Information is widely perceived as essential to the study of communication and representation; still, theorists working on these topics often take themselves not to be centrally concerned with "Shannon information", as it is often put, but with some other, sometimes called "semantic" or "nonnatural",kind of information. This perception is wrong. Shannon's theory of information is the only one we need. -/- I intend to make good on this last assertion by canvassing a fully (Shannon) informational answer to the metasemantic question (...) of what makes something a representation, for a certain important family of cases. This answer and the accompanying theory, which represents a significant departure from the broadly Dretskean philosophical mainstream, will show how a number of threads in the literature on naturalistic metasemantics, aimed at describing the purportedly non-informational ingredients in representation, actually belong in the same coherent, purely information-theoretic picture. (shrink)

There are many different notions of information in logic, epistemology, psychology, biology and cognitive science, which are employed differently in each discipline, often with little overlap. Since our interest here is in biological processes and organisms, we develop a taxonomy of functional information that extends the standard cue/signal distinction. Three general, main claims are advanced here. This new taxonomy can be useful in describing learning and communication. It avoids some problems that the natural/non-natural information distinction faces. Functional information is​ ​produced (...) through exploration and stabilisation processes. (shrink)

Philosophical proponents of predictive processing cast the novelty of predictive models of perception in terms of differences in the functional role and information content of neural signals. However, they fail to provide constraints on how the crucial semantic mapping from signals to their informational contents is determined. Beyond a novel interpretative gloss on neural signals, they have little new to say about the causal structure of the system, or even what statistical information is carried by the signals. That means that (...) the predictive framework for perception can be relabeled in traditional, non-predictive terms, with no empirical consequences relevant to existing or future data. To the extent that neuroscientific research based on predictive processing is both innovative and productive, it will be due to the framework’s suggestive heuristic effects, or perhaps auxiliary empirical claims about implementation, rather than a difference in the information-processing structure that it describes. (shrink)