CEST: a Cognitive Event based Semi-automatic Technique for behavior segmentation

This work introduces CEST, a Cognitive Event based Semiautomatic Technique for behavior segmentation. The technique was inspired by an everyday cognitive process. Humans, in fact, make sense of what happens to them by breaking the continuous stream of activity into smaller units, through a process known as segmentation. A cognitive theory, the Event Segmentation Theory, provides a computational and neurophysiological account of this process, describing how the detection of changes in the current situation drive boundary perception. CEST was designed with the aim of providing affective researchers with a tool to semi-automatically segment behavior. Researchers investigating behavior, as a matter of fact, often need to parse their research data into simpler units, either manually or automatically. To perform segmentation, the technique combines manual annotations and the output of change-point detection algorithms, techniques from time-series research that afford the detection of abrupt changes in time-series. CEST is inherently multidisciplinary: it is, to the best of our knowledge, the first attempt to adopt a cognitive science perspective on the issue of (semi) automatic behavior segmentation. CEST is a general-purpose technique, as it aims at providing a tool for segmenting behavior across research areas. In this manuscript, we detail the theories behind the design of CEST and the results of two experimental studies aimed at assessing the feasibility of the approach on both single and group scenarios. Most importantly, we present the results of the evaluation of CEST on a data-set of dance performances. We explore seven different techniques for change-point detection that could be leveraged to achieve semi-automatic segmentation through CEST and illustrate how two different bayesian algorithms led to the highest scores. Upon selecting the best algorithms, we measured the effect of the temporal grain of the analysis on the performance. Overall, our results support the idea of a semiautomatic segmentation technique for behavior segmentation. The output of the analysis mirrors cognitive science research on segmentation and on event structure perception. The work also tackles new challenges that may arise from our approach

An Emotional Agent for Moral Impairment Rehabilitation in TBI Patients

The ability to identify the emotions of others is a key component of what is known as social cognition. Narratives exploit this mechanism to create an emotional bond with the characters and to maintain the engagement of the audience throughout the story. In this paper, we illustrate a case study in emotion understanding in stories that exploits a computational agent to explore emotion impairment in a group of traumatic brain injured people. The study focuses on moral emotions, aiming to investigate the differences in moral functioning that characterize traumatic brain injured patients. After comparing the understanding of the moral and emotional facets of the agent's behavior in traumatic brain injured patients and in neurologically intact controls, slight–yet meaningful–differences were observed between the two groups. We describe the test methodology and results, highlighting their implications for the design of rehabilitation applications based on virtual agents

Computational Commensality: from theories to computational models for social food preparation and consumption in HCI

Food and eating are inherently social activities taking place, for example, around the dining table at home, in restaurants, or in public spaces. Enjoying eating with others, often referred to as “commensality,” positively affects mealtime in terms of, among other factors, food intake, food choice, and food satisfaction. In this paper we discuss the concept of “Computational Commensality,” that is, technology which computationally addresses various social aspects of food and eating. In the past few years, Human-Computer Interaction started to address how interactive technologies can improve mealtimes. However, the main focus has been made so far on improving the individual's experience, rather than considering the inherently social nature of food consumption. In this survey, we first present research from the field of social psychology on the social relevance of Food- and Eating-related Activities (F&EA). Then, we review existing computational models and technologies that can contribute, in the near future, to achieving Computational Commensality. We also discuss the related research challenges and indicate future applications of such new technology that can potentially improve F&EA from the commensality perspective

GAME-ON: A Multimodal Dataset for Cohesion and Group Analysis

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The Playful Potential of Digital Commensality: Learning from Spontaneous Playful Remote Dining Practices

With one-person households being increasingly common and Covid-19 lockdown policies forcing people to stay home, remote dining has become common practice for many, who take it as an opportunity to connect with others in times of loneliness. Sharing meals online, also known as digital commensality, is a rich form of interaction, where people leverage technology to achieve a sense of connectedness and belonging while eating. In this paper, we look at digital commensality and we explore its inherent playful potential with the aim to inspire the design of engaging technologies that can support, enhance and augment this form of interaction. For this, we used a situated play design approach to document and analyze the behavior of 36 people (including pairs of friends and strangers) sharing meals online. Our analysis surfaced a set of play potentials of remote dining -- i.e., playful things people already do and enjoy spontaneously while sharing meals online. We present those play potentials as inspirational material: they can motivate and enrich the design of future digital commensality technologies by responding to people's desire for playful and social interaction with, through, and around food

The role of emotion in movement segmentation

Humans understand ongoing events by breaking reality into meaningful units through a process named "event segmentation". In psychology, theories such as the "Event Segmentation theory" have been proposed to illustrate how humans perceive the structure of ongoing behavior. Parsing discrete gestures from a continuous movement stream is also a necessary step for movement analysis. Many approaches towards automatic emotion recognition from full body movement leverage automatic segmentation methods. Nonetheless, to the best of my knowledge, no framework has applied Event Segmentation theory to the automatic segmentation of emotion conveying movements. In this paper, I propose the exploitation of a computational model of event segmentation to extend a movement-analysis framework with an event segmentation module

An Exploratory Study on Group Potency Classification from Non-verbal Social Behaviours

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APPReddit: a Corpus of Reddit Posts Annotated for Appraisal

Despite the large number of computational resources for emotion recognition, there is a lack of data sets relying on appraisal models. According to Appraisal theories, emotions are the outcome of a multi-dimensional evaluation of events. In this paper, we present APPReddit, the first corpus of non-experimental data annotated according to this theory. After describing its development, we compare our resource with enISEAR, a corpus of events created in an experimental setting and annotated for appraisal. Results show that the two corpora can be mapped notwithstanding different typologies of data and annotations schemes. A SVM model trained on APPReddit predicts four appraisal dimensions without significant loss. Merging both corpora in a single training set increases the prediction of 3 out of 4 dimensions. Such findings pave the way to a better performing classification model for appraisal prediction