Stephen Francis Mann

We propose a pluralist account of content for predictive processing systems. Our pluralism combines Millikan’s teleosemantics with existing structural resemblance accounts. The paper has two goals. First, we outline how a teleosemantic treatment of signal passing in predictive processing systems would work, and how it integrates with structural resemblance accounts. We show that the core explanatory motivations and conceptual machinery of teleosemantics and predictive processing mesh together well. Second, we argue this pluralist approach expands the range of empirical cases to which the predictive processing framework might be successfully applied. This is because our pluralism is practice-oriented. A range of different notions of content are used in the cognitive sciences to explain behaviour, and some of these cases look to employ teleosemantic notions. As a result, our pluralism gives predictive processing the scope to cover these cases.

This paper aims to establish several interconnected points. First, a particular interpretation of the mathematical definition of information, known as the causal interpretation, is supported largely by misunderstandings of the engineering context from which it was taken. A better interpretation, which makes the definition and quantification of information relative to the function of its user, is outlined. The first half of the paper is given over to introducing communication theory and its competing interpretations. The second half explores three consequences of the main thesis. First, a popular claim that the quantification of information in a signal is irrelevant for the meaning of that signal is exposed as fallacious. Second, a popular distinction between causal and semantic information is shown to be misleading, and I argue it should be replaced with a related distinction between natural and intentional signs. Finally, I argue that recent empirical work from microbiology and cognitive science drawing on resources of mathematical communication theory is best interpreted by the functional account. Overall, a functional approach is shown to be both theoretically and empirically well-supported.

The Price equation is usually treated as a description of how the population average value of a trait changes due to selection and other factors. Despite the fact that its generality is often emphasised, the Price equation is typically only applied to numeric traits, like weight and height. After a thorough yet accessible introduction to the numeric form, I derive a version of the Price equation for categorical traits, like colour and shape. The new equation describes how the distribution of types, rather than the population average, changes due to selection and other factors. I argue that this is a useful and conceptually sound extension of the traditional Price equation formalism. Although categorical traits can be represented numerically via dummy coding, I argue that the new version of the equation introduces an important perspective that previous versions lack: selection does not just change averages, it changes distributions.

The goal of this paper is to show that the information carried by a structural representation can be decomposed into the information carried by its component parts. In particular, the relations between the components of a structural representation carry quantifiable information about the relations between components of their signifieds. It follows that the information carried by cognitive structural representations, including cognitive maps, can in principle be quantified and decomposed. This is perhaps surprising given that the formal tools of communication theory have typically only been applied to simpler representation-like states without significant structure, such as detectors or indicators. In the final section I consider using computational complexity theory to capture the processing advantages afforded by structural representation.

Prominent views about representation share a premise: that mathematical communication theory is blind to representational content. Here I challenge that premise by rejecting two common misconceptions: that Claude Shannon said that the meanings of signals are irrelevant for communication theory (he didn't and they aren't), and that since correlational measures can't distinguish representations from natural signs, communication theory can't distinguish them either (the premise is true but the conclusion is false; no valid argument can link them).

In November 2022, OpenAI released ChatGPT, an incredibly sophisticated chatbot. Its capability is astonishing: as well as conversing with human interlocutors, it can answer questions about history, explain almost anything you might think to ask it, and write poetry. This level of achievement has provoked interest in questions about whether a chatbot might have something similar to human intelligence or even consciousness. Given that the function of a chatbot is to process linguistic input and produce linguistic output, we consider the question whether a sophisticated chatbot might have inner speech. That is: Might it talk to itself, internally? We explored this via a conversation with ‘Playground’, a chatbot which is very similar to ChatGPT but more flexible in certain respects. We asked it questions which, plausibly, can only be answered if one first produces some inner speech. Here, we present our findings and discuss their philosophical significance.

The free energy principle is notoriously difficult to understand. In this paper, we relate the principle to a framework that philosophers of biology are familiar with: Ruth Millikan’s teleosemantics. We argue that: systems that minimise free energy are systems with a proper function; and Karl Friston’s notion of implicit modelling can be understood in terms of Millikan’s notion of mapping relations. Our analysis reveals some surprising formal similarities between the two frameworks, and suggests interesting lines of future research. We hope this will aid further philosophical evaluation of the free energy principle.

Over the last fifteen years, an ambitious explanatory framework has been proposed to unify explanations across biology and cognitive science. Active inference, whose most famous tenet is the free energy principle, has inspired excitement and confusion in equal measure. Here, we lay the ground for proper critical analysis of active inference, in three ways. First, we give simplified versions of its core mathematical models. Second, we outline the historical development of active inference and its relationship to other theoretical approaches. Third, we describe three different kinds of claim -- labelled mathematical, empirical and general -- routinely made by proponents of the framework, and suggest dialectical links between them. Overall, we aim to increase philosophical understanding of active inference so that it may be more readily evaluated. This the final submitted version of the Introduction to the Topical Collection "The Free Energy Principle: From Biology to Cognition", forthcoming in Biology & Philosophy.

Hutto and Myin claim that teleosemantics cannot account for mental content. In their view, teleosemantics accounts for a poorer kind of relation between cognitive states and the world but lacks the theoretical tools to account for a richer kind. We show that their objection imposes two criteria on theories of content: a truth-evaluable criterion and an intensionality criterion. For the objection to go through, teleosemantics must be subject to both these criteria and must fail to satisfy them. We argue that teleosemantics meets the truth-evaluable criterion and is not required to meet the intensionality criterion. We conclude that Hutto and Myin’s objection fails.

This article establishes grounds on which attributions of information and encoding in animal signals are warranted. As common interest increases between evolutionary agents, the theoretical approach best suited to describing their interaction shifts from evolutionary game theory to communication theory, which warrants informational language. The take-home positive message is that in cooperative settings, signals can appropriately be described as transmitting encoded information, regardless of the cognitive powers of signalers. The canonical example is the honeybee waggle dance, which is discussed extensively in the second and third sections. The take-home negative message is that signals are not always a consequence of coadaptation. The communication theory approach is just one end of a continuum explored more thoroughly by evolutionary game theory. The fourth and fifth sections explore this wider framework, as well as overturning some widely held misconceptions about information theory.