On the dynamic interplay between perception and action - a connectionist perspective

Increasing evidence suggests that perception and action planning do not represent separable stages of a unidirectional processing sequence, but rather emerging properties of highly interactive processes. To capture these characteristics of the human cognitive system, we have developed a connectionist model of the interaction between perception and action planning: HiTEC, based on the Theory of Event Coding (Hommel, M_sseler, Achschersleben & Prinz, 2001). The model is characterized by representations at multiple levels and by shared representations and processes. It complements available models of stimulus__response translation by providing a rationale for (1) how situation-specific meanings of motor actions emerge, (2) how and why some aspects of stimulus__response translation occur automatically and (3) how task demands modulate sensorimotor processing. The model is demonstrated to provide a unitary account and simulation of a number of key findings with multiple experimental paradigms on the interaction between perception and action such as the Simon effect, its inversion (Hommel, 1993), and action__effect learning.Action Contro

NIM as a brain for a humanoid robot

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Gamification to engage clinicians in registering data: A randomized trial

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A dynamic neural field approach to natural and efficient human-robot collaboration

A major challenge in modern robotics is the design of autonomous robots that are able to cooperate with people in their daily tasks in a human-like way. We address the challenge of natural human-robot interactions by using the theoretical framework of dynamic neural fields (DNFs) to develop processing architectures that are based on neuro-cognitive mechanisms supporting human joint action. By explaining the emergence of self-stabilized activity in neuronal populations, dynamic field theory provides a systematic way to endow a robot with crucial cognitive functions such as working memory, prediction and decision making . The DNF architecture for joint action is organized as a large scale network of reciprocally connected neuronal populations that encode in their firing patterns specific motor behaviors, action goals, contextual cues and shared task knowledge. Ultimately, it implements a context-dependent mapping from observed actions of the human onto adequate complementary behaviors that takes into account the inferred goal of the co-actor. We present results of flexible and fluent human-robot cooperation in a task in which the team has to assemble a toy object from its components.The present research was conducted in the context of the fp6-IST2 EU-IP Project JAST (proj. nr. 003747) and partly financed by the FCT grants POCI/V.5/A0119/2005 and CONC-REEQ/17/2001. We would like to thank Luis Louro, Emanuel Sousa, Flora Ferreira, Eliana Costa e Silva, Rui Silva and Toni Machado for their assistance during the robotic experiment

A computational model of perception and action for cognitive robotics

Robots are increasingly expected to perform tasks in complex environments. To this end, engineers provide them with processing architectures that are based on models of human information processing. In contrast to traditional models, where information processing is typically set up in stages (i.e., from perception to cognition to action), it is increasingly acknowledged by psychologists and robot engineers that perception and action are parts of an interactive and integrated process. In this paper, we present HiTEC, a novel computational (cognitive) model that allows for direct interaction between perception and action as well as for cognitive control, demonstrated by task-related attentional influences. Simulation results show that key behavioral studies can be readily replicated. Three processing aspects of HiTEC are stressed for their importance for cognitive robotics: (1) ideomotor learning of action control, (2) the influence of task context and attention on perception, action planning, and learning, and (3) the interaction between perception and action planning. Implications for the design of cognitive robotics are discussed

An architecture for fluid real-time conversational agents: Integrating incremental output generation and input processing

Kopp S, van Welbergen H, Yaghoubzadeh R, Buschmeier H. An architecture for fluid real-time conversational agents: Integrating incremental output generation and input processing. Journal on Multimodal User Interfaces. 2014;8:97-108.Embodied conversational agents still do not achieve the fluidity and smoothness of natural conversational interaction. One main reason is that current system often respond with big latencies and in inflexible ways. We argue that to overcome these problems, real-time conversational agents need to be based on an underlying architecture that provides two essential features for fast and fluent behavior adaptation: a close bi-directional coordination between input processing and output generation, and incrementality of processing at both stages. We propose an architectural framework for conversational agents [Artificial Social Agent Platform (ASAP)] providing these two ingredients for fluid real-time conversation. The overall architectural concept is described, along with specific means of specifying incremental behavior in BML and technical implementations of different modules. We show how phenomena of fluid real- time conversation, like adapting to user feedback or smooth turn-keeping, can be realized with ASAP and we describe in detail an example real-time interaction with the implemented system

On the dynamic interplay between perception and action - a connectionist perspective

Increasing evidence suggests that perception and action planning do not represent separable stages of a unidirectional processing sequence, but rather emerging properties of highly interactive processes. To capture these characteristics of the human cognitive system, we have developed a connectionist model of the interaction between perception and action planning: HiTEC, based on the Theory of Event Coding (Hommel, M_sseler, Achschersleben & Prinz, 2001). The model is characterized by representations at multiple levels and by shared representations and processes. It complements available models of stimulus__response translation by providing a rationale for (1) how situation-specific meanings of motor actions emerge, (2) how and why some aspects of stimulus__response translation occur automatically and (3) how task demands modulate sensorimotor processing. The model is demonstrated to provide a unitary account and simulation of a number of key findings with multiple experimental paradigms on the interaction between perception and action such as the Simon effect, its inversion (Hommel, 1993), and action__effect learning.</p

A dynamic quality assessment tool for laparoscopic hysterectomy to measure surgical outcomes

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A dynamic quality assessment tool for laparoscopic hysterectomy to measure surgical outcomes

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