Online research in philosophy

Kyle Stanford has recently claimed to offer a new challenge to scientific realism. Taking his inspiration from the familiar Pessimistic Induction (PI), Stanford proposes a New Induction (NI). Contra Anjan Chakravartty’s suggestion that the NI is a ‘red herring’, I argue that it reveals something deep and important about science. The Problem of Unconceived Alternatives, which lies at the heart of the NI, yields a richer anti-realism than the PI. It explains why science falls short when it falls short, and so it might figure in the most coherent account of scientific practice. However, this best account will be antirealist in some respects and about some theories. It will not be a sweeping antirealism about all or most of science.

How are we to define red? We seem to face a dilemma. For it seems that we must define red in terms of looks red. But looks red is semantically complex. We must therefore define looks red in terms of red. Can we avoid this dilemma? Christopher Peacocke thinks we can. He claims that we can define the concept of being red in terms of the concept of being red; the concept of a sensational property of visual experience. Peacocke agrees that his definition of red makes use of a concept that those who possess the concept of being red need not possess; namely, red. But he thinks that this does not matter. For, he says, the definition is justified provided we can specify what it is to possess the concept of being red in terms of the concept of being red. What he tries to show is that this might be so even if no-one could possess the concept of being red unless he possessed the concept of being red. Peacocke has two attempts at showing this. However, both these attempts fail. What Peacocke does show is something weaker. He shows that, using red, we can construct a concept that gives what he calls the constitutive role of the concept of being red; but, importantly, that it gives the constitutive role of red does not suffice for what Peacocke says is required for giving a definition. Thus, if we accept Peacocke's standard for definition, it follows that he gives us no way of avoiding the original dilemma. If this is right then perhaps we should join with those like Colin McGinn who think that we should give up our attempts to define our secondary quality concepts.

Suppose there is a red ball against a uniformly gray background moving toward my left. I am seeing the moving red ball. I am having a visual experience that carries the information (among other things) that [the ball] is red.1 Now supposing that I have the concepts RED and SEEING, and all my other cognitive (including introspective) mechanisms are intact and working normally, the job is to say exactly how I do come to know that I am seeing [the ball] as red. How do I come to know, as I shall sometimes put it, that I am seeing red?

At root, the systematicity debate over classical versus connectionist explanations for cognitive architecture turns on quantifying the degree to which human cognition is systematic. We introduce into the debate recent psychological data that provides strong support for the purely structure-based generalizations claimed by Fodor and Pylyshyn (1988). We then show, via simulation, that two widely used connectionist models (feedforward and simple recurrent networks) do not capture the same degree of generalization as human subjects. However, we show that this limitation is overcome by tensor networks that support relational processing.

This book records the thoughts of researchers -- from both computer science and philosophy -- on resolving the debate between the symbolic and connectionist...

1.1 All mental terms are defined by private ostensive definition. 1.1.1 For example, the word "red" used to denote the conscious colour experience of red, as opposed to red light or red paint, is defined by attending to a red sensation and designating it "red".

There is currently a debate over whether cognitive architecture is classical or connectionist in nature. One finds the following three comparisons between classical architecture and connectionist architecture made in the pro-connectionist literature in this debate: (1) connectionist architecture is neurally plausible and classical architecture is not; (2) connectionist architecture is far better suited to model pattern recognition capacities than is classical architecture; and (3) connectionist architecture is far better suited to model the acquisition of pattern recognition capacities by learning than is classical architecture. If true, (1)–(3) would yield a compelling case against the view that cognitive architecture is classical, and would offer some reason to think that cognitive architecture may be connectionist. We first present the case for (1)–(3) in the very words of connectionist enthusiasts. We then argue that the currently available evidence fails to support any of (1)–(3).