I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

The majority of functional neuroscience studies have focused on the brain's response to a task or stimulus. However, the brain is very active even in the absence of explicit input or output. In this Article we review recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity. Although several challenges remain, these studies have provided insight into the intrinsic functional architecture of the brain, variability in behaviour and potential physiological correlates of neurological and psychiatric disease.

/pdf/spontaneous-fluctuations-in-brain-activity-observed-with-32z1nyfc7n.pdf

Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging.

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components - not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.

/pdf/perceptual-symbol-systems-4spsk97r61.pdf

Perceptual symbol systems.

One of the strengths of scientific inquiry is that it can progress with any mixture of empiricism, intuition, and formal theory that suits the convenience of the investigator. Many sciences develop for a time as exercises in description and empirical generalization. Only later do they acquire reasoned connections within themselves and with other branches of knowledge. Many things were scientifically known of human anatomy and the motions of the planets before they were scientifically explained.

The psychological foundations of culture.

The task of quantizing general relativity raises serious questions about the meaning of the present formulation and interpretation of quantum mechanics when applied to so fundamental a structure as the space-time geometry itself. This paper seeks to clarify the foundations of quantum mechanics. It presents a reformulation of quantum theory in a form believed suitable for application to general relativity. The aim is not to deny or contradict the conventional formulation of quantum theory, which has demonstrated its usefulness in an overwhelming variety of problems, but rather to supply a new, more general and complete formulation, from which the conventional interpretation can be deduced. The relationship of this new formulation to the older formulation is therefore that of a metatheory to a theory, that is, it is an underlying theory in which the nature and consistency, as well as the realm of applicability, of the older theory can be investigated and clarified.

"relative State" Formulation of Quantum Mechanics

When a person attempts to produce from memory a given spatial or temporal interval, there is inevitably some error associated with the estimate. The time course of this error was measured in a series of experiments where subjects repeatedly attempted to replicate given target intervals. Sequences of the errors in both spatial and temporal replications were found to fluctuate as 1/f noises. 1/f noise is encountered in a wide variety of physical systems and is theorized to be a characteristic signature of complexity.

http://science.sciencemag.org/content/sci/267/5205/1837.full.pdf

1/f noise in human cognition

The residual fluctuations that naturally arise in experimental inquiry are analyzed in terms of their time histories. Although these fluctuations are generally relegated to a statistical purgatory known as unexplained variance, this article shows that they may harbor a long-term memory process known as 1/f noise. This type of noise has been encountered in a number of biological and physical systems and is theorized to be a signature of dynamic complexity. Its presence in psychological data appears to be associated with the most elementary aspect of cognitive process, the formation of representations.

Cognitive emissions of 1/f noise

When making dynamical judgments, people can make effective use of only one salient dimension of information present in the event. People do not make dynamical judgments by deriving multidimensional quantities. The adequacy of dynamical judgments, therefore, depends on the degree of dimensionality that is both inherent in the physics of the event and presumed to be present by the observer. There are two classes of physical motion contexts in which objects may appear. In the simplest class, there exists only one dynamically relevant object parameter: the position over time of the object's center of mass. In the other class of motion contexts, there are additional object attributes, such as mass distribution and orientation, that are of dynamical relevance. In the former class, objects may be formally treated as extensionless point particles, whereas in the latter class some aspect of the object's extension in space is coupled into its motion. A survey of commonsense understandings showed that people are relatively accurate when specific dynamical judgments can be accurately based on a single information dimension; however, erroneous judgments are pervasive when simple motion contexts are misconstrued as being multidimensional, and when multidimensional quantities are the necessary basis for accurate judgments.

Understanding natural dynamics.

A long-standing issue in the study of how people acquire visual information centers around the scheduling and deployment of attentional resources: Is the process serial, or is it parallel? A substantial empirical effort has been dedicated to resolving this issue (e.g., J. M. Wolfe, 1998a, 1998b). However, the results remain largely inconclusive because the methodologies that have historically been used cannot make the necessary distinctions (J. Palmer, 1995; J. T. Townsend, 1972, 1974, 1990). In this article, the authors develop a rigorous procedure for deciding the scheduling problem in visual search by making improvements in both search methodology and data interpretation. The search method, originally used by A. H. C. van der Heijden (1975), generalizes the traditional single-target methodology by permitting multiple targets. Reaction times and error rates from 29 representative search studies were analyzed using Monte Carlo simulation. Parallel and serial models of attention were defined by coupling the appropriate sequential sampling algorithms to realistic constraints on decision making. The authors found that although most searches are conducted by a parallel limited-capacity process, there is a distinguishable search class that is serial.

Parallel and serial processes in visual search.

Two distinct families of statistical processes are considered in the production of psychophysical time series data (Gilden, 1997, 2001; Gilden, Thornton, & Mallon, 1995). We inquire whether the spectral signatures of the underlying dynamics are better described in terms of short-range autoregressive movingaverage (ARMA) processes or long-range fractal processes. A thorough presentation of both families is given so as to clarify the scope and generalizability of the models as descriptions of choice reaction time data. Analyses of data are supplemented by the construction of a spectral likelihood classifier that discriminates between the two families of processes. The classifier has sufficient sensitivity to ensure that fractals are correctly identified and that ARMA processes will rarely be misconstrued as belonging to the fractal family. Spectral likelihood classification illustrates an extremely general framework for testing competing spectral hypotheses and is offered for use in measuring the specific character of fluctuations in designed experiments.

David L. Gilden

Papers

1/f noise in human cognition

Cognitive emissions of 1/f noise

Understanding natural dynamics.

Parallel and serial processes in visual search.

Provenance of correlations in psychological data.