile); that is to say, the system can connect with input/output processes in any sensory modality. Language processing consists of a complex and nested set of subroutines to get from sound to meaning (in comprehension) or meaning to sound (in production), with remarkable speed and accuracy. The fi rst section outlines a selection of the major constituent operations, from fractionating the input into manageable units to combining and unifying information in the construction of meaning. The next section addresses the neurobiological infrastructure hypothesized to form the basis for language processing. Principal insights are summarized by building on the notion of “brain networks” for speech–sound processing, syntactic processing, and the construction of meaning, bearing in mind that such a neat three-way subdivision overlooks important overlap and shared mechanisms in the neural architecture subserving language processing. Finally, in keeping with the spirit of the volume, some possible relations are highlighted between language and music that arise from the infrastructure developed here. Our characterization of language and its neurobiological foundations is necessarily selective and brief. Our aim is to identify for the reader critical questions that require an answer to have a plausible cognitive neuroscience of language processing.

Abstract

This chapter contains sections titled: Introduction Questions Questioning and the Epistemic Gradient Presuppositions, Agenda Setting and Preferences Social Actions Implemented by Questions Questions as Building Blocks of Institutional Activities Future Directions

Abstract

What kinds of resources to languages have for describing location and position? For some languages, verbs have an important role to play in describing different kinds of situations (e.g., whether a bottle is standing or lying on the table). This task is designed to examine the use of positional verbs in locative constructions, with respect to the presence or absence of a human “positioner”. Participants are asked to describe video clips showing locative states that occur spontaneously, or because of active interference from a person. The task follows on from two earlier tools for the elicitation of static locative descriptions (BowPed and the Ameka picture book task). A number of additional variables (e.g. canonical v. non-canonical orientation of the figure) are also targeted in the stimuli set.

Abstract

Conversation Analysis (CA) is an inductive, micro-analytic, and predominantly qualitative
method for studying human social interactions. This chapter describes and illustrates the basic
methods of CA. We first situate the method by describing its sociological foundations, key areas
of analysis, and particular approach in using naturally occurring data. The bulk of the chapter is
devoted to practical explanations of the typical conversation analytic process for collecting data
and producing an analysis. We analyze a candidate interactional practice – the assessmentimplicative
interrogative – using real data extracts as a demonstration of the method, explicitly
laying out the relevant questions and considerations for every stage of an analysis. The chapter
concludes with some discussion of quantitative approaches to conversational interaction, and
links between CA and psycholinguistic concerns

Abstract

In human face-to-face communication, language comprehension is a multi-modal, situated activity. However, little is known about how we combine information from these different modalities, and how perceived communicative intentions, often signaled through visual signals, such as eye
gaze, may influence this processing. We address this question by simulating a triadic communication context in which a
speaker alternated her gaze between two different recipients. Participants thus viewed speech-only or speech+gesture
object-related utterances when being addressed (direct gaze) or unaddressed (averted gaze). Two object images followed
each message and participants’ task was to choose the object that matched the message. Unaddressed recipients responded significantly slower than addressees for speech-only
utterances. However, perceiving the same speech accompanied by gestures sped them up to a level identical to
that of addressees. That is, when speech processing suffers due to not being addressed, gesture processing remains intact and enhances the comprehension of a speaker’s message

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

This chapter deals with the question of whether there is one syntactic system that is shared by language production and comprehension or whether there are two separate systems. It first discusses arguments in favor of one or the other option and then presents the current evidence on the brain structures involved in sentence processing. The results of meta-analyses of numerous neuroimaging studies suggest that there is one system consisting of functionally distinct cortical regions: the dorsal part of Broca’s area subserving compositional syntactic processing; the ventral part of Broca’s area subserving compositional semantic processing; and the left posterior temporal cortex (Wernicke’s area) subserving the retrieval of lexical syntactic and semantic information. Sentence production, the comprehension of simple and complex sentences, and the parsing of sentences containing grammatical violations differ with respect to the recruitment of these functional components.

Abstract

Models of cognitive processes often include simplifications, idealisations, and fictionalisations, so how should we learn about cognitive processes from such models? Particularly in cognitive science, when many features of the target system are unknown, it is not always clear which simplifications, idealisations, and so on, are appropriate for a research question, and which are highly misleading. Here we use a case-study from studies of language evolution, and ideas from philosophy of science, to illustrate a robustness approach to learning from models. Robust properties are those that arise across a range of models, simulations and experiments, and can be used to identify key causal structures in the models, and the phenomenon, under investigation. For example, in studies of language evolution, the emergence of compositional structure is a robust property across models, simulations and experiments of cultural transmission, but only under pressures for learnability and expressivity. This arguably illustrates the principles underlying real cases of language evolution. We provide an outline of the robustness approach, including its limitations, and suggest that this methodology can be productively used throughout cognitive science. Perhaps of most importance, it suggests that different modelling frameworks should be used as tools to identify the abstract properties of a system, rather than being definitive expressions of theories.

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

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

Second language (L2) research often involves analyses of acoustic measures of fluency. The studies investigating fluency, however, have been difficult to compare because the measures of fluency that were used differed widely. One of the differences between studies concerns the lower cut-off point for silent pauses, which has been set anywhere between 100 ms and 1000 ms. The goal of this paper is to find an optimal cut-off point. We calculate acoustic measures of fluency using different pause thresholds and then relate these measures to a measure of L2 proficiency and to ratings on fluency.

Abstract

Anthropology began by comparing aspects of kinship across cultures, while linguists interested in semantic domains such as kinship necessarily compare across languages. In this chapter I show how phylogenetic comparative methods from evolutionary biology can be used to study evolutionary processes relating to kinship and kinship terminologies across language and culture.

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

Why do languages have the categories they do? It has been argued that spatial terms in the world’s languages reflect categories that support highly informative communication, and that this accounts for the spatial categories found across languages. However, this proposal has been tested against only nine languages, and in a limited fashion. Here, we consider two new languages: Maijɨki, an under-documented language of Peruvian Amazonia, and English. We analyze spatial data from these two new languages and the original nine, using thorough and theoretically targeted computational tests. The results support the hypothesis that spatial terms across dissimilar languages enable near-optimally informative communication, over an influential competing hypothesis

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

Gesture is an integral part of face-to-face communication, and provides a rich area for cross-cultural comparison. “Locally-anchored spatial gestures” are gestures that are roughly oriented to the actual geographical direction of referents. For example, such gestures may point to a location or a thing, trace the shape of a path, or indicate the direction of a particular area. The goal of this task is to elicit locally-anchored spatial gestures across different cultures. The task follows an interview format, where one participant prompts another to talk in detail about a specific area that the main speaker knows well. The data can be used for additional purposes such as the investigation of demonstratives.

Abstract

It is usually assumed that the language spoken by a human community is independent of the community's genetic makeup, an assumption supported by an overwhelming amount of evidence. However, the possibility that language is influenced by its speakers' genes cannot be ruled out a priori, and a recently discovered correlation between the geographic distribution of tone languages and two human genes seems to point to a genetically influenced bias affecting language. This entry describes this specific correlation and highlights its major implications. Voice pitch has a variety of communicative functions. Some of these are probably universal, such as conveying information about the speaker's sex, age, and emotional state. In many languages, including the European languages, voice pitch also conveys certain sentence-level meanings such as signaling that an utterance is a question or an exclamation; these uses of pitch are known as intonation. Some languages, however, known as tone languages, nian ...

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

Research in body language with use of gesture recognition and speech analysis has gained much attention in the recent times, influencing disciplines related to image and speech processing.

This study aims to design the Media Query Language (MQL) (Lenkiewicz, et al. 2012) combined with the Linguistic Media Query Interface (LMQI) for Elan (Wittenburg, et al. 2006). The system integrated with the new achievements in audio-video recognition will allow querying media files with predefined gesture phases (or motion primitives) and speech characteristics as well as combinations of both. For the purpose of this work the predefined motions and speech characteristics are called patterns for atomic elements and actions for a sequence of patterns. The main assumption is that a user-customized library of patterns and actions and automated media annotation with LMQI will reduce annotation time, hence decreasing costs of creation of annotated corpora. Increase of the number of annotated data should influence the speed and number of possible research in disciplines in which human multimodal interaction is a subject of interest and where annotated corpora are required.

Abstract

How do languages express ideas of movement, and how do they package different components of this domain, such as manner and path of motion? This task uses one large set of stimuli to gain knowledge of certain key aspects of motion verb meanings in the target language, and expands the investigation beyond simple verbs (e.g., go) to include the semantics of motion predications complete with adjuncts (e.g., go across something). Consultants are asked to view and briefly describe 96 animations of a few seconds each. The task is designed to get linguistic elicitations of motion predications under contrastive comparison with other animations in the same set. Unlike earlier tasks, the stimuli focus on inanimate moving items or “figures” (in this case, a ball).

Abstract

Since the core matrix for language use is interaction, the main job of language
is not to express propositions or abstract meanings, but to deliver actions.
For in order to respond in interaction we have to ascribe to the prior turn
a primary ‘action’ – variously thought of as an ‘illocution’, ‘speech act’, ‘move’,
etc. – to which we then respond. The analysis of interaction also relies heavily
on attributing actions to turns, so that, e.g., sequences can be characterized in
terms of actions and responses. Yet the process of action ascription remains way
understudied. We don’t know much about how it is done, when it is done, nor even
what kind of inventory of possible actions might exist, or the degree to which they
are culturally variable.
The study of action ascription remains perhaps the primary unfulfilled task in
the study of language use, and it needs to be tackled from conversationanalytic,
psycholinguistic, cross-linguistic and anthropological perspectives.
In this talk I try to take stock of what we know, and derive a set of goals for and
constraints on an adequate theory. Such a theory is likely to employ, I will suggest,
a top-down plus bottom-up account of action perception, and a multi-level notion
of action which may resolve some of the puzzles that have repeatedly arisen.

Abstract

Given the diversity of languages, it is unlikely that the human capacity for language resides in rich universal syntactic machinery. More likely, it resides centrally in the capacity for vocal learning combined with a distinctive ethology for communicative interaction, which together (no doubt with other capacities) make diverse languages learnable. This chapter focuses on face-to-face communication, which is characterized by the mapping of sounds and multimodal signals onto speech acts and which can be deeply recursively embedded in interaction structure, suggesting an interactive origin for complex syntax. These actions are recognized through Gricean intention recognition, which is a kind of “ mirroring” or simulation distinct from the classic mirror neuron system. The multimodality of conversational interaction makes evident the involvement of body, hand, and mouth, where the burden on these can be shifted, as in the use of speech and gesture, or hands and face in sign languages. Such shifts having taken place during the course of human evolution. All this suggests a slightly different approach to the mystery of music, whose origins should also be sought in joint action, albeit with a shift from turn-taking to simultaneous expression, and with an affective quality that may tap ancient sources residual in primate vocalization. The deep connection of language to music can best be seen in the only universal form of music, namely song.

Abstract

Demonstratives (e.g., words such as this and that in English) pivot on relationships between the item being talked about, and features of the speech act situation (e.g., where the speaker and addressee are standing or looking). However, they are only rarely investigated multi-modally, in natural language contexts. This task is designed to build a video corpus of cross-linguistically comparable discourse data for the study of “deixis in action”, while simultaneously supporting the investigation of joint attention as a factor in speaker selection of demonstratives. In the task, two or more speakers are asked to discuss and evaluate a group of similar items (e.g., examples of local handicrafts, tools, produce) that are placed within a relatively defined space (e.g., on a table). The task can additionally provide material for comparison of pointing gesture practices.

Abstract

This entry contains: 1. An invitation to think about to what extent the grammar of space and time share lexical and morphosyntactic resources − the suggestions here are only prompts, since it would take a long questionnaire to fully explore this; 2. A suggestion about how to collect gestural data that might show us to what extent the spatial and temporal domains, have a psychological continuity. This is really the goal − but you need to do the linguistic work first or in addition. The goal of this task is to explore the extent to which time is conceptualised on a spatial basis.

Abstract

The ECHO proposal aims to bring to life the cultural heritage of Europe, through internet technology that encourages collaboration across the Humanities disciplines which interpret it – at the same time making all this scholarship accessible to the citizens of Europe. An essential part of the cultural heritage of Europe is the diverse set of languages used on the continent, in their historical, literary and spoken forms. Amongst these are the ‘hidden languages’ used by minorities but of wide interest to the general public. We take the 18 Sign Languages of the EEC – the natural languages of the deaf - as an example. Little comparative information about these is available, despite their special scientific importance, the widespread public interest and the policy implications. We propose a research project on these languages based on placing fully annotated digitized moving images of each of these languages on the internet. This requires significant development of multi-media technology which would allow distributed annotation of a central corpus, together with the development of special search techniques. The technology would have widespread application to all cultural performances recorded as sound plus moving images. Such a project captures in microcosm the essence of the ECHO proposal: cultural heritage is nothing without the humanities research which contextualizes and gives it comparative assessment; by marrying information technology to humanities research, we can bring these materials to a wider public while simultaneously boosting Europe as a research area.

Abstract

As for 'Locally-anchored spatial gestures task, version 2', a major goal of this task is to elicit locally-anchored spatial gestures across different cultures. “Locally-anchored spatial gestures” are gestures that are roughly oriented to the actual geographical direction of referents. Rather than set up an interview situation, this task involves recording informal, animated narrative delivered to a native-speaker interlocutor. Locally-anchored gestures produced in such narrative are roughly comparable to those collected in the interview task. The data collected can also be used to investigate a wide range of other topics.

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

Since the cognitive revolution, a widely held assumption has been that—whereas content may vary across cultures—cognitive processes would be universal, especially those on the more basic levels. Even if scholars do not fully subscribe to this assumption, they often conceptualize, or tend to investigate, cognition as if it were universal (Henrich, Heine, & Norenzayan, 2010). The insight that universality must not be presupposed but scrutinized is now gaining ground, and cognitive diversity has become one of the hot (and controversial) topics in the field (Norenzayan & Heine, 2005). We argue that, for scrutinizing the cultural dimension of cognition, taking an anthropological perspective is invaluable, not only for the task itself, but for attenuating the home-field disadvantages that are inescapably linked to cross-cultural research (Medin, Bennis, & Chandler, 2010).

Abstract

The results of a study on perceptual learning are reported. Dutch subjects made lexical decisions on a list of words and nonwords. Embedded in the list were either [f]- or [s]-final words in which the final fricative had been replaced by an ambiguous sound, midway between [f] and [s]. One group of listeners heard ambiguous [f]- final Dutch words like [kara?] (based on karaf, carafe) and unambiguous [s]-final words (e.g., karkas, carcase). A second group heard the reverse (e.g., ambiguous [karka?] and unambiguous karaf). After this training phase, listeners labelled ambiguous fricatives on an [f]- [s] continuum. The subjects who had heard [?] in [f]- final words categorised these fricatives as [f] reliably more often than those who had heard [?] in [s]-final words. These results suggest that speech recognition is dynamic: the system adjusts to the constraints of each particular listening situation. The lexicon can provide this adjustment process with a training signal.

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

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.