... framework of cognitive psychology in favor of the framework of nonlinear dynamical systems theory. Van Orden et al. presented evidence that“purposive behavior originates in self-organized criticality ” (p. 333). Here, the authors show that Van Orden et al.’s analyses do not test their hypotheses. Further, the authors argue that a confirmation of Van Orden et al.’s hypotheses would not have constituted firm evidence in support of their framework. Finally, the absence of a specific model for how self-organized criticality produces the observed behavior makes it very difficult to derive testable predictions. The authors conclude that the proposed paradigm shift is presently unwarranted.

The remarkable successes of the physical sciences have been built on highly general quantitative laws, which serve as the basis for understanding an enormous variety of specific physical systems. How far is it possible to construct universal principles in the cognitive sciences, in terms of which specific aspects of perception, memory, or decision making might be modelled? Following Shepard (e.g., 1987), it is argued that some universal principles may be attainable in cognitive science. Here we propose two examples: The simplicity principle (which states that the cognitive system prefers patterns that provide simpler explanations of available data); and the scale-invariance principle, which states that many cognitive phenomena are independent of the scale of relevant underlying physical variables, such as time, space, luminance, or sound pressure. We illustrate how principles may be combined to explain specific cognitive processes by using these principles to derive SIMPLE, a formal model of memory for serial order (Brown, Neath & Chater, in press), and briefly mention some extensions to models of identification and categorization. We also consider the scope and limitations of universal laws in cognitive science.

1/f scaling has been observed throughout human physiology and behavior, but its origins and meaning remain a matter of debate. Some argue that it is a byproduct of ongoing processes in the brain or body and therefore of limited relevance to psychological theory. Others argue that 1/f scaling reflects a fundamental aspect of all physiological and cognitive functions, namely, that they emerge in the balance of independent versus interdependent component activities. In 4 experiments, series of key-press responses were used to test between these 2 alternative explanations. The critical design feature was to take 2 measures of each key-press response: reaction time and key-contact duration. These measures resulted in 2 parallel series of intrinsic fluctuations for each series of key-press responses. Intrinsic fluctuations exhibited 1/f scaling in both reaction times and key-contact durations, yet the 2 measures were uncorrelated with each other and separately perturbable. These and other findings indicate that 1/f scaling is too pervasive to be idiosyncratic and of limited relevance. It is instead argued that 1/f scaling reflects the coordinative, metastable basis of cognitive function.

We have questioned whether a complex behavior, such as fish swimming, can be better described quantitatively as a sequence of discrete events or states than with classical kinematic measures which can be compromised by inherent variability. Here, the different states, expressed as combinations of symbols, were defined on the basis of the animal’s location (A: periphery, and B: inner part of the aquarium) and speed (Fast and Slow). We observed that the distributions of time intervals spent in the successive states were not gaussian. Rather, they were fit by power laws associated with an underlying Lévy-like process which has more long intervals, primarily due to prolonged periods of relative inactivity. Furthermore, our data suggest that the swimming behavior can be attributed to interactions between two intrinsic systems. One is represented by the matrix of transition of probabilities between states and controls their sequential organization while the second, which is defined by interval distributions, determines the time spent in each state. This kinetic model detects subtle effects of low doses of neuroactive compounds, and identifies their specific locus of action. We propose that this paradigm can be ‡ Corresponding author. 233 234 P. Faure et al. applied to characterize normal behavior and its modifications by genetic or pharmacological manipulations.

Key words: aging • fitness • frailty • integrative approach • macroscopic state variable • mathematical modeling In "Help Wanted: Physiologists for Research on Aging", George Martin challenged the scientific community to find better means of tackling the aging of the whole organism. He wrote "Aging involves each and every organ system. Organ systems communicate with each other in order to maintain homeostasis. We need to understand how these diverse systems integrate their several languages (endocrine, paracrine, autocrine). In addition, we need to decipher how these communications change over the life course. Solving such enigmas requires complex systems research into issues such as nonlinear dynamic networks, information theory, chaos, and fractals (1). " He suggested that we meet this challenge through integrative physiology, and he outlined a detailed plan for how an aspiring scientist might do so, right down to a trip to the National Institute on Aging (NIA) to liberate some extramural funds. Although such initiatives would be most welcome, we would like to propose an additional means by which scientists can take into account the integrative response of the organism and its changes over time.

This collection of invited papers covers a lot of ground in its nearly 800 pages, so any review of reasonable length will necessarily be selective. However, there are a number of features that make the book as a whole a comparatively easy and thoroughly rewarding read. Multiauthor compendia of this kind are often disjointed, with very little uniformity from chapter to chapter in terms of breadth, depth, and format. Such is not the case here. Breadth and depth of treatment are surprisingly consistent, with coherent formats that often include both a little history of the field and some thoughts about the future. The volume has a very logical structure in which the chapters flow and follow on from each other in an orderly fashion. There are also many crossreferences between chapters, which allow the authors to build upon the foundation of one another’s work and eliminate redundancies. Specifically, the contents consist of 38 survey papers grouped into three parts: Fundamentals; Processes, Methods, and Resources; and Applications. Taken together, they provide both a comprehensive introduction to the field and a useful reference volume. In addition to the usual author and subject matter indices, there is a substantial

frequency spectrum of finite samples from the

License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Biological organisms have intrinsic control systems that act in response to internal and external stimuli maintaining homeostasis. Human heart rate is not regular and varies in time and such variability, also known as heart rate variability (HRV), is not random. HRV depends upon organism’s physiologic and/or pathologic state. Physicians are always interested in predicting patient’s risk of developing major and life-threatening complications. Understanding biological signals behavior helps to characterize patient’s state and might represent a step toward a better care. The main advantage of signals such as HRV indexes is that it can be calculated in real time in noninvasive manner, while all current biomarkers used in clinical practice are discrete and imply blood sample analysis. In this paper HRV linear and nonlinear indexes are reviewed and data from real patients are provided to show how these indexes might be used in clinical practice. 1. Complexity in Biological Signals Biological systems are complex systems; particularly, they are systems that are spatially and temporally complex, built from a dynamic web of interconnected feedback loops and marked by interdependence, pleiotropy, and redundancy [1].