Giuliano Matessi, Ricardo J. Matos, and Torben Dabelsteen
- Published in print:
- 2008
- Published Online:
- September 2008
- ISBN:
- 9780199216840
- eISBN:
- 9780191712043
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199216840.003.0003
- Subject:
- Biology, Animal Biology, Evolutionary Biology / Genetics
Communication allows individuals to share information and plays a central role in determining animal social behaviour. Animals live in social networks of multiple individuals connected by links ...
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Communication allows individuals to share information and plays a central role in determining animal social behaviour. Animals live in social networks of multiple individuals connected by links representing different interaction types. Signalling interactions form the base of the communication network (i.e., all conspecifics within signalling range) experienced by an individual and are particularly important for information exchange. Looking at interactions within a network has helped identify and explain the diverse signalling and receiving strategies adopted by animals, and may likewise help explain other social interactions. This chapter presents a network model which integrates the concepts of communication and social network. It illustrates how this model can affect information exchange in animal communities with different social structures and ecologies. Finally, it presents some concrete examples of the questions that arise and can be answered when looking at the behavioural ecology of birds from a network perspective.Less
Communication allows individuals to share information and plays a central role in determining animal social behaviour. Animals live in social networks of multiple individuals connected by links representing different interaction types. Signalling interactions form the base of the communication network (i.e., all conspecifics within signalling range) experienced by an individual and are particularly important for information exchange. Looking at interactions within a network has helped identify and explain the diverse signalling and receiving strategies adopted by animals, and may likewise help explain other social interactions. This chapter presents a network model which integrates the concepts of communication and social network. It illustrates how this model can affect information exchange in animal communities with different social structures and ecologies. Finally, it presents some concrete examples of the questions that arise and can be answered when looking at the behavioural ecology of birds from a network perspective.
Valerie Isham
- Published in print:
- 2005
- Published Online:
- September 2007
- ISBN:
- 9780198566540
- eISBN:
- 9780191718038
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566540.003.0002
- Subject:
- Mathematics, Probability / Statistics
This chapter provides an overview of stochastic models for epidemics, focusing on topics that have preoccupied researchers for the last 15 years, and identifying continuing challenges. After some ...
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This chapter provides an overview of stochastic models for epidemics, focusing on topics that have preoccupied researchers for the last 15 years, and identifying continuing challenges. After some historical background and a brief account of basic deterministic models for transmission of infectious diseases, the principles of stochastic modeling of epidemics in homogeneous populations are outlined. The chapter then discusses the complications that arise owing to heterogeneity of host population, of mixing within the population, and of the network among the population, due for example to its social or spatial structure. The chapter concludes with a brief discussion of statistical issues.Less
This chapter provides an overview of stochastic models for epidemics, focusing on topics that have preoccupied researchers for the last 15 years, and identifying continuing challenges. After some historical background and a brief account of basic deterministic models for transmission of infectious diseases, the principles of stochastic modeling of epidemics in homogeneous populations are outlined. The chapter then discusses the complications that arise owing to heterogeneity of host population, of mixing within the population, and of the network among the population, due for example to its social or spatial structure. The chapter concludes with a brief discussion of statistical issues.
D. Hugh Whittaker and Robert E. Cole
- Published in print:
- 2006
- Published Online:
- September 2007
- ISBN:
- 9780199297320
- eISBN:
- 9780191711237
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199297320.003.0017
- Subject:
- Business and Management, Innovation
This chapter attempts to tease out the implications of the individual chapters for the future of innovation and MOT in Japan, beginning with problems in the Japanese ‘knowledge-creating’ company ...
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This chapter attempts to tease out the implications of the individual chapters for the future of innovation and MOT in Japan, beginning with problems in the Japanese ‘knowledge-creating’ company model. The strengths of this model are also its weaknesses. In particular, Japanese companies have had difficulties in accessing external tacit knowledge and global knowledge networks. The emergence of a ‘dual innovation system’ is considered, which consists of a ‘reformed Japanese/large firm model’ and a ‘nascent network model’, both lying between closed and open innovation system model poles. Eight features of the former are identified. Policy, on the other hand, has become oriented toward promoting the latter, with limited success. Relations and tensions between the two are discussed.Less
This chapter attempts to tease out the implications of the individual chapters for the future of innovation and MOT in Japan, beginning with problems in the Japanese ‘knowledge-creating’ company model. The strengths of this model are also its weaknesses. In particular, Japanese companies have had difficulties in accessing external tacit knowledge and global knowledge networks. The emergence of a ‘dual innovation system’ is considered, which consists of a ‘reformed Japanese/large firm model’ and a ‘nascent network model’, both lying between closed and open innovation system model poles. Eight features of the former are identified. Policy, on the other hand, has become oriented toward promoting the latter, with limited success. Relations and tensions between the two are discussed.
M. E. J. Newman
- Published in print:
- 2010
- Published Online:
- September 2010
- ISBN:
- 9780199206650
- eISBN:
- 9780191594175
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199206650.003.0014
- Subject:
- Physics, Theoretical, Computational, and Statistical Physics
Generative network models model the mechanisms by which networks are created. The idea behind models such as these is to explore hypothesized generative mechanisms to see what structures they ...
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Generative network models model the mechanisms by which networks are created. The idea behind models such as these is to explore hypothesized generative mechanisms to see what structures they produce. If the structures are similar to those of networks observed in the real world, this suggests — though does not prove — that similar generative mechanisms may be at work in the real networks. This chapter examines the best-known example of a generative network model: the ‘preferential attachment’ model for the growth of networks with power-law degree distributions. Exercises are provided at the end of the chapter.Less
Generative network models model the mechanisms by which networks are created. The idea behind models such as these is to explore hypothesized generative mechanisms to see what structures they produce. If the structures are similar to those of networks observed in the real world, this suggests — though does not prove — that similar generative mechanisms may be at work in the real networks. This chapter examines the best-known example of a generative network model: the ‘preferential attachment’ model for the growth of networks with power-law degree distributions. Exercises are provided at the end of the chapter.
Thomas W. Valente
- Published in print:
- 2010
- Published Online:
- May 2010
- ISBN:
- 9780195301014
- eISBN:
- 9780199777051
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195301014.003.0012
- Subject:
- Public Health and Epidemiology, Epidemiology
This summary chapter reviews the models, methods, and applications that have been presented throughout the book. Network concepts have focused the research attention of many scientists and produced a ...
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This summary chapter reviews the models, methods, and applications that have been presented throughout the book. Network concepts have focused the research attention of many scientists and produced a rich body of research. In public health, virtually all chronic and infectious diseases are affected by networks and can profitably be studied using network tools. This summary chapter reiterates these points and provides some small empirical example which the reader can use to get started in this new, exciting and growing field. A glossary and long bibliography are provided.Less
This summary chapter reviews the models, methods, and applications that have been presented throughout the book. Network concepts have focused the research attention of many scientists and produced a rich body of research. In public health, virtually all chronic and infectious diseases are affected by networks and can profitably be studied using network tools. This summary chapter reiterates these points and provides some small empirical example which the reader can use to get started in this new, exciting and growing field. A glossary and long bibliography are provided.
Josh Whitford
- Published in print:
- 2005
- Published Online:
- September 2007
- ISBN:
- 9780199286010
- eISBN:
- 9780191713903
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199286010.001.0001
- Subject:
- Business and Management, Political Economy
American manufacturing is in crisis: the sector lost three million jobs between 2000 and 2003 as the American trade deficit shot to record highs. Manufacturers have increasingly decentralized ...
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American manufacturing is in crisis: the sector lost three million jobs between 2000 and 2003 as the American trade deficit shot to record highs. Manufacturers have increasingly decentralized productive responsibilities to armies of supplier firms, both domestically and abroad. Many have speculated as to whether or not manufacturing is even feasible in the United States, given the difficulties. This book examines the issues behind this crisis, looking at the emergence of a ‘new old economy’, in which relationships between firms have become much more important. It shows that discussion of this shift, in the media and in the academic literature, hits on the right issues — globalization, de-industrialization, and the outsourcing of production in marketized and in network relationships — but in an overly polarized way that obscures as much as it enlightens. Drawing on the results of interviews conducted with manufacturers in the American Upper Midwest, the book shows that the range of possibilities is more complex and contingent than is usually recognised. Highlighting heretofore unexamined elements of constraint, contradiction, and innovation that characterize contemporary network production models, the book shakes received understandings in economic and organizational sociology, comparative political economy, and economic geography to reveal ways in which the American economic development apparatus can be adjusted to better meet the challenges of a highly decentralized production regime.Less
American manufacturing is in crisis: the sector lost three million jobs between 2000 and 2003 as the American trade deficit shot to record highs. Manufacturers have increasingly decentralized productive responsibilities to armies of supplier firms, both domestically and abroad. Many have speculated as to whether or not manufacturing is even feasible in the United States, given the difficulties. This book examines the issues behind this crisis, looking at the emergence of a ‘new old economy’, in which relationships between firms have become much more important. It shows that discussion of this shift, in the media and in the academic literature, hits on the right issues — globalization, de-industrialization, and the outsourcing of production in marketized and in network relationships — but in an overly polarized way that obscures as much as it enlightens. Drawing on the results of interviews conducted with manufacturers in the American Upper Midwest, the book shows that the range of possibilities is more complex and contingent than is usually recognised. Highlighting heretofore unexamined elements of constraint, contradiction, and innovation that characterize contemporary network production models, the book shakes received understandings in economic and organizational sociology, comparative political economy, and economic geography to reveal ways in which the American economic development apparatus can be adjusted to better meet the challenges of a highly decentralized production regime.
Thomas W. Valente
- Published in print:
- 2010
- Published Online:
- May 2010
- ISBN:
- 9780195301014
- eISBN:
- 9780199777051
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195301014.003.0001
- Subject:
- Public Health and Epidemiology, Epidemiology
This chapter introduces the basic network concepts and then outlines some of the major research fronts and principles under study in the network analysis field. Major research advances include the ...
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This chapter introduces the basic network concepts and then outlines some of the major research fronts and principles under study in the network analysis field. Major research advances include the small world phenomenon, scale-free networks, influence versus selection, diffusion of innovations and behavior change, determining efficient network forms, agent based modeling and simulation, and algorithm development. Although the research topics are diverse and have been applied to a diverse set of phenomenon, they have all been fundamentally interested in connectedness and how the set of social relations can be studied. These various research topics shed some light on how these diverse areas of investigation have implications for studying human behavior, how people gain access to resources and jobs; and how social networks influence individual and system behavior.Less
This chapter introduces the basic network concepts and then outlines some of the major research fronts and principles under study in the network analysis field. Major research advances include the small world phenomenon, scale-free networks, influence versus selection, diffusion of innovations and behavior change, determining efficient network forms, agent based modeling and simulation, and algorithm development. Although the research topics are diverse and have been applied to a diverse set of phenomenon, they have all been fundamentally interested in connectedness and how the set of social relations can be studied. These various research topics shed some light on how these diverse areas of investigation have implications for studying human behavior, how people gain access to resources and jobs; and how social networks influence individual and system behavior.
W. K. Estes
- Published in print:
- 1994
- Published Online:
- January 2008
- ISBN:
- 9780195073355
- eISBN:
- 9780199867899
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195073355.003.0003
- Subject:
- Psychology, Cognitive Psychology
This chapter begins with a discussion of the exemplar-similarity model, which is useful for making predictions about categorization assuming that the current state of an individual's memory is known, ...
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This chapter begins with a discussion of the exemplar-similarity model, which is useful for making predictions about categorization assuming that the current state of an individual's memory is known, or that plausible assumptions about it can be made. However, this model lacks the machinery to address the dynamics of learning. Augmentations of the core model, categorization and identification, similarity and cognitive distance, status of the exemplar-similarity model, and network-based learning models are described.Less
This chapter begins with a discussion of the exemplar-similarity model, which is useful for making predictions about categorization assuming that the current state of an individual's memory is known, or that plausible assumptions about it can be made. However, this model lacks the machinery to address the dynamics of learning. Augmentations of the core model, categorization and identification, similarity and cognitive distance, status of the exemplar-similarity model, and network-based learning models are described.
M. E. J. Newman
- Published in print:
- 2010
- Published Online:
- September 2010
- ISBN:
- 9780199206650
- eISBN:
- 9780191594175
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199206650.003.0015
- Subject:
- Physics, Theoretical, Computational, and Statistical Physics
This chapter describes two of the best-known additional types of network models: the small-world model and exponential random graphs. Exercises are provided at the end of the chapter.
This chapter describes two of the best-known additional types of network models: the small-world model and exponential random graphs. Exercises are provided at the end of the chapter.
W. K. Estes
- Published in print:
- 1994
- Published Online:
- January 2008
- ISBN:
- 9780195073355
- eISBN:
- 9780199867899
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195073355.003.0004
- Subject:
- Psychology, Cognitive Psychology
The models for category learning discussed in the preceding chapters have been developed largely in conjunction with a standard experimental design in which a subject classifies category exemplars ...
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The models for category learning discussed in the preceding chapters have been developed largely in conjunction with a standard experimental design in which a subject classifies category exemplars with feedback during a learning phase, often running to hundreds of trials, then tested for transfer to new stimulus patterns of various types. This chapter describes efforts to modify the standard design in ways that may enable informative analyses pertaining to concepts of interference and organization in memory. Topics discussed include concurrent categorizations, categorization with constraints on memory, and exemplar and network models.Less
The models for category learning discussed in the preceding chapters have been developed largely in conjunction with a standard experimental design in which a subject classifies category exemplars with feedback during a learning phase, often running to hundreds of trials, then tested for transfer to new stimulus patterns of various types. This chapter describes efforts to modify the standard design in ways that may enable informative analyses pertaining to concepts of interference and organization in memory. Topics discussed include concurrent categorizations, categorization with constraints on memory, and exemplar and network models.
Thomas R. Shultz
- Published in print:
- 2010
- Published Online:
- January 2011
- ISBN:
- 9780195366709
- eISBN:
- 9780199863969
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195366709.003.0007
- Subject:
- Psychology, Developmental Psychology, Vision
This chapter concerns the computational modeling of one of Les Cohen's most important discoveries in infant information processing—the developmental shift from learning about visual stimulus features ...
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This chapter concerns the computational modeling of one of Les Cohen's most important discoveries in infant information processing—the developmental shift from learning about visual stimulus features to learning about correlations between these features. It describes the theoretical origins of this work, and reviews the relevant psychology experiments and the several attempts to simulate it with artificial neural networks, before presenting a new neural network model. The modeling suggests a somewhat different explanation than originally proposed, based on depth of learning rather than qualitative shifts in learning strategies. Computational models of this shift may be equally relevant to several other documented developmental shifts from learning about stimulus elements to learning about relations between those elements.Less
This chapter concerns the computational modeling of one of Les Cohen's most important discoveries in infant information processing—the developmental shift from learning about visual stimulus features to learning about correlations between these features. It describes the theoretical origins of this work, and reviews the relevant psychology experiments and the several attempts to simulate it with artificial neural networks, before presenting a new neural network model. The modeling suggests a somewhat different explanation than originally proposed, based on depth of learning rather than qualitative shifts in learning strategies. Computational models of this shift may be equally relevant to several other documented developmental shifts from learning about stimulus elements to learning about relations between those elements.
M. E. J. Newman
- Published in print:
- 2010
- Published Online:
- September 2010
- ISBN:
- 9780199206650
- eISBN:
- 9780191594175
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199206650.003.0013
- Subject:
- Physics, Theoretical, Computational, and Statistical Physics
The previous chapter looked at the classic random graph model, in which pairs of vertices are connected at random with uniform probabilities. Although this model has proved tremendously useful as a ...
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The previous chapter looked at the classic random graph model, in which pairs of vertices are connected at random with uniform probabilities. Although this model has proved tremendously useful as a source of insight into the structure of networks, it also has a number of serious shortcomings. Chief among these is its degree distribution, which follows the Poisson distribution which is quite different from the degree distributions seen in most real-world networks. This chapter shows how to create more sophisticated random graph models, which incorporate arbitrary degree distributions and yet are still exactly solvable for many of their properties in the limit of large network size. The fundamental mathematical tool used to derive the results of this chapter is the probability generating function. Exercises are provided at the end of the chapter.Less
The previous chapter looked at the classic random graph model, in which pairs of vertices are connected at random with uniform probabilities. Although this model has proved tremendously useful as a source of insight into the structure of networks, it also has a number of serious shortcomings. Chief among these is its degree distribution, which follows the Poisson distribution which is quite different from the degree distributions seen in most real-world networks. This chapter shows how to create more sophisticated random graph models, which incorporate arbitrary degree distributions and yet are still exactly solvable for many of their properties in the limit of large network size. The fundamental mathematical tool used to derive the results of this chapter is the probability generating function. Exercises are provided at the end of the chapter.
W. K. Estes
- Published in print:
- 1994
- Published Online:
- January 2008
- ISBN:
- 9780195073355
- eISBN:
- 9780199867899
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195073355.001.0001
- Subject:
- Psychology, Cognitive Psychology
Based on the author's important Fitts Lectures, this book details a set of psychological concepts and principles that offers a unified interpretation of a wide variety of memory, categorization, and ...
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Based on the author's important Fitts Lectures, this book details a set of psychological concepts and principles that offers a unified interpretation of a wide variety of memory, categorization, and decision-making phenomena. These phenomena are explained via two families of models established by the book: a storage-retrieval model and an adaptive network model. The book considers whether the models are competing or complementary, offering cogent and instructive arguments for both perspectives. The book's theory is then applied to two large-scale series of studies on category learning and recognition, providing an integrated understanding of seemingly disparate phenomena. This book is the culmination of more than ten years research in the field.Less
Based on the author's important Fitts Lectures, this book details a set of psychological concepts and principles that offers a unified interpretation of a wide variety of memory, categorization, and decision-making phenomena. These phenomena are explained via two families of models established by the book: a storage-retrieval model and an adaptive network model. The book considers whether the models are competing or complementary, offering cogent and instructive arguments for both perspectives. The book's theory is then applied to two large-scale series of studies on category learning and recognition, providing an integrated understanding of seemingly disparate phenomena. This book is the culmination of more than ten years research in the field.
PETER TILLERS
- Published in print:
- 2011
- Published Online:
- January 2013
- ISBN:
- 9780197264843
- eISBN:
- 9780191754050
- Item type:
- chapter
- Publisher:
- British Academy
- DOI:
- 10.5871/bacad/9780197264843.003.0009
- Subject:
- Sociology, Methodology and Statistics
This chapter discusses the limitations of approaches to modelling and handling evidential issues using hierarchical network representations. Such models of evidential inference rest on the compound ...
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This chapter discusses the limitations of approaches to modelling and handling evidential issues using hierarchical network representations. Such models of evidential inference rest on the compound proposition that real-world evidential inference usually or always consists of propositional ‘atoms’ (i.e. relatively granular propositional statements about states of the world) that are linked together by nomological entities of some kind, entities that are often — but not always — called ‘generalisations’. These sorts of models or representations of evidential inference are referred to as ‘network-and-generalisation’ models of evidential inference. It is argued that for certain important problems, especially where these concern meaning and human understanding, these need to be complemented by other methods.Less
This chapter discusses the limitations of approaches to modelling and handling evidential issues using hierarchical network representations. Such models of evidential inference rest on the compound proposition that real-world evidential inference usually or always consists of propositional ‘atoms’ (i.e. relatively granular propositional statements about states of the world) that are linked together by nomological entities of some kind, entities that are often — but not always — called ‘generalisations’. These sorts of models or representations of evidential inference are referred to as ‘network-and-generalisation’ models of evidential inference. It is argued that for certain important problems, especially where these concern meaning and human understanding, these need to be complemented by other methods.
W. K. Estes
- Published in print:
- 1994
- Published Online:
- January 2008
- ISBN:
- 9780195073355
- eISBN:
- 9780199867899
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195073355.003.0007
- Subject:
- Psychology, Cognitive Psychology
This chapter broadens the scope of theory developed in Chapters 1-6 to include many of the phenomena traditionally treated in memory research and theory. It begins with what is believed to be the ...
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This chapter broadens the scope of theory developed in Chapters 1-6 to include many of the phenomena traditionally treated in memory research and theory. It begins with what is believed to be the most nearly basic index of memory, that is, recognition, and takes up the idea from a much earlier period — that a close study of recognition may yield dividends in helping us to understand apparently more complex forms of memory. Topics discussed include recognition as a window to memory, recognition in the array framework, recognition in the similarity-network model, short-term memory search, and recognition as a measure of memory.Less
This chapter broadens the scope of theory developed in Chapters 1-6 to include many of the phenomena traditionally treated in memory research and theory. It begins with what is believed to be the most nearly basic index of memory, that is, recognition, and takes up the idea from a much earlier period — that a close study of recognition may yield dividends in helping us to understand apparently more complex forms of memory. Topics discussed include recognition as a window to memory, recognition in the array framework, recognition in the similarity-network model, short-term memory search, and recognition as a measure of memory.
M. E. J. Newman
- Published in print:
- 2010
- Published Online:
- September 2010
- ISBN:
- 9780199206650
- eISBN:
- 9780191594175
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199206650.003.0012
- Subject:
- Physics, Theoretical, Computational, and Statistical Physics
This chapter discusses the basic mathematics of the random graph G(n, p), focusing particularly on the degree distribution and component sizes, which are two of the model's most illuminating ...
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This chapter discusses the basic mathematics of the random graph G(n, p), focusing particularly on the degree distribution and component sizes, which are two of the model's most illuminating characteristics. The techniques developed in this chapter prove useful for some of the more complex models examined later in the book. Exercises are provided at the end of the chapter.Less
This chapter discusses the basic mathematics of the random graph G(n, p), focusing particularly on the degree distribution and component sizes, which are two of the model's most illuminating characteristics. The techniques developed in this chapter prove useful for some of the more complex models examined later in the book. Exercises are provided at the end of the chapter.
Mary Coleman
- Published in print:
- 2005
- Published Online:
- September 2007
- ISBN:
- 9780195182224
- eISBN:
- 9780199786701
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195182224.003.0002
- Subject:
- Psychology, Cognitive Neuroscience
This chapter examines the components of the impaired neural networks that might underlie the presentation of autistic symptoms. Topics covered include the neural network models of autism, central ...
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This chapter examines the components of the impaired neural networks that might underlie the presentation of autistic symptoms. Topics covered include the neural network models of autism, central nervous system ontology, candidate regions in autism, trouble at the cellular level, trouble with neurotransmitters, trouble with circuitry in autism, and trouble with myelination of neural networks. It is shown that the autistic syndromes may be considered as one extensive set of impaired final common circuits presenting with dysfunctional information processing of behavior and cognition in very young children. Deficits in pragmatics, linguistic abilities, mindreading, executive functions, episodic memory, self-awareness, central coherence, and affective processing have been documented. These deficits are caused by many disease entities whose shared symptoms likely occur owing to malfunction of certain distributed neural networks.Less
This chapter examines the components of the impaired neural networks that might underlie the presentation of autistic symptoms. Topics covered include the neural network models of autism, central nervous system ontology, candidate regions in autism, trouble at the cellular level, trouble with neurotransmitters, trouble with circuitry in autism, and trouble with myelination of neural networks. It is shown that the autistic syndromes may be considered as one extensive set of impaired final common circuits presenting with dysfunctional information processing of behavior and cognition in very young children. Deficits in pragmatics, linguistic abilities, mindreading, executive functions, episodic memory, self-awareness, central coherence, and affective processing have been documented. These deficits are caused by many disease entities whose shared symptoms likely occur owing to malfunction of certain distributed neural networks.
Manuel Castells
- Published in print:
- 2002
- Published Online:
- September 2011
- ISBN:
- 9780199255771
- eISBN:
- 9780191698279
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199255771.003.0004
- Subject:
- Business and Management, Information Technology
This chapter defines e-Business as any business activity whose performance of the key operations of management, financing innovation, production, distribution, sales, employee relations, and customer ...
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This chapter defines e-Business as any business activity whose performance of the key operations of management, financing innovation, production, distribution, sales, employee relations, and customer relations predominantly takes place by or on the Internet or other networks of computer networks regardless of the kind of connection between the virtual and the physical dimensions of the firm. It evaluates the transformation of the practice of the firm, the relationship between the Internet and capital markets, the role of work and flexible employment practices in the networking business model, and the specificity of innovation in the economy, at the source of labour productivity growth. It also proposes some hypotheses regarding the characteristics of the new business cycle and of potential crises, prompted by a sharp downturn in the value of technology stocks in financial markets, based on the observation of the period from March 2000 to March 2001.Less
This chapter defines e-Business as any business activity whose performance of the key operations of management, financing innovation, production, distribution, sales, employee relations, and customer relations predominantly takes place by or on the Internet or other networks of computer networks regardless of the kind of connection between the virtual and the physical dimensions of the firm. It evaluates the transformation of the practice of the firm, the relationship between the Internet and capital markets, the role of work and flexible employment practices in the networking business model, and the specificity of innovation in the economy, at the source of labour productivity growth. It also proposes some hypotheses regarding the characteristics of the new business cycle and of potential crises, prompted by a sharp downturn in the value of technology stocks in financial markets, based on the observation of the period from March 2000 to March 2001.
Jamshed J. Bharucha, Kaivon Paroo, and Meagan Curtis
- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780199553426
- eISBN:
- 9780191731020
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199553426.003.0020
- Subject:
- Psychology, Music Psychology, Cognitive Psychology
This chapter presents a response to the commentaries in Chapters 17–19. It focuses on the commentary written by Wiggins, who provides a spirited critique of network models, and the MUSACT model in ...
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This chapter presents a response to the commentaries in Chapters 17–19. It focuses on the commentary written by Wiggins, who provides a spirited critique of network models, and the MUSACT model in particular — drawing extensively on the philosophy of science.Less
This chapter presents a response to the commentaries in Chapters 17–19. It focuses on the commentary written by Wiggins, who provides a spirited critique of network models, and the MUSACT model in particular — drawing extensively on the philosophy of science.
Bruno G. Breitmeyer and Haluk ÖĞmen
- Published in print:
- 2006
- Published Online:
- April 2010
- ISBN:
- 9780198530671
- eISBN:
- 9780191728204
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198530671.003.0004
- Subject:
- Psychology, Cognitive Psychology
This chapter presents models and mechanisms of masking according to five distinguishing characteristics: (i) models based on spatiotemporal response sequences; (ii) models adopting some version of an ...
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This chapter presents models and mechanisms of masking according to five distinguishing characteristics: (i) models based on spatiotemporal response sequences; (ii) models adopting some version of an overtake hypothesis; (iii) models based on two separate neural processes or channel activations; (iv) models relying on stimulus or object substitution; and (v) models based on emergent properties of distributed neural networks. The models discussed in detail include spatiotemporal sequence models (Kahneman's impossible motion model, Matin's three-neuron model, and Burr's spatiotemporal receptive field model), two-process models (Ganz's interactive trace decay and random encoding time model, Reeves' temporal integration and segregation model, and Navon and Purcell's integration and interruption model), neural network models (Bridgeman's Hartline–Ratliff inhibitory network, Weisstein's Rashevsky–Landahl two-factor neural network, perceptual retouch model, and Boundary Contour System (BCS) model), and object substitution models.Less
This chapter presents models and mechanisms of masking according to five distinguishing characteristics: (i) models based on spatiotemporal response sequences; (ii) models adopting some version of an overtake hypothesis; (iii) models based on two separate neural processes or channel activations; (iv) models relying on stimulus or object substitution; and (v) models based on emergent properties of distributed neural networks. The models discussed in detail include spatiotemporal sequence models (Kahneman's impossible motion model, Matin's three-neuron model, and Burr's spatiotemporal receptive field model), two-process models (Ganz's interactive trace decay and random encoding time model, Reeves' temporal integration and segregation model, and Navon and Purcell's integration and interruption model), neural network models (Bridgeman's Hartline–Ratliff inhibitory network, Weisstein's Rashevsky–Landahl two-factor neural network, perceptual retouch model, and Boundary Contour System (BCS) model), and object substitution models.
