167,119 research outputs found
The earlier the better? A microfoundational perspective of international explorative and exploitative capabilities in the transition of SMEs to Industry 4.0
The international exploration-exploitation dichotomy is used in this study to expand on the resource-based view (RBV) and gain a deeper understanding of how these two distinct capabilities affect the adoption of I4.0 in small and medium-sized businesses (SMEs). We investigate managers' cognitive systems as a crucial microfoundation for the international organizational ambidexterity (i.e., exploration-exploitation), given that both managerial cognition and microfoundations perspective are underdeveloped and undertheorized. The study shows that managerial cognition is a crucial microfoundation of international organizational ambidexterity in the context of I4.0 adoption by using structural equation modeling on a sample of 116 Portuguese international SMEs. The findings also reveal neither international exploration nor exploitation has succeeded in creating new opportunities for the application of a technology-based model in SMEs internationalizing earlier. However, a post-hoc analysis revealed that under early internationalization, less (more) experienced SMEs benefit from international exploration (exploitation) to implement I4.0 technologies. This study concludes with implications and future research avenues.N/
Modeling the mobility of living organisms in heterogeneous landscapes: Does memory improve foraging success?
Thanks to recent technological advances, it is now possible to track with an
unprecedented precision and for long periods of time the movement patterns of
many living organisms in their habitat. The increasing amount of data available
on single trajectories offers the possibility of understanding how animals move
and of testing basic movement models. Random walks have long represented the
main description for micro-organisms and have also been useful to understand
the foraging behaviour of large animals. Nevertheless, most vertebrates, in
particular humans and other primates, rely on sophisticated cognitive tools
such as spatial maps, episodic memory and travel cost discounting. These
properties call for other modeling approaches of mobility patterns. We propose
a foraging framework where a learning mobile agent uses a combination of
memory-based and random steps. We investigate how advantageous it is to use
memory for exploiting resources in heterogeneous and changing environments. An
adequate balance of determinism and random exploration is found to maximize the
foraging efficiency and to generate trajectories with an intricate
spatio-temporal order. Based on this approach, we propose some tools for
analysing the non-random nature of mobility patterns in general.Comment: 14 pages, 4 figures, improved discussio
Explorative Synthetic Biology in AI: Criteria of Relevance and a Taxonomy for Synthetic Models of Living and Cognitive Processes
This article tackles the topic of the special issue “Biology
in AI: New Frontiers in Hardware, Software and Wetware Modeling
of Cognition” in two ways. It addresses the problem of the relevance
of hardware, software, and wetware models for the scientific
understanding of biological cognition, and it clarifies the
contributions that synthetic biology, construed as the synthetic
exploration of cognition, can offer to artificial intelligence (AI). The
research work proposed in this article is based on the idea that the
relevance of hardware, software, and wetware models of biological
and cognitive processes—that is, the concrete contribution that
these models can make to the scientific understanding of life and
cognition—is still unclear, mainly because of the lack of explicit
criteria to assess in what ways synthetic models can support the
experimental exploration of biological and cognitive phenomena.
Our article draws on elements from cybernetic and autopoietic
epistemology to define a framework of reference, for the synthetic
study of life and cognition, capable of generating a set of assessment
criteria and a classification of forms of relevance, for synthetic
models, able to overcome the sterile, traditional polarization of their
evaluation between mere imitation and full reproduction of the target
processes. On the basis of these tools, we tentatively map the forms
of relevance characterizing wetware models of living and cognitive
processes that synthetic biology can produce and outline a
programmatic direction for the development of “organizationally
relevant approaches” applying synthetic biology techniques to the
investigative field of (embodied) AI
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Active Learning: Effects of Core Training Design Elements on Self-Regulatory Processes, Learning, and Adaptability
This research describes a comprehensive examination of the cognitive, motivational, and emotional processes underlying active learning approaches, their effects on learning and transfer, and the core training design elements (exploration, training frame, emotion-control) and individual differences (cognitive ability, trait goal orientation, trait anxiety) that shape these processes. Participants (N = 350) were trained to operate a complex computer-based simulation. Exploratory learning and error-encouragement framing had a positive effect on adaptive transfer performance and interacted with cognitive ability and dispositional goal orientation to influence trainees’ metacognition and state goal orientation. Trainees who received the emotion-control strategy had lower levels of state anxiety. Implications for developing an integrated theory of active learning, learner-centered design, and research extensions are discussed
The RobotCub Approach to the Development of Cognition
This paper elaborates on the workplan of an
initiative in embodied cognition: RobotCub. Our
goal here is to provide background and to
motivate our long-term plan of empirical
research including brain and robotic sciences
following the principles of epigenetic robotics
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
Embodied Robot Models for Interdisciplinary Emotion Research
Due to their complex nature, emotions cannot be properly understood from the perspective of a single discipline. In this paper, I discuss how the use of robots as models is beneficial for interdisciplinary emotion research. Addressing this issue through the lens of my own research, I focus on a critical analysis of embodied robots models of different aspects of emotion, relate them to theories in psychology and neuroscience, and provide representative examples. I discuss concrete ways in which embodied robot models can be used to carry out interdisciplinary emotion research, assessing their contributions: as hypothetical models, and as operational models of specific emotional phenomena, of general emotion principles, and of specific emotion ``dimensions''. I conclude by discussing the advantages of using embodied robot models over other models.Peer reviewe
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
Modeling user navigation
This paper proposes the use of neural networks as a tool for studying navigation within virtual worlds. Results indicate that the network learned to predict the next step for a given trajectory. The analysis of hidden layer shows that the network was able to differentiate between two groups of users identified on the basis of their performance for a spatial task. Time series analysis of hidden node activation values and input vectors suggested that certain hidden units become specialised for place and heading, respectively. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction applications are discussed
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