Online research in philosophy

Machine generated contents note: Preface; Introduction Angus Dawson; Part I. Concepts: 1. Resetting the parameters: public health as the foundation for public health ethics Angus Dawson; 2. Health, disease and the goal of public health Bengt Brülde; 3. Selective reproduction, eugenics and public health Stephen Wilkinson; 4. Risk and precaution Stephen John; Part II. Issues: 5. Smoking, health and ethics Richard Ashcroft; 6. Infectious disease control Marcel Verweij; 7. Population screening Ainsley Newson; 8. Vaccination ethics Angus Dawson; 9. Environment, ethics and public health: the climate change dilemma Anthony Kessel and Carolyn Stephens; 10. Public health research ethics: is non-exploitation the new principle for population-based research ethics? John McMillan; 11. Equity and population health: toward a broader bioethics agenda Norman Daniels; 12. Health inequities James Wilson; Index.

Pylyshyn argues that many of the methods used to study perception are too coarse to detect the distinction between perceptual and cognitive processing. We suggest that the reason for this is that the theories used to guide research in perception are at fault. More powerful theories – for instance, computer simulations – will be required to identify where perception ends and where cognition begins.

This paper examines whether a classical model could be translated into a PDP network using a standard connectionist training technique called extra output learning. In Study 1, standard machine learning techniques were used to create a decision tree that could be used to classify 8124 different mushrooms as being edible or poisonous on the basis of 21 different Features (Schlimmer, 1987). In Study 2, extra output learning was used to insert this decision tree into a PDP network being trained on the identical problem. An interpretation of the trained network revealed a perfect mapping from its internal structure to the decision tree, representing a precise translation of the classical theory to the connectionist model. In Study 3, a second network was trained on the mushroom problem without using extra output learning. An interpretation of this second network revealed a different algorithm for solving the mushroom problem, demonstrating that the Study 2 network was indeed a proper theory translation.

There is widespread belief that connectionist networks are dramatically different from classical or symbolic models. However, connectionists rarely test this belief by interpreting the internal structure of their nets. A new approach to interpreting networks was recently introduced by Berkeley et al. (1995). The current paper examines two implications of applying this method: (1) that the internal structure of a connectionist network can have a very classical appearance, and (2) that this interpretation can provide a cognitive theory that cannot be dismissed as a mere implementation.

In this book Robert Piercey asks how it is possible to do philosophy by studying the thinkers of the past. He develops his answer through readings of Martin Heidegger, Richard Rorty, Paul Ricoeur, Alasdair MacIntyre, and other historically-minded philosophers. Piercey shows that what is distinctive about these figures is a concern with philosophical pictures - extremely general conceptions of what the world is like - rather than specific theories. He offers a comprehensive and illuminating exploration of the way in which these thinkers use narrative to evaluate and criticise these pictures. The result is a powerful and original account of how philosophers use the past.

In 1988, Smolensky proposed that connectionist processing systems should be understood as operating at what he termed the `subsymbolic'' level. Subsymbolic systems should be understood by comparing them to symbolic systems, in Smolensky''s view. Up until recently, there have been real problems with analyzing and interpreting the operation of connectionist systems which have undergone training. However, recently published work on a network trained on a set of logic problems originally studied by Bechtel and Abrahamsen (1991) seems to offer the potential to provide a detailed, empirically based answer to questions about the nature of subsymbols. In this paper, a network analysis procedure and the results obtained using it are discussed. This provides the basis for an insight into the nature of subsymbols, which is surprising.

PDP networks that use nonmonotonic activation functions often produce hidden unit regularities that permit the internal structure of these networks to be interpreted (Berkeley et al., 1995; McCaughan, 1997; Dawson, 1998). In particular, when the responses of hidden units to a set of patterns are graphed using jittered density plots, these plots organize themselves into a set of discrete stripes or bands. In some cases, each band is associated with a local interpretation. On the basis of these observations, Berkeley (2000) has suggested that these bands are both subsymbolic and symbolic in nature, and has used the analysis of one network to support the claim that there are fewer differences between symbols and subsymbols than one might expect. We suggest below that this conclusion is premature. First, in many cases the local interpretation of each band is difficult to relate to the interpretation of a network's response; a more appropriate relationship only emerges when a band associated with one hidden unit is considered in the context of other bands associated with other hidden units (i.e., interpretations of distributed representations are more useful than interpretations of local representations). Second, the content that a band designates to an external observer (i.e., the interpretation assigned to a band by the researcher) can be quite different from the content that a band designates to the output units of the network itself.. We use two different network simulations – including the one described by Berkeley (2000) – to illustrate these points. We conclude that current evidence involving interpretations of nonmonotonic PDP networks actually illustrates the differences between symbolic and subsymbolic processing.