An exploration of sarcasm detection in children with Attention Deficit Hyperactivity Disorder

This document is the Accepted Manuscript version of the following article: Amanda K. Ludlow, Eleanor Chadwick, Alice Morey, Rebecca Edwards, and Roberto Gutierrez, ‘An exploration of sarcasm detection in children with Attention Deficit Hyperactivity Disorder’, Journal of Communication Disorders, Vol. 70: 25-34, November 2017. Under embargo. Embargo end date: 31 October 2019. The Version of Record is available at doi: https://doi.org/10.1016/j.jcomdis.2017.10.003.The present research explored the ability of children with ADHD to distinguish between sarcasm and sincerity. Twenty-two children with a clinical diagnosis of ADHD were compared with 22 age and verbal IQ matched typically developing children using the Social Inference–Minimal Test from The Awareness of Social Inference Test (TASIT, McDonald, Flanagan, & Rollins, 2002). This test assesses an individual’s ability to interpret naturalistic social interactions containing sincerity, simple sarcasm and paradoxical sarcasm. Children with ADHD demonstrated specific deficits in comprehending paradoxical sarcasm and they performed significantly less accurately than the typically developing children. While there were no significant differences between the children with ADHD and the typically developing children in their ability to comprehend sarcasm based on the speaker’s intentions and beliefs, the children with ADHD were found to be significantly less accurate when basing their decision on the feelings of the speaker, but also on what the speaker had said. Results are discussed in light of difficulties in their understanding of complex cues of social interactions, and non-literal language being symptomatic of children with a clinical diagnosis of ADHD. The importance of pragmatic language skills in their ability to detect social and emotional information is highlighted.Peer reviewe

Inferring Acceptance and Rejection in Dialogue by Default Rules of Inference

This paper discusses the processes by which conversants in a dialogue can infer whether their assertions and proposals have been accepted or rejected by their conversational partners. It expands on previous work by showing that logical consistency is a necessary indicator of acceptance, but that it is not sufficient, and that logical inconsistency is sufficient as an indicator of rejection, but it is not necessary. I show how conversants can use information structure and prosody as well as logical reasoning in distinguishing between acceptances and logically consistent rejections, and relate this work to previous work on implicature and default reasoning by introducing three new classes of rejection: {\sc implicature rejections}, {\sc epistemic rejections} and {\sc deliberation rejections}. I show how these rejections are inferred as a result of default inferences, which, by other analyses, would have been blocked by the context. In order to account for these facts, I propose a model of the common ground that allows these default inferences to go through, and show how the model, originally proposed to account for the various forms of acceptance, can also model all types of rejection.Comment: 37 pages, uses fullpage, lingmacros, name

SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

Estudios acerca del establecimiento de conexiones entre enunciados hablados: ¿qué pueden contribuir a la promoción de la construcción de una representación coherente del discurso por parte de los estudiantes?

The aim of this article is to provide an overview of how the establishment of discourse connections among spoken statements has been studied by approaches to discourse analysis and psycholinguistic studies, in order to highlight what variables appear to be important for understanding how comprehension of spoken discourse can be facilitated. The consideration of discourse analysis approaches allows us to think about the role of the establishment of discourse connections among speech acts in the classroom, the uses of contextualization cues by bilingual students, the identification of social and cultural notions in teachers’ discourse, and the interactional effects of teachers’ interventions. Preliminary psycholinguistic studies contribute to our understanding of the role of establishing causal connections and integrating adjacent statements through the presence of discourse markers in the comprehension of spoken discourse by college students. The results of these approaches and studies provide insight into students’ comprehension of classroom discourse, and hold the potential for implications for instruction.El propósito de este artículo es realizar un recorrido a través de enfoques de análisis del discurso y estudios de psicolingüística que han investigado el establecimiento de conexiones entre enunciados hablados, a fin de destacar las variables que parecen ser centrales para facilitar la comprensión. La consideración de los enfoques del análisis del discurso nos permitirán pensar acerca del rol del establecimiento de conexiones entre actos del lenguaje en el aula, las funciones de las claves de contextualización, la identificación de las nociones sociales y culturales en el discurso de los profesores, los efectos de las intervenciones de los profesores en la interacción con los estudiantes. Los estudios preliminares de psicolingüística contribuirán a nuestra comprensión del rol del establecimiento de conexiones causales e integración de enunciados adyacentes a través de marcadores del discurso por parte de estudiantes universitarios. La consideración de estos enfoques y estudios nos ayudarán a pensar acerca de las contribuciones que sus propuestas y métodos pueden hacer al enriquecimiento de nuestro entendimiento de cómo los estudiantes comprenden el discurso producido durante las clases.Fil: Yomha Cevasco, Jazmin. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Broek, Paul van den. Leiden University; Países Bajo

Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

Predicting continuous conflict perception with Bayesian Gaussian processes

Conflict is one of the most important phenomena of social life, but it is still largely neglected by the computing community. This work proposes an approach that detects common conversational social signals (loudness, overlapping speech, etc.) and predicts the conflict level perceived by human observers in continuous, non-categorical terms. The proposed regression approach is fully Bayesian and it adopts Automatic Relevance Determination to identify the social signals that influence most the outcome of the prediction. The experiments are performed over the SSPNet Conflict Corpus, a publicly available collection of 1430 clips extracted from televised political debates (roughly 12 hours of material for 138 subjects in total). The results show that it is possible to achieve a correlation close to 0.8 between actual and predicted conflict perception