The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform
Louisphilippe Morency - One of the best experts on this subject based on the ideXlab platform.
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multi attention recurrent network for Human Communication comprehension
arXiv: Artificial Intelligence, 2018Co-Authors: Amir Zadeh, Paul Pu Liang, Soujanya Poria, Prateek Vij, Erik Cambria, Louisphilippe MorencyAbstract:Human face-to-face Communication is a complex multimodal signal. We use words (language modality), gestures (vision modality) and changes in tone (acoustic modality) to convey our intentions. Humans easily process and understand face-to-face Communication, however, comprehending this form of Communication remains a significant challenge for Artificial Intelligence (AI). AI must understand each modality and the interactions between them that shape Human Communication. In this paper, we present a novel neural architecture for understanding Human Communication called the Multi-attention Recurrent Network (MARN). The main strength of our model comes from discovering interactions between modalities through time using a neural component called the Multi-attention Block (MAB) and storing them in the hybrid memory of a recurrent component called the Long-short Term Hybrid Memory (LSTHM). We perform extensive comparisons on six publicly available datasets for multimodal sentiment analysis, speaker trait recognition and emotion recognition. MARN shows state-of-the-art performance on all the datasets.
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Modeling Human Communication Dynamics [Social Sciences]
IEEE Signal Processing Magazine, 2010Co-Authors: Louisphilippe MorencyAbstract:Face-to-face Communication is a highly interactive process where participants mutually exchange and interpret verbal and nonverbal messages. Communication dynamics represent the temporal relationship between these communicative messages. Even when only one person speaks at a time, other participants exchange information continuously among themselves and with the speaker through gesture, gaze, posture, and facial expressions. The transactional view of Human Communication shows an important dynamic between communicative behaviors where each person serves simultaneously as speaker and listener. At the same time you send a message, you also receive messages from your own Communications (individual dynamics) as well as from the reactions of the other person(s) (interpersonal dynamics).
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Modeling Human Communication Dynamics
IEEE Signal Processing Magazine, 2010Co-Authors: Louisphilippe MorencyAbstract:Human face-to-face Communication is a little like a dance, in that participants continuously adjust their behaviors based on verbal and nonverbal cues from the social context. Today’s computers and interactive devices are still lacking many of these Human-like abilities to hold fluid and natural interactions. Leveraging recent advances in machine learning, audio-visual signal processing and computational linguistics, my research focuses on creating Human-computer interaction (HCI) technologies able to analyze, recognize and predict Human subtle communicative behaviors in a social context. I formalize this new research endeavor with a Human Communication Dynamics framework, addressing four key computational challenges: behavioral dynamic, multimodal dynamic, interpersonal dynamic and societal dynamic. Central to this research effort is the introduction of new probabilistic models able to learn the temporal and fine-grained latent dependencies across behaviors, modalities and interlocutors. In this talk, I will present some of our recent achievements modeling multiple aspects of Human Communication dynamics, motivated by applications in healthcare (depression, PTSD, suicide, autism), education (learning analytics), business (negotiation, interpersonal skills) and social multimedia (opinion mining, social influence). Institute for Creative Technologies, University of Southern California, USA, e-mail: morency@ict.usc.edu Proceedings of 5th International Workshop on Spoken Dialog Systems Napa, January 17-20, 2014 3
Nicolas Fay - One of the best experts on this subject based on the ideXlab platform.
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How to create a Human Communication system a theoretical model
Interaction Studies, 2017Co-Authors: Casey J. Lister, Nicolas FayAbstract:Following a synthesis of naturalistic and experimental studies of language creation, we propose a theoretical model that describes the process through which Human Communication systems might arise and evolve. Three key processes are proposed that give rise to effective, efficient and shared Human Communication systems: (1) motivated signs that directly resemble their meaning facilitate cognitive alignment, improving Communication success; (2) behavioral alignment onto an inventory of shared sign-to-meaning mappings bolsters cognitive alignment between interacting partners; (3) sign refinement, through interactive feedback, enhances the efficiency of the evolving Communication system. By integrating the findings across a range of diverse studies, we propose a theoretical model of the process through which the earliest Human Communication systems might have arisen and evolved. Importantly, because our model is not bound to a single modality it can describe the creation of shared sign systems across a range of contexts, informing theories of language creation and evolution.
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Cultural selection drives the evolution of Human Communication systems
Proceedings. Biological sciences, 2014Co-Authors: Monica Tamariz, T. Mark Ellison, Dale J. Barr, Nicolas FayAbstract:Human Communication systems evolve culturally, but the evolutionary mechanisms that drive this evolution are not well understood. Against a baseline that Communication variants spread in a population following neutral evolutionary dynamics (also known as drift models), we tested the role of two cultural selection models: coordination- and content-biased. We constructed a parametrized mixed probabilistic model of the spread of communicative variants in four 8-person laboratory micro-societies engaged in a simple Communication game. We found that selectionist models, working in combination, explain the majority of the empirical data. The best-fitting parameter setting includes an egocentric bias and a content bias, suggesting that participants retained their own previously used communicative variants unless they encountered a superior (content-biased) variant, in which case it was adopted. This novel pattern of results suggests that (i) a theory of the cultural evolution of Human Communication systems must integrate selectionist models and (ii) Human Communication systems are functionally adaptive complex systems.
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Human Communication Systems Evolve by Cultural Selection
arXiv: Social and Information Networks, 2014Co-Authors: Nicolas Fay, Monica Tamariz, T. Mark Ellison, Dale J. BarrAbstract:Human Communication systems, such as language, evolve culturally; their components undergo reproduction and variation. However, a role for selection in cultural evolutionary dynamics is less clear. Often neutral evolution (also known as 'drift') models, are used to explain the evolution of Human Communication systems, and cultural evolution more generally. Under this account, cultural change is unbiased: for instance, vocabulary, baby names and pottery designs have been found to spread through random copying. While drift is the null hypothesis for models of cultural evolution it does not always adequately explain empirical results. Alternative models include cultural selection, which assumes variant adoption is biased. Theoretical models of Human Communication argue that during conversation interlocutors are biased to adopt the same labels and other aspects of linguistic representation (including prosody and syntax). This basic alignment mechanism has been extended by computer simulation to account for the emergence of linguistic conventions. When agents are biased to match the linguistic behavior of their interlocutor, a single variant can propagate across an entire population of interacting computer agents. This behavior-matching account operates at the level of the individual. We call it the Conformity-biased model. Under a different selection account, called content-biased selection, functional selection or replicator selection, variant adoption depends upon the intrinsic value of the particular variant (e.g., ease of learning or use). This second alternative account operates at the level of the cultural variant. Following Boyd and Richerson we call it the Content-biased model. The present paper tests the drift model and the two biased selection models' ability to explain the spread of communicative signal variants in an experimental micro-society.
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The cultural evolution of Human Communication systems in different sized populations: usability trumps learnability.
PloS one, 2013Co-Authors: Nicolas Fay, T. Mark EllisonAbstract:This study examines the intergenerational transfer of Human Communication systems. It tests if Human Communication systems evolve to be easy to learn or easy to use (or both), and how population size affects learnability and usability. Using an experimental-semiotic task, we find that Human Communication systems evolve to be easier to use (production efficiency and reproduction fidelity), but harder to learn (identification accuracy) for a second generation of naive participants. Thus, usability trumps learnability. In addition, the Communication systems that evolve in larger populations exhibit distinct advantages over those that evolve in smaller populations: the learnability loss (from the Initial signs) is more muted and the usability benefits are more pronounced. The usability benefits for Human Communication systems that evolve in a small and large population is explained through guided variation reducing sign complexity. The enhanced performance of the Communication systems that evolve in larger populations is explained by the operation of a content bias acting on the larger pool of competing signs. The content bias selects for information-efficient iconic signs that aid learnability and enhance usability.
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How to bootstrap a Human Communication system
Cognitive Science, 2013Co-Authors: Nicolas Fay, Michael Arbib, Simon GarrodAbstract:How might a Human Communication system be bootstrapped in the absence of conventional language? We argue that motivated signs play an important role (i.e., signs that are linked to meaning by structural resemblance or by natural association). An experimental study is then reported in which participants try to communicate a range of pre-specified items to a partner using repeated non-linguistic vocalization, repeated gesture, or repeated non-linguistic vocalization plus gesture (but without using their existing language system). Gesture proved more effective (measured by Communication success) and more efficient (measured by the time taken to communicate) than non-linguistic vocalization across a range of item categories (emotion, object, and action). Combining gesture and vocalization did not improve performance beyond gesture alone. We experimentally demonstrate that gesture is a more effective means of bootstrapping a Human Communication system. We argue that gesture outperforms non-linguistic vocalization because it lends itself more naturally to the production of motivated signs.
Benjamin Harris - One of the best experts on this subject based on the ideXlab platform.
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LibGuides: Human Communication Subject Guide: Find Periodicals
2009Co-Authors: Benjamin HarrisAbstract:This guide offers suggestions for locating materials related to courses in the Human Communication section of the Human Communication and Theater department.
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LibGuides: Human Communication Subject Guide: Find Media
2009Co-Authors: Benjamin HarrisAbstract:This guide offers suggestions for locating materials related to courses in the Human Communication section of the Human Communication and Theater department.
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LibGuides: Human Communication Subject Guide: Quick Start
2009Co-Authors: Benjamin HarrisAbstract:This guide offers suggestions for locating materials related to courses in the Human Communication section of the Human Communication and Theater department.
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LibGuides: Human Communication Subject Guide: Find Reference Sources
2009Co-Authors: Benjamin HarrisAbstract:This guide offers suggestions for locating materials related to courses in the Human Communication section of the Human Communication and Theater department.
Mikael Drugge - One of the best experts on this subject based on the ideXlab platform.
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Interaction aspects of wearable computing for Human Communication
2006Co-Authors: Mikael DruggeAbstract:This thesis presents the use of wearable computers for aiding Human Communication over a distance, focusing on interaction aspects that need to be resolved in order to realize this goal. As wearabl ...
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Wearable computer interaction issues in mediated Human to Human Communication
2004Co-Authors: Mikael DruggeAbstract:This thesis explores the use of wearable computers as mediators for Human to Human Communication. The user of a wearable computer can deliver live video, audio and commentary from a remote location ...
T. Mark Ellison - One of the best experts on this subject based on the ideXlab platform.
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Cultural selection drives the evolution of Human Communication systems
Proceedings. Biological sciences, 2014Co-Authors: Monica Tamariz, T. Mark Ellison, Dale J. Barr, Nicolas FayAbstract:Human Communication systems evolve culturally, but the evolutionary mechanisms that drive this evolution are not well understood. Against a baseline that Communication variants spread in a population following neutral evolutionary dynamics (also known as drift models), we tested the role of two cultural selection models: coordination- and content-biased. We constructed a parametrized mixed probabilistic model of the spread of communicative variants in four 8-person laboratory micro-societies engaged in a simple Communication game. We found that selectionist models, working in combination, explain the majority of the empirical data. The best-fitting parameter setting includes an egocentric bias and a content bias, suggesting that participants retained their own previously used communicative variants unless they encountered a superior (content-biased) variant, in which case it was adopted. This novel pattern of results suggests that (i) a theory of the cultural evolution of Human Communication systems must integrate selectionist models and (ii) Human Communication systems are functionally adaptive complex systems.
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Human Communication Systems Evolve by Cultural Selection
arXiv: Social and Information Networks, 2014Co-Authors: Nicolas Fay, Monica Tamariz, T. Mark Ellison, Dale J. BarrAbstract:Human Communication systems, such as language, evolve culturally; their components undergo reproduction and variation. However, a role for selection in cultural evolutionary dynamics is less clear. Often neutral evolution (also known as 'drift') models, are used to explain the evolution of Human Communication systems, and cultural evolution more generally. Under this account, cultural change is unbiased: for instance, vocabulary, baby names and pottery designs have been found to spread through random copying. While drift is the null hypothesis for models of cultural evolution it does not always adequately explain empirical results. Alternative models include cultural selection, which assumes variant adoption is biased. Theoretical models of Human Communication argue that during conversation interlocutors are biased to adopt the same labels and other aspects of linguistic representation (including prosody and syntax). This basic alignment mechanism has been extended by computer simulation to account for the emergence of linguistic conventions. When agents are biased to match the linguistic behavior of their interlocutor, a single variant can propagate across an entire population of interacting computer agents. This behavior-matching account operates at the level of the individual. We call it the Conformity-biased model. Under a different selection account, called content-biased selection, functional selection or replicator selection, variant adoption depends upon the intrinsic value of the particular variant (e.g., ease of learning or use). This second alternative account operates at the level of the cultural variant. Following Boyd and Richerson we call it the Content-biased model. The present paper tests the drift model and the two biased selection models' ability to explain the spread of communicative signal variants in an experimental micro-society.
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The cultural evolution of Human Communication systems in different sized populations: usability trumps learnability.
PloS one, 2013Co-Authors: Nicolas Fay, T. Mark EllisonAbstract:This study examines the intergenerational transfer of Human Communication systems. It tests if Human Communication systems evolve to be easy to learn or easy to use (or both), and how population size affects learnability and usability. Using an experimental-semiotic task, we find that Human Communication systems evolve to be easier to use (production efficiency and reproduction fidelity), but harder to learn (identification accuracy) for a second generation of naive participants. Thus, usability trumps learnability. In addition, the Communication systems that evolve in larger populations exhibit distinct advantages over those that evolve in smaller populations: the learnability loss (from the Initial signs) is more muted and the usability benefits are more pronounced. The usability benefits for Human Communication systems that evolve in a small and large population is explained through guided variation reducing sign complexity. The enhanced performance of the Communication systems that evolve in larger populations is explained by the operation of a content bias acting on the larger pool of competing signs. The content bias selects for information-efficient iconic signs that aid learnability and enhance usability.
