Christopher Mole

The semantic issues that Saul Kripke addressed in Naming and Necessity overlap substantially with those that were addressed by Michel Foucault in “What Is an Author?”. The present essay examines their area of overlap, with a view to showing that each of these works affords a perspective on the other, from which facets that are usually obscure can be brought into view. It shows that Foucault needs to take some assumptions from Kripke’s theory of naming in order to secure one of his arguments for treating authorial names as special. It then shows that, once it has been placed on these Kripkean foundations, Foucault’s position avoids the metaphysically peculiar commitments that are sometimes thought to be essential...

It has recently become popular to suggest that cognition can be explained as a process of Bayesian prediction error minimization. Some advocates of this view propose that attention should be understood as the optimization of expected precisions in the prediction-error signal (Clark, 2013, 2016; Feldman & Friston, 2010; Hohwy, 2012, 2013). This proposal successfully accounts for several attention-related phenomena. We claim that it cannot account for all of them, since there are certain forms of voluntary attention that it cannot accommodate. We therefore suggest that, although the theory of Bayesian prediction error minimization introduces some powerful tools for the explanation of mental phenomena, its advocates have been wrong to claim that Bayesian prediction error minimization is ‘all the brain ever does’.

In their 2013 study of traumatic flashback formation, Bourne, Mackay and Holmes raise the question of whether the propensity of a traumatic experience to produce flashbacks is determined by the emotions that are felt at the time of that experience. They suggest that it is not, but the grounds on which they make this suggestion are flawed. Further research is required. That research will need to overcome a significant methodological difficulty — one which is hard to avoid when fMRI data and introspective reports are combined in a single causal inference.

Some works of literature are compromised because their authors get the facts wrong. In other works deviations from the facts don’t seem to matter, and authors quite legitimately make things up. This paper gives an account of the various ways in which matters of fact can make a difference to the aesthetic value of works of literature. It concludes by showing how this account can be applied in determining when a concern with matters of fact is an important part of literary criticism and when it is merely pedantic.

Dead reckoning is a feature of the navigation behaviour shown by several creatures, including the desert ant. Recent work by C. Randy Gallistel shows that some connectionist models of dead reckoning face important challenges. These challenges are thought to arise from essential features of the connectionist approach, and have therefore been taken to show that connectionist models are unable to explain even the most primitive of psychological phenomena. I show that Gallistel’s challenges are successfully met by one recent connectionist model, proposed by Ulysses Bernardet, Sergi Bermúdez i Badia, and Paul F.M.J. Verschure. The success of this model suggests that there are ways to implement dead reckoning with neural circuits that fall within the bounds of what many people regard as neurobiologically plausible, and so that the wholesale dismissal of the connectionist modelling project remains premature

It has recently been suggested that philosophy – in particular epistemology – has a contribution to make to the analysis of criminal and military intelligence. The present article pursues this suggestion, taking three phenomena that have recently been studied by philosophers, and showing that they have important implications for the gathering and sharing of intelligence, and for the use of intelligence in the determining of military strategy. The phenomena discussed are: (1) Simpson's Paradox, (2) the distinction between resiliency and reliability of data, and (3) the Causal Markov Condition.