I see what you mean

The ability to understand and predict others' behavior is essential for successful interactions. When making predictions about what other humans will do, we treat them as intentional systems and adopt the intentional stance, i.e., refer to their mental states such as desires and intentions. In the present experiments, we investigated whether the mere belief that the observed agent is an intentional system influences basic social attention mechanisms. We presented pictures of a human and a robot face in a gaze cuing paradigm and manipulated the likelihood of adopting the intentional stance by instruction: in some conditions, participants were told that they were observing a human or a robot, in others, that they were observing a human-like mannequin or a robot whose eyes were controlled by a human. In conditions in which participants were made to believe they were observing human behavior (intentional stance likely) gaze cuing effects were significantly larger as compared to conditions when adopting the intentional stance was less likely. This effect was independent of whether a human or a robot face was presented. Therefore, we conclude that adopting the intentional stance when observing others' behavior fundamentally influences basic mechanisms of social attention. The present results provide striking evidence that high-level cognitive processes, such as beliefs, modulate bottom-up mechanisms of attentional selection in a top-down manner

Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. This Indexical Model incorporates multiple information sources, including perceptions, domain knowledge, and short-term and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of underspecific referring expressions and unexpressed argument alternations of verbs. The model is being used to support linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.Comment: Advances in Cognitive Systems 3 (2014

Reasoning about space for human-robot interaction

L'interaction Homme-Robot est un domaine de recherche qui se développe de manière exponentielle durant ces dernières années, ceci nous procure de nouveaux défis au raisonnement géométrique du robot et au partage d'espace. Le robot pour accomplir une tâche, doit non seulement raisonner sur ses propres capacités, mais également prendre en considération la perception humaine, c'est à dire "Le robot doit se placer du point de vue de l'humain". Chez l'homme, la capacité de prise de perspective visuelle commence à se manifester à partir du 24ème mois. Cette capacité est utilisée pour déterminer si une autre personne peut voir un objet ou pas. La mise en place de ce genre de capacités sociales améliorera les capacités cognitives du robot et aidera le robot pour une meilleure interaction avec les hommes. Dans ce travail, nous présentons un mécanisme de raisonnement spatial de point de vue géométrique qui utilise des concepts psychologiques de la "prise de perspective" et "de la rotation mentale" dans deux cadres généraux: - La planification de mouvement pour l'interaction homme-robot: le robot utilise "la prise de perspective égocentrique" pour évaluer plusieurs configurations où le robot peut effectuer différentes tâches d'interaction. - Une interaction face à face entre l'homme et le robot : le robot emploie la prise de point de vue de l'humain comme un outil géométrique pour comprendre l'attention et l'intention humaine afin d'effectuer des tâches coopératives.Human Robot Interaction is a research area that is growing exponentially in last years. This fact brings new challenges to the robot's geometric reasoning and space sharing abilities. The robot should not only reason on its own capacities but also consider the actual situation by looking from human's eyes, thus "putting itself into human's perspective". In humans, the "visual perspective taking" ability begins to appear by 24 months of age and is used to determine if another person can see an object or not. The implementation of this kind of social abilities will improve the robot's cognitive capabilities and will help the robot to perform a better interaction with human beings. In this work, we present a geometric spatial reasoning mechanism that employs psychological concepts of "perspective taking" and "mental rotation" in two general frameworks: - Motion planning for human-robot interaction: where the robot uses "egocentric perspective taking" to evaluate several configurations where the robot is able to perform different tasks of interaction. - A face-to-face human-robot interaction: where the robot uses perspective taking of the human as a geometric tool to understand the human attention and intention in order to perform cooperative tasks

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

Beyond Gazing, Pointing, and Reaching: A Survey of Developmental Robotics

Developmental robotics is an emerging field located at the intersection of developmental psychology and robotics, that has lately attracted quite some attention. This paper gives a survey of a variety of research projects dealing with or inspired by developmental issues, and outlines possible future directions

The Impact of Animated Banner Ads on Online Consumers: A Feature-Level Analysis Using Eye Tracking

Despite the popular use of animated banner ads on websites, extant research on the effects of web animation has generated mixed results. We argue that it is critical to identify feature-level animation characteristics and examine their individual and combined effects on capturing online consumers’ attention across different task conditions. We identify three key animation features (i.e., motion, lagging, and looming) based on three attention theories and investigate their effects on online consumers’ attention and recall across browsing and searching tasks in three laboratory experiments using an eye tracking machine. Experiment 1 found that both motion and looming (animation features) are effective in attracting online consumers’ attention to animated ads when they are performing a browsing task. However, combining a salient feature (e.g., motion) with another salient feature (e.g., looming) does not improve the original attention attraction effect, suggesting a “banner saturation” effect. Further, we found that online consumers’ attention positively affects their recall performance. In Experiment 2, none of the animation features or their interactions had a significant effect when the subjects were performing a searching task, indicating that task is an important boundary condition when applying attention theories. Experiment 3 replicated Experiment 1 in a more realistic context and produced similar results. We conclude the paper by discussing theoretical and practical implications as well as avenues for future research