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Recent work in cognitive science, much of it placed in opposition
to a computational view of the mind, has argued that the concept of
representation and theories based on that concept are not
sufficient to explain the details of cognitive processing. These
attacks on representation have focused on the importance of context
sensitivity in cognitive processing, on the range of individual
differences in performance, and on the relationship between minds
and the bodies and environments in which they exist. In each case,
models based on traditional assumptions about representation have
been assumed to be too rigid to account for the effects of these
factors on cognitive processing. In place of a representational
view of mind, other formalisms and methodologies, such as nonlinear
differential equations (or dynamical systems) and situated
robotics, have been proposed as better explanatory tools for
understanding cognition.
This book is based on the notion that, while new tools and
approaches for understanding cognition are valuable,
representational approaches do not need to be abandoned in the
course of constructing new models and explanations. Rather, models
that incorporate representation are quite compatible with the kinds
of complex situations being modeled with the new methods. This
volume illustrates the power of this explicitly representational
approach--labeled "cognitive dynamics"--in original essays by
prominent researchers in cognitive science. Each chapter explores
some aspect of the dynamics of cognitive processing while still
retaining representations as the centerpiece of the explanations of
the key phenomena. These chapters serve as an existence proof that
representation is not incompatible with the dynamics of cognitive
processing. The book is divided into sections on foundational
issues about the use of representation in cognitive science, the
dynamics of low level cognitive processes (such as visual and
auditory perception and simple lexical priming), and the dynamics
of higher cognitive processes (including categorization, analogy,
and decision making).
Recent work in cognitive science, much of it placed in opposition
to a computational view of the mind, has argued that the concept of
representation and theories based on that concept are not
sufficient to explain the details of cognitive processing. These
attacks on representation have focused on the importance of context
sensitivity in cognitive processing, on the range of individual
differences in performance, and on the relationship between minds
and the bodies and environments in which they exist. In each case,
models based on traditional assumptions about representation have
been assumed to be too rigid to account for the effects of these
factors on cognitive processing. In place of a representational
view of mind, other formalisms and methodologies, such as nonlinear
differential equations (or dynamical systems) and situated
robotics, have been proposed as better explanatory tools for
understanding cognition. This book is based on the notion that,
while new tools and approaches for understanding cognition are
valuable, representational approaches do not need to be abandoned
in the course of constructing new models and explanations. Rather,
models that incorporate representation are quite compatible with
the kinds of complex situations being modeled with the new methods.
This volume illustrates the power of this explicitly
representational approach--labeled "cognitive dynamics"--in
original essays by prominent researchers in cognitive science. Each
chapter explores some aspect of the dynamics of cognitive
processing while still retaining representations as the centerpiece
of the explanations of the key phenomena. These chapters serve as
an existence proof that representation is not incompatible with the
dynamics of cognitive processing. The book is divided into sections
on foundational issues about the use of representation in cognitive
science, the dynamics of low level cognitive processes (such as
visual and auditory perception and simple lexical priming), and the
dynamics of higher cognitive processes (including categorization,
analogy, and decision making).
No matter how advanced the technology, there is always the human
factor involved - the power behind the technology. Interpreting
Remote Sensing Imagery: Human Factors draws together leading
psychologists, remote sensing scientists, and government and
industry scientists to consider the factors involved in expertise
and perceptual skill. This book covers the cognitive issues of
learning, perception, and expertise, the applied issues of display
design, interface design, software design, and mental workload
issues, and the practitioner's issues of workstation design, human
performance, and training. It tackles the intangibles of data
interpretation, based on information from experts who do the job.
You will learn: Information and perception What do experts perceive
in remote sensing and cartographic displays? Reasoning and
perception How do experts "see through" the data display to
understand its meaning and significance? Human-computer interaction
How do experts work with their displays and what happens when the
"fiddle" with them? Learning and training What are the milestones
in training development from novice to expert image interpreter?
Interpreting Remote Sensing Imagery: Human Factors breaks down the
mystery of what experts do when they interpret data, how they
learn, and what individual factors speed or impede training. Even
more importantly, it gives you the tools to train efficiently and
understand how the human factor impacts data interpretation.
Knowledge representation is fundamental to the study of mind. All
theories of psychological processing are rooted in assumptions
about how information is stored. These assumptions, in turn,
influence the explanatory power of theories. This book fills a gap
in the existing literature by providing an overview of types of
knowledge representation techniques and their use in cognitive
models.
Organized around types of representations, this book begins with a
discussion of the foundations of knowledge representation, then
presents discussions of different ways that knowledge
representation has been used. Both symbolic and connectionist
approaches to representation are discussed and a set of
recommendations about the way representations should be used is
presented. This work can be used as the basis for a course on
knowledge representation or can be read independently. It will be
useful to students of psychology as well as people in related
disciplines--computer science, philosophy, anthropology, and
linguistics--who want an introduction to techniques for knowledge
representation.
Knowledge representation is fundamental to the study of mind. All
theories of psychological processing are rooted in assumptions
about how information is stored. These assumptions, in turn,
influence the explanatory power of theories. This book fills a gap
in the existing literature by providing an overview of types of
knowledge representation techniques and their use in cognitive
models.
Organized around types of representations, this book begins with a
discussion of the foundations of knowledge representation, then
presents discussions of different ways that knowledge
representation has been used. Both symbolic and connectionist
approaches to representation are discussed and a set of
recommendations about the way representations should be used is
presented. This work can be used as the basis for a course on
knowledge representation or can be read independently. It will be
useful to students of psychology as well as people in related
disciplines--computer science, philosophy, anthropology, and
linguistics--who want an introduction to techniques for knowledge
representation.
The traditional structures of business and the workplace have
changed drastically in recent years. In response, companies have
continuously worked to improve their ability to generate new ideas
and bring them to market by removing the common constraints on the
processes of innovation. This movement of open innovation in the
workforce necessitates improvements in the arenas of idea
generation, evaluation, funding, and the presentation of these
ideas to the marketplace in new and exciting ways. Edited by Arthur
B. Markman, Open Innovation: Academic and Practical Perspectives on
the Journey from Idea to Market brings together the latest
scholarship on these elements with tried-and-true stories of
successful applications in real-world settings. Authors explore a
variety of new methods for improving idea development and
processes, making this book an invaluable resource for readers as
they learn how to create a supportive ecosystem for their employees
and co-workers.
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