Affective language processing and bilingualism: Complementary perspectives

2017 ◽  
Vol 53 (1) ◽  
pp. 1-16
Author(s):  
Ewa Tomczak ◽  
Dorota Jaworska-Pasterska
Keyword(s):  
Language Processing ◽  
Language Comprehension ◽  
The Other ◽  
Academic Interest ◽  
Current Volume

AbstractRecent years have witnessed revival of academic interest in the study of two areas. One is related to processing of emotional input, both linguistic and nonlinguistic; the other is centred on mechanisms underlying bilingual language comprehension and production. The current volume comprises substantial contributions by researchers working within various fields of linguistics and psychology. The Authors elaborate upon cognitively sophisticated frameworks for conceptualising the complexities of attitudes towards and beliefs about language, i.e.

scholarly journals Does headedness affect processing? A new look at the VO–OV contrast

2009 ◽  
Vol 45 (3) ◽  
pp. 675-710 ◽  
Author(s):  
MIEKO UENO ◽  
MARIA POLINSKY
Keyword(s):  
Language Processing ◽  
Language Comprehension ◽  
Word Order ◽  
The Other ◽  
Differential Processing ◽  
Corpus Studies ◽  
Other Hand ◽  
Compensatory Strategy ◽  
The Relationship ◽  
Affect Processing

This paper examines the relationship between headedness and language processing and considers two strategies that potentially ease language comprehension and production. Both strategies allow a language to minimize the number of arguments in a given clause, either by reducing the number of overtly expressed arguments or by reducing the number of structurally required arguments. The first strategy consists of minimizing the number of overtly expressed arguments by using more pro-drop for two-place predicates (Pro-drop bias). According to the second strategy, a language gives preference to one-place predicates over two-place predicates, thus minimizing the number of structural arguments (Intransitive bias). In order to investigate these strategies, we conducted a series of comparative corpus studies of SVO and SOV languages. Study 1 examined written texts of various genres and children's utterances in English and Japanese, while Study 2 examined narrative stories in English, Spanish, Japanese, and Turkish. The results for these studies showed that pro-drop was uniformly more common with two-place predicates than with one-place predicates, regardless of the OV/VO distinction. Thus the Pro-drop bias emerges as a universal economy principle for making utterances shorter. On the other hand, SOV languages showed a much stronger Intransitive bias than SVO languages. This finding suggests that SOV word order with all the constituents explicitly expressed is potentially harder to process; the dominance of one-place predicates is therefore a compensatory strategy in order to reduce the number of preverbal arguments. The overall pattern of results suggests that human languages utilize both general (Pro-drop bias) and headedness-order-specific (Intransitive bias) strategies to facilitate processing. The results on headedness-order-specific strategies are consistent with other researchers' findings on differential processing in head-final and non-head-final languages, for example, Yamashita & Chang's (2001) ‘long-before-short’ parameterization.

scholarly journals Integration of gaze information during online language comprehension and learning

2019 ◽  
Author(s):  
Kyle Earl MacDonald ◽  
Elizabeth Swanson ◽  
Michael C. Frank
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Language Processing ◽  
Language Comprehension ◽  
Visual Information ◽  
Eye Gaze ◽  
The Other ◽  
Face To Face ◽  
Social Partners ◽  
Processing Mechanisms

Face-to-face communication provides access to visual information that can support language processing. But do listeners automatically seek social information without regard to the language processing task? Here, we present two eye-tracking studies that ask whether listeners’ knowledge of word-object links changes how they actively gather a social cue to reference (eye gaze) during real-time language processing. First, when processing familiar words, children and adults did not delay their gaze shifts to seek a disambiguating gaze cue. When processing novel words, however, children and adults fixated longer on a speaker who provided a gaze cue, which led to an increase in looking to the named object and less looking to the other object in the scene. These results suggest that listeners use their knowledge of object labels when deciding how to allocate visual attention to social partners, which in turn changes the visual input to language processing mechanisms.

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

scholarly journals Listener’s personality traits predict changes in pupil size during auditory language comprehension

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Isabell Hubert Lyall ◽  
Juhani Järvikivi
Keyword(s):  
Personality Traits ◽  
Language Processing ◽  
Language Comprehension ◽  
Pupil Size ◽  
Pupil Dilation ◽  
Systematic Research ◽  
Cognitive Resources ◽  
Personality Profiles ◽  
Pupillary Responses ◽  
Grammatical Errors

AbstractResearch suggests that listeners’ comprehension of spoken language is concurrently affected by linguistic and non-linguistic factors, including individual difference factors. However, there is no systematic research on whether general personality traits affect language processing. We correlated 88 native English-speaking participants’ Big-5 traits with their pupillary responses to spoken sentences that included grammatical errors, "He frequently have burgers for dinner"; semantic anomalies, "Dogs sometimes chase teas"; and statements incongruent with gender stereotyped expectations, such as "I sometimes buy my bras at Hudson's Bay", spoken by a male speaker. Generalized additive mixed models showed that the listener's Openness, Extraversion, Agreeableness, and Neuroticism traits modulated resource allocation to the three different types of unexpected stimuli. No personality trait affected changes in pupil size across the board: less open participants showed greater pupil dilation when processing sentences with grammatical errors; and more introverted listeners showed greater pupil dilation in response to both semantic anomalies and socio-cultural clashes. Our study is the first one demonstrating that personality traits systematically modulate listeners’ online language processing. Our results suggest that individuals with different personality profiles exhibit different patterns of the allocation of cognitive resources during real-time language comprehension.

scholarly journals Machine Learning in Medical Emergencies: a Systematic Review and Analysis

2021 ◽  
Vol 45 (10) ◽  
Author(s):  
Inés Robles Mendo ◽  
Gonçalo Marques ◽  
Isabel de la Torre Díez ◽  
Miguel López-Coronado ◽  
Francisco Martín-Rodríguez
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Language Processing ◽  
Emergency Services ◽  
Clinical Decision ◽  
Quality Of Healthcare ◽  
The Other ◽  
Other Hand ◽  
Increasing Demand ◽  
Commercial Applications

AbstractDespite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.

Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension

2021 ◽  
Vol 30 (6) ◽  
pp. 526-534
Author(s):  
Evelina Fedorenko ◽  
Cory Shain
Keyword(s):  
Language Processing ◽  
Language Comprehension ◽  
Fluid Intelligence ◽  
Linguistic Knowledge ◽  
Domain Specific ◽  
Cognitive Operations ◽  
High Level ◽  
General Circuits ◽  
The Mind ◽  
Language Network

Understanding language requires applying cognitive operations (e.g., memory retrieval, prediction, structure building) that are relevant across many cognitive domains to specialized knowledge structures (e.g., a particular language’s lexicon and syntax). Are these computations carried out by domain-general circuits or by circuits that store domain-specific representations? Recent work has characterized the roles in language comprehension of the language network, which is selective for high-level language processing, and the multiple-demand (MD) network, which has been implicated in executive functions and linked to fluid intelligence and thus is a prime candidate for implementing computations that support information processing across domains. The language network responds robustly to diverse aspects of comprehension, but the MD network shows no sensitivity to linguistic variables. We therefore argue that the MD network does not play a core role in language comprehension and that past findings suggesting the contrary are likely due to methodological artifacts. Although future studies may reveal some aspects of language comprehension that require the MD network, evidence to date suggests that those will not be related to core linguistic processes such as lexical access or composition. The finding that the circuits that store linguistic knowledge carry out computations on those representations aligns with general arguments against the separation of memory and computation in the mind and brain.

scholarly journals Managing Free Text for Secondary Use of Health Data

2014 ◽  
Vol 23 (01) ◽  
pp. 167-169 ◽  
Author(s):  
N. Griffon ◽  
J. Charlet ◽  
S. J. Darmoni ◽  
Keyword(s):  
Language Processing ◽  
Meaningful Use ◽  
Health Data ◽  
Structured Data ◽  
The Other ◽  
Free Text ◽  
Reference Resolution ◽  
Comprehensive Review ◽  
Secondary Use ◽  
Corpus Creation

Summary Objective: To summarize the best papers in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM and natural language processing (NLP) published in 2013. Results: Four articles were selected, one focuses on Electronic Health Record (EHR) interoperability for clinical pathway personalization based on structured data. The other three focus on NLP (corpus creation, de-identification, and co-reference resolution) and highlight the increase in NLP tools performances. Conclusion: NLP tools are close to being seriously concurrent to humans in some annotation tasks. Their use could increase drastically the amount of data usable for meaningful use of EHR.

scholarly journals Emotional aspects of language

ANALES RANM ◽  
2018 ◽  
Vol 135 (135(02)) ◽  
pp. 41-46
Author(s):  
J.A. Hinojosa ◽  
E.M. Moreno ◽  
P. Ferré ◽  
M.A. Pozo
Keyword(s):  
Language Processing ◽  
Language Comprehension ◽  
Developmental Psychology ◽  
Brain Regions ◽  
Emotional Content ◽  
Current Work ◽  
Subcortical Structures ◽  
Emotional Aspects ◽  
The Relationship ◽  
And Linguistics

Up to date the study of the relationship between language and emotion has received little attention from researchers. In the current work we will summarize evidence coming from the fields of developmental psychology and affective neurolinguistics. The results from different studies indicate that learning emotional language has its own idiosyncrasy. Also, the emotional content of words, sentences and texts modulates several levels of language processing, including phonological, lexico-semantic and morpho-syntactic aspects of language comprehension and production. Finally, the interactions between language and emotion involve the activation of several brain regions linked to distinct affective and linguistics processes, like parts of frontal and temporal cortices or subcortical structures such as the amygdala. Overall, the results of these studies clearly show that emotional content determines certain aspects of how we acquire and process language.

Processing Relationships Between Language-Being-Spoken and Other Speech Dimensions in Monolingual and Bilingual Listeners

2016 ◽  
Vol 60 (4) ◽  
pp. 530-561
Author(s):  
Charlotte R. Vaughn ◽  
Ann R. Bradlow
Keyword(s):  
Internal Structure ◽  
Mandarin Chinese ◽  
Language Processing ◽  
The Other ◽  
Speech Features ◽  
The Relationship ◽  
Speeded Classification

While indexical information is implicated in many levels of language processing, little is known about the internal structure of the system of indexical dimensions, particularly in bilinguals. A series of three experiments using the speeded classification paradigm investigated the relationship between various indexical and non-linguistic dimensions of speech in processing. Namely, we compared the relationship between a lesser-studied indexical dimension relevant to bilinguals, which language is being spoken (in these experiments, either Mandarin Chinese or English), with: talker identity (Experiment 1), talker gender (Experiment 2), and amplitude of speech (Experiment 3). Results demonstrate that language-being-spoken is integrated in processing with each of the other dimensions tested, and that these processing dependencies seem to be independent of listeners’ bilingual status or experience with the languages tested. Moreover, the data reveal processing interference asymmetries, suggesting a processing hierarchy for indexical, non-linguistic speech features.

Spoken language comprehension: insights from eye movements

2007 ◽  
pp. 308-326 ◽  
Author(s):  
Michael K. Tanenhaus
Keyword(s):  
Eye Movements ◽  
Language Processing ◽  
Language Comprehension ◽  
Language Production ◽  
World Literature ◽  
Spoken Language ◽  
Response Measure ◽  
Visual World ◽  
Spoken Language Processing ◽  
Spoken Language Comprehension

Recently, eye movements have become a widely used response measure for studying spoken language processing in both adults and children, in situations where participants comprehend and generate utterances about a circumscribed “Visual World” while fixation is monitored, typically using a free-view eye-tracker. Psycholinguists now use the Visual World eye-movement method to study both language production and language comprehension, in studies that run the gamut of current topics in language processing. Eye movements are a response measure of choice for addressing many classic questions about spoken language processing in psycholinguistics. This article reviews the burgeoning Visual World literature on language comprehension, highlighting some of the seminal studies and examining how the Visual World approach has contributed new insights to our understanding of spoken word recognition, parsing, reference resolution, and interactive conversation. It considers some of the methodological issues that come to the fore when psycholinguists use eye movements to examine spoken language comprehension.

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