Online research in philosophy

What distinguishes a whole from an arbitrary sum of elements? I suggest a temporal and causal oriented approach. I defend two connected claims. The former is that existence is, by every means, coextensive with being the cause of a causal process. The latter is that a whole is the cause of a causal process with a joint effect. Thus, a whole is something that takes place in time. The approach endorses an unambiguous version of Restricted Composition that suits most commonsensical intuitions about wholes.

Philosophers of mind tend to take it for granted that causal relations are part of the mind-independent, objective fabric of the physical world. In fact, their status has been hotly contested since Russell famously observed that the closest thing to causal relations in physics are timesymmetric dynamical laws relating global time slices of world-history. 1 These bear a distant relationship to the local, asymmetric relations that form the core of the folk notion of cause. Nancy Cartwright, in an influential response, agreed about the absence of causal relations from physics, but argued that Russell’s position was not viable because agents choosing among potential actions need specifically causal information to distinguish effective from ineffective strategies for bringing about ends. 2 Causal beliefs play an ineliminable role in practical deliberation In recent years, there has been a great deal of progress in understanding the relationship between causal concepts and the dynamical laws that appear in advanced physics, together with a proliferation of new tools for representing and discovering causal structure.

I examine one of the conceptual cornerstones of the field known as computational neuroscience, especially as articulated in Churchland et al. (1990), an article that is arguably the locus classicus of this term and its meaning. The authors of that article try, but I claim ultimately fail, to mark off the enterprise of computational neuroscience as an interdisciplinary approach to understanding the cognitive, information-processing functions of the brain. The failure is a result of the fact that the authors provide no principled means to distinguish the study of neural systems as genuinely computational/information-processing from the study of any complex causal process. I then argue for two things. First, that in order to appropriately mark off computational neuroscience, one must be able to assign a semantics to the states over which an attempt to provide a computational explanation is made. Second, I show that neither of the two most popular ways of trying to effect such content assignation -- informational semantics and 'biosemantics' -- can make the required distinction, at least not in a way that a computational neuroscientist should be happy about. The moral of the story as I take it is not a negative one to the effect that computational neuroscience is in principle incapable of doing what it wants to do. Rather, it is to point out some work that remains to be done.

A recent version of the causal theory of time makes crucial use of a concept of the genidentity of events when it attempts to define temporal betweenness in terms of empirical, physical properties. By presenting and discussing an apparent counter-example it is argued that the role of genidentity in an empirical theory of time is problematic. In particular, it may be that the temporal behavior of objects is used to decide which events are genidentical, and, if so, the definition of temporal betweenness is circular. On the other hand, though there are strategies for avoiding the charge of circularity, in certain hypothetical situations the definition may yield inconsistent temporal orders. Then, the definition would have to be supplemented by a choice of temporal orders, and this choice may introduce an element of conventionality into the causal theory of time.

models of cognition are essentially incomplete because they fail to capture the temporal properties of mental processing. I present two possible interpretations of the dynamicists' argument from time and show that neither one is successful. The disagreement between dynamicists and symbolic theorists rests not on temporal considerations per se, but on differences over the multiple realizability of cognitive states and the proper explanatory goals of psychology. The negative arguments of dynamicists against symbolic models fail, and it is doubtful whether pursuing dynamicists' explanatory goals will lead to a robust psychological theory. Introduction Elements of the symbolic theory Elements of dynamical systems theory The argument from time 4.1 First interpretation of the argument from time 4.2 Second interpretation of the argument from time Limits of dynamical systems theory.

The aim of Jonathan Tallant’s recent article ‘What is B-time?’ (2007) is to demonstrate that B-time - which holds that time consists solely of tenseless temporal relations - is something of which we have no understanding, and that, therefore, if mind-independent time is B-time, then time is unreal. Of course, implicit in his own position is that since time is plausibly real and we do understand what time is, the correct ontology of time is A-time or tensed time. How then does Tallant purport to substantiate the crucial claim that ‘we have no understanding of what “B-time” is’ (2007: 147)? The overall structure of his argument may be stated as follows:1 Argument A..

Any analysis of the concept of computation as it occurs in the context of a discussion of the computational model of the mind must be consonant with the philosophic burden traditionally carried by that concept as providing a bridge between a physical and a psychological description of an agent. With this analysis in hand, one may ask the question: are connectionist-based systems consistent with the computational model of the mind? The answer depends upon which of several versions of connectionism one presupposes: non-learning connectionist-based systems as simulated on digital computers are consistent with the computational model of the mind, whereas connectionist-based systems (/dynamical systems) qua analog systems are not.

It has recently been suggested that, for Leibniz, temporal facts globally supervene on causal facts, with the result that worlds differing with respect to their causal facts can be indiscernible with respect to their temporal facts. Such an interpretation is at variance with more traditional readings of Leibniz's causal theory of time, which hold that Leibniz reduces temporal facts to causal facts. In this article, I argue against the global supervenience construal of Leibniz's philosophy of time. On the view of Leibniz defended here, he adopts a non-modal reduction of time to events, a form of reductionism that entails a strong covariation between a world's temporal facts and its causal facts. Consequently, worlds discernible with respect to their temporal facts must be discernible with respect to their causal facts, and worlds discernible with respect to their causal facts must be discernible with respect to their temporal facts. This position strongly favors the standard identificatory reduction of time to causation often imputed to Leibniz.

Traditional approaches to modeling cognitive systems are computational, based on utilizing the standard tools and concepts of the theory of computation. More recently, a number of philosophers have argued that cognition is too subtle or complex for these tools to handle. These philosophers propose an alternative based on dynamical systems theory. Proponents of this view characterize dynamical systems as (i) utilizing continuous rather than discrete mathematics, and, as a result, (ii) being computationally more powerful than traditional computational automata. Indeed, the logical possibility of such super-powerful systems has been demonstrated in the form of analog artificial neural networks. In this paper I consider three arguments against the nomological possibility of these automata. While the first two arguments fail, the third succeeds. In particular, the presence of noise reduces the computational power of analog networks to that of traditional computational automata, and noise is a pervasive feature of information processing in biological systems. Consequently, as an empirical thesis, the proposed dynamical alternative is under-motivated: What is required is an account of how continuously valued systems could be realized in physical systems despite the ubiquity of noise.

Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these questions. Justifying the role of computation requires analysis of implementation, the nexus between abstract computations and concrete physical systems. I give such an analysis, based on the idea that a system implements a computation if the causal structure of the system mirrors the formal structure of the computation. This account can be used to justify the central commitments of artificial intelligence and computational cognitive science: the thesis of computational sufficiency, which holds that the right kind of computational structure suffices for the possession of a mind, and the thesis of computational explanation, which holds that computation provides a general framework for the explanation of cognitive processes. The theses are consequences of the facts that (a) computation can specify general patterns of causal organization, and (b) mentality is an organizational invariant, rooted in such patterns. Along the way I answer various challenges to the computationalist position, such as those put forward by Searle. I close by advocating a kind of minimal computationalism, compatible with a very wide variety of empirical approaches to the mind. This allows computation to serve as a true foundation for cognitive science.