Quine, Ontology, and Physicalism

Quine's views on ontology and naturalism are well-known but rarely considered in tandem. According to my interpretation the connection between them is vital. I read Quine as a global epistemic structuralist. Quine thought we only ever know objects qua solutions to puzzles about significant intersections in observations. Objects are always accessed descriptively, via their roles in our best theory. Quine's Kant lectures contain an early version of epistemic structuralism with uncharacteristic remarks about the mental. Here Quine embraces mitigated anomalous monism, allowing introspection and the availability in principle of full physical descriptions of the perceptual states which get science off the ground. Later versions abandon these ideas. My epistemic-structural interpretation explains why. I argue first-personal introspective access to mental states is incompatible with global epistemic structuralism

Mental States Are Like Diseases

While Quine’s linguistic behaviorism is well-known, his Kant Lectures contain one of his most detailed discussions of behaviorism in psychology and the philosophy of mind. Quine clarifies the nature of his psychological commitments by arguing for a modest view that is against ‘excessively restrictive’ variants of behaviorism while maintaining ‘a good measure of behaviorist discipline…to keep [our mental] terms under control’. In this paper, I use Quine’s Kant Lectures to reconstruct his position. I distinguish three types of behaviorism in psychology and the philosophy of mind: ontological behaviorism, logical behaviorism, and epistemological behaviorism. I then consider Quine’s perspective on each of these views and argue that he does not fully accept any of them. By combining these perspectives we arrive at Quine’s surprisingly subtle view about behaviorism in psychology

On the Fourier expansion method for highly accurate computation of the Voigt/complex error function in a rapid algorithm

In our recent publication [1] we presented an exponential series approximation suitable for highly accurate computation of the complex error function in a rapid algorithm. In this Short Communication we describe how a simplified representation of the proposed complex error function approximation makes possible further algorithmic optimization resulting in a considerable computational acceleration without compromise on accuracy.Comment: 4 page