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Neuronal networks in the mammalian cortex generate several distinct oscillatory bands. These neuronal oscillators are linked to the much slower metabolic oscillators. The mean frequencies of the experimentally observed oscillator categories form a linear progression on a natural logarithmic scale with a constant ratio between neighboring frequencies. Because the ratios of the mean frequencies of the neighboring cortical oscillators are not integers, adjacent bands cannot linearly phase-lock. Oscillators of different bands couple with shifting phases and give rise to a state of perpetual fluctuation between unstable and transient stable phase synchrony. The resulting interference dynamics are a fundamental feature of the global temporal organization of the cerebral cortex. Although brain states are highly labile, neuronal avalanches are prevented by oscillatory dynamics. Scale-free dynamics generate complexity, whereas oscillations allow for temporal predictions. Dynamics in the cerebral cortex constantly alternate between the most complex metastable state and the highly predictable oscillatory state.

This chapter is all about changes and developments related to the Weber's Law during the past fifty years. Two ideas have transformed our understanding of sensory discrimination and of sensation since 1958. These are the ideas that sensory discrimination is differentially coupled to the physical world and that there is no absolute judgement. During the 1960s, many researchers, prompted by signal detection theory, proposed models for Weber's Law. Most of these models were based on particular sensory modality and aimed to explain the law without recourse to a logarithmic transform.

This chapter is concerned with the putative difference between psychology and the physical sciences. Although this difference has been viewed as problematic for psychology, it is yet to be seen why this should be so. Also considered here are the Weber-Fechner laws, a paradigm of psychophysical respectability which claims that the intensity of a percept is a logarithmic function of the intensity of the stimulus. Such laws are well established by repeated experiments, are robust across many subjects, and take the form of a mathematical equation. In short, they have many of the hallmarks of respectable scientific results. It is not true, however, that the intensity of the percept is always related to that of the stimulus in the manner which they predict.

The goal of visual psychophysics is the quantification of perceptual experience—understanding the relationship between the external stimulus, the internal representation, and the overt responses. Scaling is one of the major tools in psychophysics to measure the perceived intensity or magnitude of different physical stimuli or to understand the relationships of different stimuli in perceptual space as a function of physical variation. Scaling allows us to infer the relationship between stimuli from their distances within this space. In this chapter, we illustrate and discuss several of the most powerful and commonly used scaling methods, such as direct scaling, indirect scaling, multidimensional scaling, and their theoretical underpinnings. We anticipate that applications of scaling to brain imaging will provide new methods of constraining theories from both approaches.

This chapter discusses the hypothesis that the strongly skewed nature of our perceptions and memory result from log-normal distributions of anatomical connectivity at both micro- and mesoscales, synaptic weight distributions, firing rates, and neuronal population activity. Nearly all anatomical and physiological features of the brain are part of a continuous but wide distribution, typically obeying a log-normal form. This organization implies that the interactions that give rise to this distribution involve multiplication or division of random factors, resulting in values that can span several orders of magnitude. Neuronal networks with such broad distributions are needed to maintain stability against competing needs, including wide dynamic range, redundancy, resilience, homeostasis, and plasticity. These features of the brain may explain the Weber-Fechner law: for any sensory modality, perceptual intensity is a logarithmic function of physical intensity. Neuronal systems organized according to log rules form brain networks that can produce good-enough and fast decisions in most situations using only a subset of the brain’s resources.

This introductory chapter explains the coverage of this book, which is about the fifty-year history of the science of psychology. This book examines the ups and downs of cognitive science, modern face recognition, and the evolution of learning theory and cognitive science from 1961 to 1971. It also discusses ageing memory, Weber's law, and psycholinguistics, and analyses the changes and progress in the fields of social psychology, objection recognition, and visual perception.

Photographic color test cards having four rows of six squares include a six-square row that has six gradations of gray, including pure white and pure black at the ends. The intervening values are different manifestations of gray, going from lighter to darker. But how are the intervening values selected? What determines how “gray” they are? It turns out that they are not steps of equal change in transmission (or reflection, depending upon the type of chart), nor are they steps of equal change in optical density. The size of the gray “steps” are chosen on a somewhat different scale of values. Who came up with them, and how did they decide which values to use?