Social signals: from theory to applications

Poggi, Isabella; D’Errico, Francesca; Vinciarelli, Alessandro

doi:10.1007/s10339-012-0514-4

Cited by 8 publications

(5 citation statements)

References 46 publications

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“…Here the capacity to embody one's own mental state and intentions is of import (i.e., specifying intentions through kinematic movements) given this allows humans, and ideally robots, to establish predictability, maintain shared representations, and facilitate interactions in a way that can maximize the success of joint tasks 20 . These issues, however, are just as theoretical as they are technical, and investigators are necessarily exploring these issues by integrating insights across disciplines [21][22][23][24] . While the implications of embodiment have allowed for the design of physical systems that can act dynamically within the niche of its physical environment 25 , functional performance within a social environment requires an understanding of embodiment as it applies to intelligent social behavior.…”

Section: Natural and Artificial Social-cognitive Systems Are Embodiedmentioning

confidence: 98%

“…While recent works have attempted to integrate the socialcognitive theories underlying SSP into a cohesive framework from which models can be established [21][22][23][24] , little effort has been given toward incorporating the current body of knowledge regarding data-capture and the classification of signals and cues into a taxonomy capable of guiding researchers across disciplines 30 . An initial attempt at such a taxonomy, however, has been produced with respects to implementations within social robotics, providing a distinction between natural cues/signals (i.e., those present in humans that can be mimicked by robots) and artificial cues/signals (i.e., those that only an artificial agent can express, such as beeps, LED lights, etc.)…”

Section: Social Cues and Signalsmentioning

confidence: 99%

“…While there is still a general lack of consensus across disciplines, and a cohesive framework has yet to be developed, the field of Social Signal Processing (SSP) offers a promising approach that draws upon the insights of a wide array of disciplines, including neuroscience, psychology, and linguistics. SSP utilizes theory from the social and cognitive sciences with the aim of establishing mechanisms that recognize social signals and cues, allowing for their interpretation through socially-aware computing 8,21 . Progress within this field is providing guidance for modelling social-cognitive mechanisms for implementation within social robots 24,29 .…”

Section: Natural and Artificial Social-cognitive Systems Are Embodiedmentioning

confidence: 99%

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An interdisciplinary taxonomy of social cues and signals in the service of engineering robotic social intelligence

et al. 2014

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Understanding intentions is a complex social-cognitive task for humans, let alone machines. In this paper we discuss how the developing field of Social Signal Processing, and assessing social cues to interpret social signals, may help to develop a foundation for robotic social intelligence. We describe a taxonomy to further R&D in HRI and facilitate natural interactions between humans and robots. This is based upon an interdisciplinary framework developed to integrate: (1) the sensors used for detecting social cues, (2) the parameters for differentiating and classifying differing levels of those cues, and (3) how sets of social cues indicate specific social signals. This is necessarily an iterative process, as technologies improve and social science researchers better understand the complex interactions of vast quantities of social cue combinations. As such, the goal of this paper is to advance a taxonomy of this nature to further stimulate interdisciplinary collaboration in the development of advanced social intelligence that mutually informs areas of robotic perception and intelligence.

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Section: Natural and Artificial Social-cognitive Systems Are Embodiedmentioning

confidence: 98%

Section: Social Cues and Signalsmentioning

confidence: 99%

Section: Natural and Artificial Social-cognitive Systems Are Embodiedmentioning

confidence: 99%

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An interdisciplinary taxonomy of social cues and signals in the service of engineering robotic social intelligence

et al. 2014

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“…The three perspectives presented above (psychobiologic, psycho-social and bio-social) must merge into a unitary bio-psycho-social model, with such a unified model being specific for humans (35). In support of this, we are for example conscious about psycho-social reality not only during interaction with social medium (as a result of social activity), but also at the individual level (as a result of cerebral activity).…”

Section: A Bio-psycho-social Integrationmentioning

confidence: 99%

The mind-body problem; three equations and one solution represented by immaterial-material data

Motofei¹,

Rowland²

2018

JMMS

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To cite this article: Motofei IG, Rowland Dl. The mind-body problem; three equations and one solution represented by immaterial-material data. AbstractHuman life occurs within a complex bio-psycho-social milieu, a heterogeneous system that is integrated by multiple bidirectional interrelations existing between the abstract-intangible ideas and physical-chemical support of environment. The mind is thus placed between the abstract ideas/ concepts and neurobiological brain that is further connected to environment. In other words, the mind acts as an interface between the immaterial (abstract/ intangible) data and material (biological) support. The science is unable to conceives and explains an interaction between the immaterial and material domains (to understand nature of the mind), this question generating in literature the mind-body problem.We have published in the past a succession of articles related to the mind-body problem, in order to demonstrate the fact that this question is actually a false issue. The phenomenon of immaterial-material interaction is impossible to be explained because it never occurs, which means that there is no need to explain the immaterial-material interaction. Our mind implies only a temporal association between the immaterial data and material support, this dynamic interrelation being presented and argued here as a solution to the mind-body problem.The limited psycho-biologic approach of the mind-body problem is expanded here to a more comprehensive and feasible bio-psycho-social perspective, generating thus three distinct (biopsychological, bio-social, and psycho-social) equations. These three equations can be solved through a solution represented by a dynamic cerebral system (two distinct and interconnected subunits of the brain) which presumably could have the capability of receiving and processing abstract data through association (with no interaction) between immaterial and material data.

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“…In 1993, Ambady and Rosenthal found out that college teachers were able to predict students' end‐of‐semester results by observing “thin slices” of interactions, that is, looking at their physical and non‐verbal behaviour with short video clips (Ambady, Rosenthal, Scollon, & Kress, ). These early findings paved the way towards a new research hypothesis, the possibility to infer cognitive and social processes by using multiple data sources and social signal processing (Poggi & Errico, ).…”

Section: Introductionmentioning

confidence: 99%

From signals to knowledge: A conceptual model for multimodal learning analytics

Mitri

Schneider

Specht

et al. 2018

Computer Assisted Learning

165

100

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Multimodality in learning analytics and learning science is under the spotlight. The landscape of sensors and wearable trackers that can be used for learning support is evolving rapidly, as well as data collection and analysis methods. Multimodal data can now be collected and processed in real time at an unprecedented scale. With sensors, it is possible to capture observable events of the learning process such as learner's behaviour and the learning context. The learning process, however, consists also of latent attributes, such as the learner's cognitions or emotions. These attributes are unobservable to sensors and need to be elicited by human-driven interpretations. We conducted a literature survey of experiments using multimodal data to frame the young research field of multimodal learning analytics. The survey explored the multimodal data used in related studies (the input space) and the learning theories selected (the hypothesis space). The survey led to the formulation of the Multimodal Learning Analytics Model whose main objectives are of (O1) mapping the use of multimodal data to enhance the feedback in a learning context; (O2) showing how to combine machine learning with multimodal data; and (O3) aligning the terminology used in the field of machine learning and learning science. KEYWORDSlearning analytics, machine learning, multimodal data, multimodality, sensors, social signal processing | INTRODUCTIONWith the rise of data-driven techniques to discover insights and generate predictions from the learning process such as learning analytics, the need for 360°data about learners has grown consistently. Combining data coming from multiple sources has become a prominent necessity in learning research and has led to an increased interest in multimodality and consequently into multimodal data analysis. To clarify the concept of multimodality, we use the definition provided by Nigay and Coutaz. The term "multi" refers to "more than one", whereas the term "modal" stands both for "modality" and for "mode". The modality is the type of communication channel used by two agents to convey and acquire information that defines the data exchange.The mode is the state that determines the context in which the information is interpreted (Nigay & Coutaz, 1993). The reasons why multimodality in learning is drawing so much attention can be summarized according to four developments.First of all, multimodality is a consolidated theory. It has been subjected of investigation already for two decades in different fields including functional linguistic, conversational analysis, and social semiotics (Jewitt, Bezemer, & O'Halloran, 2016). Research in multimodal interaction investigated how different modalities interact andThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Social signals: from theory to applications

Cited by 8 publications

References 46 publications

An interdisciplinary taxonomy of social cues and signals in the service of engineering robotic social intelligence

An interdisciplinary taxonomy of social cues and signals in the service of engineering robotic social intelligence

The mind-body problem; three equations and one solution represented by immaterial-material data

From signals to knowledge: A conceptual model for multimodal learning analytics

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