Results 1 - 10
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125
Self-organization of cognitive performance
- Journal of Experimental Psychology: General
, 2003
"... Background noise is the irregular variation across repeated measurements of human performance. Background noise remains after task and treatment effects are minimized. Background noise refers to intrinsic sources of variability, the intrinsic dynamics of mind and body, and the internal workings of a ..."
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Cited by 86 (14 self)
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Background noise is the irregular variation across repeated measurements of human performance. Background noise remains after task and treatment effects are minimized. Background noise refers to intrinsic sources of variability, the intrinsic dynamics of mind and body, and the internal workings of a living being. Two experiments demonstrate 1/f scaling (pink noise) in simple reaction times and speeded word naming times, which round out a catalog of laboratory task demonstrations that background noise is pink noise. Ubiquitous pink noise suggests processes of mind and body that change each other’s dynamics. Such interaction-dominant dynamics are found in systems that self-organize their behavior. Self-organization provides an unconventional perspective on cognition, but this perspective closely parallels a contemporary interdisciplinary view of living systems. Psychological science usually ignores the background noise in behavioral data. Background noise is what is left over when task demands, experimental manipulations, and other external sources of variability have been eliminated or minimized. What we call background noise is treated as random variability in most research, the nuisance factor in factorial experiments. We argue, to the
Hierarchy and scaling: Extrapolating information along a scaling ladder
- Canadian Journal of Remote Sensing
, 1999
"... The large number of components, nonlinear interactions, time delays and feedbacks, and spatial heterogeneity together often make ecological systems overwhelmingly complex. This complexity must be effectively dealt with for understanding and scaling. Hierarchy theory suggests that ecological systems ..."
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Cited by 67 (16 self)
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The large number of components, nonlinear interactions, time delays and feedbacks, and spatial heterogeneity together often make ecological systems overwhelmingly complex. This complexity must be effectively dealt with for understanding and scaling. Hierarchy theory suggests that ecological systems are nearly completely decomposable (or nearly decomposable) systems because of their loose vertical and horizontal coupling in structure and function. Such systems can thus be simplified based on the principle of time-space decomposition. Patch dynamics provides a powerful way of dealing explicitly with spatial heterogeneity, and has emerged as a unifying
A spatially explicit hierarchical approach to modeling complex ecological systems: theory and applications, Ecological Modeling
, 2002
"... and applications ..."
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From Local to Global SDI initiatives: a pyramid of building blocks
- 4th Global Spatial Data Infrastructure Conference, Cape Town, South Africa
, 2000
"... There is a strong hierarchical relationship among different political/administrative levels of SDI concepts. Based on this hierarchical relationship a pyramid of building blocks can be formed by taking a perspective that starts at a local level and proceeds through state, national and regional level ..."
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Cited by 54 (27 self)
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There is a strong hierarchical relationship among different political/administrative levels of SDI concepts. Based on this hierarchical relationship a pyramid of building blocks can be formed by taking a perspective that starts at a local level and proceeds through state, national and regional levels and is completed by developing a Global Spatial Data Infrastructure (GSDI). Therefore, it is argued that by better understanding and demonstrating the nature of this hierarchical relationship, GSDI development can gain support from a wider community of both government and non-government data users and providers. The aim of this paper is to contribute to GSDI development in general and its organisational framework in particular. This is achieved by: a) Reviewing the results of a comparative study between different levels of SDIs; and b) Demonstrating a relationship between different components within an SDI, in comparison with other levels of SDIs. Keywords: Spatial Data Infrastructure,...
Spring). Motivating the notion of generic design within information-processing theory: The design problem space.
- AI Magazine,
, 1989
"... ..."
Hierarchical SelfOrganization in Genetic Programming
- In Proceedings of the Eleventh International Conference on Machine Learning (ICML-94
, 1994
"... ..."
A Global Map of Science Based on the ISI Subject Categories
, 2007
"... The ISI subject categories classify journals included in the Science Citation Index (SCI). The aggregated journal-journal citation matrix contained in the Journal Citation Reports can be aggregated on the basis of these categories. This leads to an asymmetrical transaction matrix (citing versus cite ..."
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Cited by 45 (11 self)
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The ISI subject categories classify journals included in the Science Citation Index (SCI). The aggregated journal-journal citation matrix contained in the Journal Citation Reports can be aggregated on the basis of these categories. This leads to an asymmetrical transaction matrix (citing versus cited) which is much more densely populated than the underlying matrix at the journal level. Exploratory factor analysis leads us to opt for a fourteen-factor solution. This solution can easily be interpreted as the disciplinary structure of science. The nested maps of science (corresponding to 14 factors, 172 categories, and 6,164 journals) are brought online at
Betweenness Centrality” as an Indicator of the “Interdisciplinarity” of Scientific Journals
- Journal of the American Society for Information Science and Technology
, 2006
"... In addition to science citation indicators of journals like impact and immediacy, social network analysis provides a set of centrality measures like degree, betweenness, and closeness centrality. These measures are first analyzed for the entire set of 7,379 journals included in the Journal Citation ..."
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Cited by 45 (10 self)
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In addition to science citation indicators of journals like impact and immediacy, social network analysis provides a set of centrality measures like degree, betweenness, and closeness centrality. These measures are first analyzed for the entire set of 7,379 journals included in the Journal Citation Reports of the Science Citation Index and the Social Sciences Citation Index 2004, and then also in relation to local citation environments which can be considered as proxies of specialties and disciplines. Betweenness centrality is shown to be an indicator of the interdisciplinarity of journals, but only in local citation environments and after normalization because otherwise the influence of degree centrality (size) overshadows the betweenness-centrality measure. The indicator is applied to a variety of citation environments, including policy-relevant ones like biotechnology and nanotechnology. The values of the indicator remain sensitive to the delineations of the set because of the indicator’s local character. Maps showing 1 interdisciplinarity of journals in terms of betweenness centrality can be drawn using information about journal citation environments which is available online.
Causality in Genetic Programming
- Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95
, 1995
"... Causality relates changes in the structure of an object with the effects of such changes, that is changes in the properties or behavior of the object. This paper analyzes the concept of causality in Genetic Programming (GP) and suggests how it can be used in adapting control parameters for speeding ..."
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Cited by 41 (6 self)
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Causality relates changes in the structure of an object with the effects of such changes, that is changes in the properties or behavior of the object. This paper analyzes the concept of causality in Genetic Programming (GP) and suggests how it can be used in adapting control parameters for speeding up GP search. We first analyze the effects of crossover to show the weak causality of the GP representation and operators. Hierarchical GP approaches based on the discovery and evolution of functions amplify this phenomenon. However, selection gradually retains strongly causal changes. Causality is correlated to search space exploitation and is discussed in the context of the exploration-exploitation tradeoff. The results described argue for a bottom-up GP evolutionary thesis. Finally, new developments based on the idea of GP architecture evolution (Koza, 1994a) are discussed from the causality perspective. Proceedings of the Fifth International Conference (ICGA95) Morgan Kaufmann, San Franc...
Content-based and Algorithmic Classifications of Journals: Perspectives on the
- Dynamics of Scientific Communication and Indexer Effects Journal of the American Society for Information Science and Technology, In print; DOI: 10.1002/asi.21086
, 2009
"... The aggregated journal-journal citation matrix—based on the Journal Citation Reports (JCR) of the Science Citation Index—can be decomposed by indexers and/or algorithmically. In this study, we test the results of two recently available algorithms for the decomposition of large matrices against two c ..."
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Cited by 38 (23 self)
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The aggregated journal-journal citation matrix—based on the Journal Citation Reports (JCR) of the Science Citation Index—can be decomposed by indexers and/or algorithmically. In this study, we test the results of two recently available algorithms for the decomposition of large matrices against two content-based classifications of journals: the ISI Subject Categories and the field/subfield classification of Glänzel & Schubert (2003). The content-based schemes allow for the attribution of more than a single category to a journal, whereas the algorithms maximize the ratio of within-category citations over between-category citations in the aggregated category-category citation matrix. By adding categories, indexers generate between-category citations, which may enrich the database, for example, in the case of inter-disciplinary developments. The consequent indexer effects are significant in sparse areas of the matrix more than in denser ones. Algorithmic decompositions, on the other hand, are more heavily skewed towards a relatively small number of categories, while this is deliberately counter-acted upon in the case of content-based classifications. Because of the indexer effects, science policy studies and the sociology of science should be careful when using content-based classifications, which are made for bibliographic disclosure, and not for the purpose of analyzing latent structures in scientific communications. Despite the large differences among them, the four classification schemes enable us to generate surprisingly similar maps of science at the global level. Erroneous classifications are cancelled as noise at the aggregate level, but may disturb the evaluation locally.
