Stormy Seas and Cloudy Skies: Conceptual Processing is (Still) Linguistic and Perceptual

Pecher et al. (2010) extended current findings on conceptual processing of words denoting references in the sky or in the ocean (e.g., falcon, dolphin) presented in different positions on the screen (top, bottom). This experimental paradigm has shown to facilitate processing for word meaning in the corresponding spatial location (e.g., falcon presented at the top of the screen). Pecher et al. (2010) compared two explanations for the interaction between concept and position, one related to perceptual simulation, the other to a response selection process, in a response time experiments. To test the perceptual simulation explanation participant

Grammar-Based and Lexicon-Based Techniques to Extract Personality Traits from Text

Language provides an important source of information to pre-dict human personality. However, most studies that have pre-dicted personality traits using computational linguistic meth-ods have focused on lexicon-based information. We investigateto what extent the performance of lexicon-based and grammar-based methods compare when predicting personality traits. Weanalyzed a corpus of student essays and their personality traitsusing two lexicon-based approaches, one top-down (Linguis-tic Inquiry and Word Count (LIWC)), one bottom-up (topicmodels) and one grammar-driven approach (Biber model), aswell as combinations of these models. Results showed thatthe performance of the models and their combinations demon-strated similar performance, showing that lexicon-based top-down models and bottom-up models do not differ, and neitherdo lexicon-based models and grammar-based models. More-over, combination of models did not improve performance.These findings suggest that predicting personality traits fromtext remains difficult, but that the performance from lexicon-based and grammar-based models are on par

Zipf’s law revisited: Spoken dialog, linguistic units, parameters, and the principle of least effort

The ubiquitous inverse relationship between word frequency and word rank is commonly known as Zipf’s law. The theoretical underpinning of this law states that the inverse relationship yields decreased effort in both the speaker and hearer, the so-called principle of least effort. Most research has focused on showing an inverse relationship only for written monolog, only for frequencies and ranks of one linguistic unit, generally word unigrams, with strong correlations of the power law to the observed frequency distributions, with limited to no attention to psychological mechanisms such as the principle of least effort. The current paper extends the existing findings, by not focusing on written monolog but on a more fundamental form of communication, spoken dialog, by not only investigating word unigrams but also units quantified on syntactic, pragmatic, utterance, and nonverbal communicative levels by showing that the adequacy of Zipf’s formula seems ubiquitous, but the exponent of the power law curve is not, and by placing these findings in the context of Zipf’s principle of least effort through redefining effort in terms of cognitive resources available for communication. Our findings show that Zipf’s law also applies to a more natural form of communication—that of spoken dialog, that it applies to a range of linguistic units beyond word unigrams, that the general good fit of Zipf’s law needs to be revisited in light of the parameters of the formula, and that the principle of least effort is a useful theoretical framework for the findings of Zipf’s law

Surface and Contextual Linguistic Cues in Dialog Act Classification: A Cognitive Science View

What role do linguistic cues on a surface and contextual level have in identifying the intention behind an utterance? Drawing on the wealth of studies and corpora from the computational task of dialog act classification, we studied this question from a cognitive science perspective. We first reviewed the role of linguistic cues in dialog act classification studies that evaluated model performance on three of the most commonly used English dialog act corpora. Findings show that frequency‐based, machine learning, and deep learning methods all yield similar performance. Classification accuracies, moreover, generally do not explain which specific cues yield high performance. Using a cognitive science approach, in two analyses, we systematically investigated the role of cues in the surface structure of the utterance and cues of the surrounding context individually and combined. By comparing the explained variance, rather than the prediction accuracy of these cues in a logistic regression model, we found that (1) while surface and contextual linguistic cues can complement each other, surface linguistic cues form the backbone in human dialog act identification, (2) with word frequency statistics being particularly important for the dialog act, and (3) the similar trends across corpora, despite differences in the type of dialog, corpus setup, and dialog act tagset. The importance of surface linguistic cues in dialog act classification sheds light on how both computers and humans take advantage of these cues in speech act recognition

Lingualyzer: A computational linguistic tool for multilingual and multidimensional text analysis

Most natural language models and tools are restricted to one language, typically English. For researchers in the behavioral sciences investigating languages other than English, and for those researchers who would like to make cross-linguistic comparisons, hardly any computational linguistic tools exist, particularly none for those researchers who lack deep computational linguistic knowledge or programming skills. Yet, for interdisciplinary researchers in a variety of fields, ranging from psycholinguistics, social psychology, cognitive psychology, education, to literary studies, there certainly is a need for such a cross-linguistic tool. In the current paper, we present Lingualyzer (https://lingualyzer.com), an easily accessible tool that analyzes text at three different text levels (sentence, paragraph, document), which includes 351 multidimensional linguistic measures that are available in 41 different languages. This paper gives an overview of Lingualyzer, categorizes its hundreds of measures, demonstrates how it distinguishes itself from other text quantification tools, explains how it can be used, and provides validations. Lingualyzer is freely accessible for scientific purposes using an intuitive and easy-to-use interface

Burstiness across multimodal human interaction reveals differences betweenverbal and non-verbal communication

Recent studies of naturalistic face-to-face communication havedemonstrated temporal coordination patterns such as thesynchronization of verbal and non-verbal behavior, which providesevidence for the proposal that verbal and non-verbalcommunicative control derives from one system. In this study, weargue that the observed relationship between verbal and non-verbalbehaviors depends on the level of analysis. In a re-analysis of acorpus of naturalistic multimodal communication (Louwerse et al.,2012), we focus on measuring the temporal patterns of specificcommunicative behaviors in terms of their burstiness. Weexamined burstiness estimates across different roles of the speakerand different communicative channels. We observed moreburstiness for verbal versus non-verbal channels, and for moreversus less informative language sub-channels. These findingsdemonstrate a new method for analyzing temporal patterns incommunicative behaviors, and they suggest a more complexrelationship between verbal and non-verbal channels thansuggested by prior studies

Modality Switch Effects Emerge Early and Increase throughout Conceptual Processing:Evidence from ERPs

We tested whether conceptual processing is modality-specific by tracking the time course of the Conceptual Modality Switch effect. Forty-six participants verified the relation between property words and concept words. The conceptual modality of consecutive trials was manipulated in order to produce an Auditory-to-visual switch condition, a Haptic-to-visual switch condition, and a Visual-to-visual, no-switch condition. Event-Related Potentials (ERPs) were time-locked to the onset of the first word (property) in the target trials so as to measure the effect online and to avoid a within-trial confound. A switch effect was found, characterized by more negative ERP amplitudes for modality switches than no-switches. It proved significant in four typical time windows from 160 to 750 milliseconds post word onset, with greater strength in posterior brain regions, and after 350 milliseconds. These results suggest that conceptual processing may be modality-specific in certain tasks, but also that the early stage of processing is relatively amodal

A realistic, multimodal virtual agent for the healthcare domain

We introduce an interactive embodied conversational agent for deployment in the healthcare sector. The agent is operated by a software architecture that integrates speech recognition, dialog management, and speech synthesis, and is embodied by a virtual human face developed using photogrammetry techniques. These features together allow for real-time, face-to-face interactions with human users. Although the developed software architecture is domain-independent and highly customizable, the virtual agent will initially be applied to healtcare domain. Here we give an overview of the different components of the architecture

Identifying Linguistic Cues that Distinguish Text Types: A Comparison of First and Second Language Speakers

The authors examine the degree to which first (L1) and second language (L2) speakers of English are able to distinguish between simplified or authentic reading texts from L2 instructional books and whether L1 and L2 speakers differ in their ability to process linguistic cues related to this distinction. These human judgments are also compared to computational judgments which are based on indices inspired by cognitive theories of reading processing. Results demonstrate that both L1 and L2 speakers of English are able to identify linguistic cues within both text types, but only L1 speakers are able to successfully distinguish between simplified and authentic texts. In addition, the performance of a computational tool was comparable to that of human performance. These findings have important implications for second language text processing and readability as well as implications for material development for second language instruction