Rules : the basis of morality...?

Most theories of moral knowledge, throughout history, have focused on behavior-guiding rules. Those theories attempt to identify which rules are the morally valid ones, and to identify the source or ground of that privileged set. The variations on this theme are many and familiar. But there is a problem here. In fact, there are several. First, many of the higher animals display a complex social order, one crucial to their biological success, and the members of such species typically display a sophisticated knowledge of what is and what is not acceptable social behavior—but those creatures have no language at all. They are unable even to express a single rule, let alone evaluate it for moral validity. Second, when we examine most other kinds of behavioral skills—playing basketball, playing the piano, playing chess—we discover that it is surpassingly difficult to articulate a set of discursive rules, which, if followed, would produce a skilled athlete, pianist, or chess master. And third, it would be physically impossible for a biological creature to identify which of its myriad rule are relevant to a given situation, and then apply them, in real time, in any case. All told, we would seem to need a new account of how our moral knowledge is stored, accessed, and applied. The present paper explores the potential, in these three regards, of recent alternative models from the computational neurosciences. The possibilities, it emerges, are considerable

The Disunity of Consciousness

It is commonplace for both philosophers and cognitive scientists to express their allegiance to the "unity of consciousness". This is the claim that a subject’s phenomenal consciousness, at any one moment in time, is a single thing. This view has had a major influence on computational theories of consciousness. In particular, what we call single-track theories dominate the literature, theories which contend that our conscious experience is the result of a single consciousness-making process or mechanism in the brain. We argue that the orthodox view is quite wrong: phenomenal experience is not a unity, in the sense of being a single thing at each instant. It is a multiplicity, an aggregate of phenomenal elements, each of which is the product of a distinct consciousness-making mechanism in the brain. Consequently, cognitive science is in need of a multi-track theory of consciousness; a computational model that acknowledges both the manifold nature of experience, and its distributed neural basis

Neuronal Variability during Handwriting: Lognormal Distribution

We examined time-dependent statistical properties of electromyographic (EMG) signals recorded from intrinsic hand muscles during handwriting. Our analysis showed that trial-to-trial neuronal variability of EMG signals is well described by the lognormal distribution clearly distinguished from the Gaussian (normal) distribution. This finding indicates that EMG formation cannot be described by a conventional model where the signal is normally distributed because it is composed by summation of many random sources. We found that the variability of temporal parameters of handwriting - handwriting duration and response time - is also well described by a lognormal distribution. Although, the exact mechanism of lognormal statistics remains an open question, the results obtained should significantly impact experimental research, theoretical modeling and bioengineering applications of motor networks. In particular, our results suggest that accounting for lognormal distribution of EMGs can improve biomimetic systems that strive to reproduce EMG signals in artificial actuators

Incorporating Prediction in Models for Two-Dimensional Smooth Pursuit

A predictive component can contribute to the command signal for smooth pursuit. This is readily demonstrated by the fact that low frequency sinusoidal target motion can be tracked with zero time delay or even with a small lead. The objective of this study was to characterize the predictive contributions to pursuit tracking more precisely by developing analytical models for predictive smooth pursuit. Subjects tracked a small target moving in two dimensions. In the simplest case, the periodic target motion was composed of the sums of two sinusoidal motions (SS), along both the horizontal and the vertical axes. Motions following the same or similar paths, but having a richer spectral composition, were produced by having the target follow the same path but at a constant speed (CS), and by combining the horizontal SS velocity with the vertical CS velocity and vice versa. Several different quantitative models were evaluated. The predictive contribution to the eye tracking command signal could be modeled as a low-pass filtered target acceleration signal with a time delay. This predictive signal, when combined with retinal image velocity at the same time delay, as in classical models for the initiation of pursuit, gave a good fit to the data. The weighting of the predictive acceleration component was different in different experimental conditions, being largest when target motion was simplest, following the SS velocity profiles