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820
The structure and function of complex networks
 SIAM REVIEW
, 2003
"... Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, ..."
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Cited by 2578 (7 self)
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Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the smallworld effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
A neuropsychological theory of multiple systems in category learning
 PSYCHOLOGICAL REVIEW
, 1998
"... A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedurallearningbased) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior ci ..."
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Cited by 328 (30 self)
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A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedurallearningbased) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior cingulate and prefrontal cortices are critical to the verbal system. In addition to making predictions for normal human adults, the theory makes specific predictions for children, elderly people, and patients suffering from Parkinson's disease, Huntington's disease, major depression, amnesia, or lesions of the prefrontal cortex. Two separate formal descriptions of the theory are also provided. One describes trialbytrial learning, and the other describes global dynamics. The theory is tested on published neuropsychological data and on category learning data with normal adults.
Contraction Analysis of Nonlinear Systems
, 1999
"... Analyzing stability differentially leads to a new perspective on nonlinear dynamic systems Winfried Lohmiller a a, b ..."
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Cited by 217 (55 self)
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Analyzing stability differentially leads to a new perspective on nonlinear dynamic systems Winfried Lohmiller a a, b
The dynamics of active categorical perception in an evolved model agent
 ADAPTIVE BEHAVIOR
, 2003
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The simplest walking model: Stability, complexity, and scaling
 ASME Journal of Biomechanical Engineering
, 1998
"... We demonstrate that an irreducibly simple, uncontrolled, 2D, twolink model, vaguely resembling human legs, can walk down a shallow slope, powered only by gravity. This model is the simplest special case of the passivedynamic models pioneered by McGeer (1990a). It has two rigid massless legs hinged ..."
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Cited by 147 (8 self)
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We demonstrate that an irreducibly simple, uncontrolled, 2D, twolink model, vaguely resembling human legs, can walk down a shallow slope, powered only by gravity. This model is the simplest special case of the passivedynamic models pioneered by McGeer (1990a). It has two rigid massless legs hinged at the hip, a pointmass at the hip, and infinitesimal pointmasses at the feet. The feet have plastic (noslip, nobounce) collisions with the slope surface, except during forward swinging, when geometric interference (foot scuffing) is ignored. After nondimensionalizing the governing equations, the model has only one free parameter, the ramp slope γ. This model shows stable walking modes similar to more elaborate models, but allows some use of analytic methods to study its dynamics. The analytic calculations find initial conditions and stability estimates for periodone gait limit cycles. The model exhibits two periodone gait cycles, one of which is stable when 0 <γ<0.015 rad. With increasing γ, stable cycles of higher periods appear, and the walkinglike motions apparently become chaotic through a sequence of period doublings. Scaling laws for the model predict that walking speed is proportional to stance angle, stance angle is proportional to γ 1/3, and that the gravitational power used is proportional to v 4 where v is the velocity along the slope. 1 1
An introduction to collective intelligence
 Handbook of Agent technology. AAAI
, 1999
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SWIFT: a dynamical model of saccade generation during reading
 Psychological review
, 2005
"... Mathematical models have become an important tool for understanding the control of eye movements during reading. Main goals of the development of the SWIFT model (R. Engbert, A. Longtin, & R. Kliegl, 2002) were to investigate the possibility of spatially distributed processing and to implement ..."
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Cited by 104 (14 self)
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Mathematical models have become an important tool for understanding the control of eye movements during reading. Main goals of the development of the SWIFT model (R. Engbert, A. Longtin, & R. Kliegl, 2002) were to investigate the possibility of spatially distributed processing and to implement a general mechanism for all types of eye movements observed in reading experiments. The authors present an advanced version of SWIFT that integrates properties of the oculomotor system and effects of word recognition to explain many of the experimental phenomena faced in reading research. They propose new procedures for the estimation of model parameters and for the test of the model’s performance. They also present a mathematical analysis of the dynamics of the SWIFT model. Finally, within this framework, they present an analysis of the transition from parallel to serial processing. In modern society, reading is a central skill, which demonstrates how efficiently a range of different cognitive processes (e.g., visual information processing, word recognition, attention, oculomotor control) can work together to perform a complex everyday task. Consequently, a full account of how we read is among the crucial problems of cognitive research. Here, we focus on the fact
Meanfield solution of the smallworld network model
, 2000
"... The smallworld network model is a simple model of the structure of social networks, which simultaneously possesses characteristics of both regular lattices and random graphs. The model consists of a onedimensional lattice with a low density of shortcuts added between randomly selected pairs of poi ..."
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Cited by 95 (6 self)
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The smallworld network model is a simple model of the structure of social networks, which simultaneously possesses characteristics of both regular lattices and random graphs. The model consists of a onedimensional lattice with a low density of shortcuts added between randomly selected pairs of points. These shortcuts greatly reduce the typical path length between any two points on the lattice. We present a meanfield solution for the average path length and for the distribution of path lengths in the model. This solution is exact in the limit of large system size and either large or small number of shortcuts. 1 Social networks, such as networks of friends, have two characteristics which one might imagine were contradictory. First, they show “clustering, ” meaning that two of your friends are far more likely also to be friends of one another than two people chosen from the population at random. Second, they exhibit what has become known as the “smallworld effect,” namely that any two people can establish contact by going through only a short chain of
W.: Behavioral dynamics of steering, obstacle avoidance, and route selection
 Journal of Experimental Psycholology: Human Perception Performance
, 2003
"... The authors investigated the dynamics of steering and obstacle avoidance, with the aim of predicting routes through complex scenes. Participants walked in a virtual environment toward a goal (Experiment 1) and around an obstacle (Experiment 2) whose initial angle and distance varied. Goals and obsta ..."
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Cited by 79 (5 self)
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The authors investigated the dynamics of steering and obstacle avoidance, with the aim of predicting routes through complex scenes. Participants walked in a virtual environment toward a goal (Experiment 1) and around an obstacle (Experiment 2) whose initial angle and distance varied. Goals and obstacles behave as attractors and repellers of heading, respectively, whose strengths depend on distance. The observed behavior was modeled as a dynamical system in which angular acceleration is a function of goal and obstacle angle and distance. By linearly combining terms for goals and obstacles, one could predict whether participants adopt a route to the left or right of an obstacle to reach a goal (Experiment 3). Route selection may emerge from online steering dynamics, making explicit path planning unnecessary. How do humans and other animals locomote effortlessly through a complex environment, steering toward goals, avoiding obstacles, and adopting particular routes through the cluttered landscape? This basic problem has inspired a good deal of research on the optical information available to a moving observer and the visual perception of selfmotion (Gibson, 1958/1998; Land, 1998; Lee, 1980; Warren, in press). At the same time, research on motor coordination has demonstrated that the action system generates stable movement patterns and qualitative transitions that can be characterized using concepts from nonlinear dynamics (Kelso,
A GameTheoretic Approach to the Simple Coevolutionary Algorithm
 Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature (PPSN VI
"... The fundamental distinction between ordinary evolutionary algorithms (EA) and coevolutionary algorithms lies in the interaction between coevolving entities. We believe that this property is essentially gametheoretic in nature. Using game theory, we describe extensions that allow familiar mixingma ..."
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Cited by 58 (8 self)
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The fundamental distinction between ordinary evolutionary algorithms (EA) and coevolutionary algorithms lies in the interaction between coevolving entities. We believe that this property is essentially gametheoretic in nature. Using game theory, we describe extensions that allow familiar mixingmatrix and Markovchain models of EAs to address coevolutionary algorithm dynamics. We then employ concepts from evolutionary game theory to examine design aspects of conventional coevolutionary algorithms that are poorly understood.