Abstract: A naturalistic scheme of primitive conceptual representations is proposed using the statistical measure of mutual information. It is argued that a concept represents, not the class of objects that caused its tokening, but the class of objects that is most likely to have caused it (had it been tokened), as specified by the statistical measure of mutual information. This solves the problem of misrepresentation which plagues causal accounts, by taking the representation relation to be determined via ordinal relationships between conditional probabilities. The scheme can deal with statistical biases and does not rely on arbitrary criteria. Implications for the theory of meaning and semantic content are addressed. 1.

The mind-body problem is typically divided into three parts: how is it that a physical thing can be conscious? How is it that a physical thing can display intentionality? And how is it that a physical thing can be rational? These three problems correspond to three leading features of the mind, each of which at one time or another has been called a

Abstract: The merit of the SINBAD model is to provide an explicit mechanism showing how the cortex may come to develop detectors responding to correlated properties and therefore corresponding to the sources of these correlations. Here I argue that, contrary to the article, SINBAD neurosemantics does not need to rely on teleofunctions to solve the problem of misrepresentation. A number of difficulties for the teleofunction theories of content are reviewed and an alternative theory based on categorization performance and statistical relations is argued to provide a better account and to come closer to the practice in neuroscience and to powerful intuitions on swampkinds and on broad/narrow content. The SINBAD model is useful in showing how a neural-type model is able to bootstrap itself, learning to develop specific detectors that respond to correlated properties. As mental representations mediate our ability to categorize and identify substances (natural kinds, individuals, etc; Millikan, 1998), which are characterized by correlated properties, the model provides an excellent illustration of how the brain may come to develop mental representations. At present, it is not obvious