The relation between Wechsler Performance Quotients and quotients on the Snijders-Oomen Non-Verbal Intelligence Scale

This paper examines the relationship between results of the Wechsler-Bellevue Performance Test of Intelligence and the Snijders-Oomen Non-Verbal Intelligence Scale (SONS) as given to hearing-impaired students at Central Institute for the Deaf.

Learning Data-Driven Models of Non-Verbal Behaviors for Building Rapport Using an Intelligent Virtual Agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].

Learning data-driven models of non-verbal behaviors for building rapport using an intelligent virtual agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness.^ Evidence-based patient-centered Brief Motivational Interviewing (BMI) interventions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary.^ Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems.^ To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].^

Non-verbal behavioural patterns in trilogue play transitions and family alliances

This case study focuses on non-verbal behaviour in father-mother-infant triads. Analyses were done on transitional moments during which the partners exchanged an active role for a participant-observer role, or vice versa. Transitions are known to be crucial moments for revealing familial transactional mechanisms. Our sample was comprised of six non-clinical families, characterized by different types of functional or problematic alliances (which is the degree of coordination between the partners). Our methodology included micro-analysis of body and gaze formations, facial expressions, and so on. Data were analysed using the research package 'THEME' for the detection of hidden patterns. Different types of non-verbal patterns were found, which may be prototypes corresponding to the different types of alliance. The patterns of the families with high alliances had a more elaborate construction and were more efficient for the concluding of transitions than the patterns of families with low alliances, which were either elementary or laborious. (PsycINFO Database Record (c) 2006 APA, all rights reserved) (journal abstract)

Non-verbal reasoning : multiple choice : 11 + practice papers.

Son pruebas de inteligencia cuyas preguntas no tienen una solución que puede ser aprendida de antemano.Son utilizadas, entre otras finalidades, para conocer de los escolares de ocho a catorce años, sus capacidades para comprender y asimilar información novedosa, independientemente de sus habilidades lingüísticas.

Estudo normativo do R-2: teste não verbal de inteligência para crianças

This study presents the standardization of the R-2 Non Verbal Intelligence Test for Children conducted at the city of Assis – SP, Brazil, and compares it with the São Paulo city standardization. The sample was composed by 559 children, between 5 and 11 years old, half of each sex, students from Assis city, randomly selected according to their proportion in private and public schools. Results indicate differences between ages and school types, but not between sexes. Percentile norms were established for the total sample at each age. The comparison of Assis and São Paulo city children reveals significant differences and Assis' results slightly higher. The conclusion is that R-2 Test is appropriate to cognitive assessment of Assis children, suggesting the use of new norms for this region.

Non-verbal markers of modality and evidentiality in MarENG

[EN] This article examines a variety of options for expressing speaker and writer stance in a subcorpus of MarENG, a maritime English learning tool sponsored by the EU (35,041 words). Non-verbal markers related to key areas of modal expression are presented; (1)epistemic adverbs and adverbial expressions, (2) epistemic adjectives, (3) deontic adjectives, (4) evidential adverbs, (5) evidential adjectives, (6) evidential interpersonal markers, and (7) single adverbials conveying the speaker’s attitudes, feelings or value judgments. The overall aim is to present an overview of how these non-verbal markers operate in this LSP genre.

Sense development through non verbal signs: early infancy and clinical psychoterapy

This thesis will describe the development of a relationship which is not necessarily verbal, but which generates communication, creates sense and meaning between human beings and produces “becomings” in the body that feels, perceives and physically transforms itself. This leads to a biosemiotic understanding of both the seen and unseen figure.

Text categorization methods for automatic estimation of verbal intelligence

In this paper we investigate whether conventional text categorization methods may suffice to infer different verbal intelligence levels. This research goal relies on the hypothesis that the vocabulary that speakers make use of reflects their verbal intelligence levels. Automatic verbal intelligence estimation of users in a spoken language dialog system may be useful when defining an optimal dialog strategy by improving its adaptation capabilities. The work is based on a corpus containing descriptions (i.e. monologs) of a short film by test persons yielding different educational backgrounds and the verbal intelligence scores of the speakers. First, a one-way analysis of variance was performed to compare the monologs with the film transcription and to demonstrate that there are differences in the vocabulary used by the test persons yielding different verbal intelligence levels. Then, for the classification task, the monologs were represented as feature vectors using the classical TF–IDF weighting scheme. The Naive Bayes, k-nearest neighbors and Rocchio classifiers were tested. In this paper we describe and compare these classification approaches, define the optimal classification parameters and discuss the classification results obtained.

Estimating Adaptacion of Dialogue Partners with Different Verbal Intelligence

This work investigates to what degree speakers with different verbal intelligence may adapt to each other. The work is based on a corpus consisting of 100 descriptions of a short film (monologues), 56 discussions about the same topic (dialogues), and verbal intelligence scores of the test participants. Adaptation between two dialogue partners was measured using cross-referencing, proportion of "I", "You" and "We" words, between-subject correlation and similarity of texts. It was shown that lower verbal intelligence speakers repeated more nouns and adjectives from the other and used the same linguistic categories more often than higher verbal intelligence speakers. In dialogues between strangers, participants with higher verbal intelligence showed a greater level of adaptation.