7,487 research outputs found
Simulating activities: Relating motives, deliberation, and attentive coordination
Activities are located behaviors, taking time, conceived as socially meaningful, and usually involving interaction with tools and the environment. In modeling human cognition as a form of problem solving (goal-directed search and operator sequencing), cognitive science researchers have not adequately studied “off-task” activities (e.g., waiting), non-intellectual motives (e.g., hunger), sustaining a goal state (e.g., playful interaction), and coupled perceptual-motor dynamics (e.g., following someone). These aspects of human behavior have been considered in bits and pieces in past research, identified as scripts, human factors, behavior settings, ensemble, flow experience, and situated action. More broadly, activity theory provides a comprehensive framework relating motives, goals, and operations. This paper ties these ideas together, using examples from work life in a Canadian High Arctic research station. The emphasis is on simulating human behavior as it naturally occurs, such that “working” is understood as an aspect of living. The result is a synthesis of previously unrelated analytic perspectives and a broader appreciation of the nature of human cognition. Simulating activities in this comprehensive way is useful for understanding work practice, promoting learning, and designing better tools, including human-robot systems
Introduction: The Third International Conference on Epigenetic Robotics
This paper summarizes the paper and poster contributions
to the Third International Workshop on
Epigenetic Robotics. The focus of this workshop is
on the cross-disciplinary interaction of developmental
psychology and robotics. Namely, the general
goal in this area is to create robotic models of the
psychological development of various behaviors. The
term "epigenetic" is used in much the same sense as
the term "developmental" and while we could call
our topic "developmental robotics", developmental
robotics can be seen as having a broader interdisciplinary
emphasis. Our focus in this workshop is
on the interaction of developmental psychology and
robotics and we use the phrase "epigenetic robotics"
to capture this focus
Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity
International audienceWe present a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, we used a branch of complex systems modeling called artificial self-organization in which large sets of software entities interacting locally give rise to bottom-up collective behaviors. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model recovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with artificial self-organization. Although there is no evidence yet of the model's connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments
The Self-Organization of Speech Sounds
The speech code is a vehicle of language: it defines
a set of forms used by a community to carry information.
Such a code is necessary to support the linguistic
interactions that allow humans to communicate.
How then may a speech code be formed prior to the
existence of linguistic interactions?
Moreover, the human speech code is discrete and compositional,
shared by all the individuals of a community but different
across communities, and phoneme inventories are characterized by
statistical regularities. How can a speech code with these properties form?
We try to approach these questions in the paper,
using the ``methodology of the artificial''. We
build a society of artificial agents, and detail a mechanism that
shows the formation of a discrete speech code without pre-supposing
the existence of linguistic capacities or of coordinated interactions.
The mechanism is based on a low-level model of
sensory-motor interactions. We show that the integration of certain very
simple and non language-specific neural devices
leads to the formation of a speech code that
has properties similar to the human speech code.
This result relies on the self-organizing properties of a generic
coupling between perception and production
within agents, and on the interactions between agents.
The artificial system helps us to develop better intuitions on how speech
might have appeared, by showing how self-organization
might have helped natural selection to find speech
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
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A generic testing framework for agent-based simulation models
International audienceAgent-based modelling and simulation (ABMS) had an increasing attention during the last decade. However, the weak validation and verification of agent-based simulation models makes ABMS hard to trust. There is no comprehensive tool set for verification and validation of agent-based simulation models, which demonstrates that inaccuracies exist and/or reveals the existing errors in the model. Moreover, on the practical side, many ABMS frameworks are in use. In this sense, we designed and developed a generic testing framework for agent-based simulation models to conduct validation and verification of models. This paper presents our testing framework in detail and demonstrates its effectiveness by showing its applicability on a realistic agent-based simulation case study
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