A novel account of semantic information is proposed. The gist is that structural correspondence, analysed in terms of similarity, underlies an important kind of semantic information. In contrast to extant accounts of semantic information, it does not rely on correlation, covariation, causation, natural laws, or logical inference. Instead, it relies on structural similarity, defined in terms of correspondence between classifications of tokens into types. This account elucidates many existing uses of the notion of information, for example, in the context of (...) scientific models and structural representations in cognitive science. It is poised to open a new research program concerned with various kinds of semantic information, its functions, and its measurement. (shrink)

Biological and cognitive sciences rely heavily on the idea of information transmitted between natural events or processes. This paper critically assesses some current philosophical views of natural information and defends a view of natural information as Nomic and Factive. Dretske offered a Factive view of information, and recent work on the topic has tended to reject this aspect of his view in favor of a non-Factive, probabilistic approach. This paper argues that the reasoning behind this move to non-Factivity is flawed (...) and mixes up different problems with Dretske’s original view. I show that one of these problems—strictness—has to do with Nomicity rather than Factivity. The other problem—reference class ambiguity—is not solvable just in terms of a theory of natural information. On the Dretske-inspired view I defend, natural information is Factive and Nomic but is insufficient to determine the cognitive or biological content of a natural process. In sum I present an examination what natural information is and what role it can play in our understanding of living and thinking things. (shrink)

Structural representations are increasingly popular in philosophy of cognitive science. A key virtue they seemingly boast is that of meeting Ramsey's job description challenge. For this reason, structural representations appear tailored to play a clear representational role within cognitive architectures. Here, however, I claim that structural representations do not meet the job description challenge. This is because even our most demanding account of their functional profile is satisfied by at least some receptors, which paradigmatically fail the job description challenge. Hence, (...) the functional profile typically associated with structural representations does not identify representational posits. After a brief introduction, I present, in the second section of the paper, the job description challenge. I clarify why receptors fail to meet it and highlight why, as a result, they should not be considered representations. In the third section I introduce what I take to be the most demanding account of structural representations at our disposal, namely Gładziejewski's account. Provided the necessary background, I turn from exposition to criticism. In the first half of the fourth section, I equate the functional profile of structural representations and receptors. To do so, I show that some receptors boast, as a matter of fact, all the functional features associated with structural representations. Since receptors function merely as causal mediators, I conclude structural representations are mere causal mediators too. In the second half of the fourth section I make this conclusion intuitive with a toy example. I then conclude the paper, anticipating some objections my argument invites. (shrink)

The disjunction problem and the distality problem each presents a challenge that any theory of mental content must address. Here we consider their bearing on purely probabilistic causal theories. In addition to considering these problems separately, we consider a third challenge—that a theory must solve both. We call this “the hard problem.” We consider 8 basic ppc theories along with 240 hybrids of them, and show that some can handle the disjunction problem and some can handle the distality problem, but (...) none can handle the hard problem. This is our main result. We then discuss three possible responses to that result, and argue that though the first two fail, the third has some promise. (shrink)

Intentionality is a central feature of our understanding of the world. We daily attribute intentional states (like beliefs, desires or perceptual states) to explain the behavior of other agents, and many theories appeal to them to understand more complex notions. Nonetheless, intentional states are puzzling entities. This article explains what intentionality is and why it is so important and problematic at the same time. Secondly, it examines various naturalistic theories, which seek to show that intentionality is compatible with a scientific (...) worldview. Finally, given that all extant proposals face significant difficulties, it explores the available options in case no naturalistic theory can succeed. (shrink)

What is the relationship between information and representation? Dating back at least to Dretske (1981), an influential answer has been that information is a rung on a ladder that gets one to representation. Representation is information, or representation is information plus some other ingredient. In this paper, I argue that this approach oversimplifies the relationship between information and representation. If one takes current probabilistic models of cognition seriously, information is connected to representation in a new way. It enters as a (...) property of the represented content as well as a property of the vehicles that carry that content. This offers a new, conceptually and logically distinct way in which information and representation are intertwined in cognition. (shrink)

The purpose of this paper is to argue against the claim that morphological computation is substantially different from other kinds of physical computation. I show that some (but not all) purported cases of morphological computation do not count as specifically computational, and that those that do are solely physical computational systems. These latter cases are not, however, specific enough: all computational systems, not only morphological ones, may (and sometimes should) be studied in various ways, including their energy efficiency, cost, reliability, (...) and durability. Second, I critically analyze the notion of “offloading” computation to the morphology of an agent or robot, by showing that, literally, computation is sometimes not offloaded but simply avoided. Third, I point out that while the morphology of any agent is indicative of the environment that it is adapted to, or informative about that environment, it does not follow that every agent has access to its morphology as the model of its environment. (shrink)

Informational theories of semantic content have been recently gaining prominence in the debate on the notion of mental representation. In this paper we examine new-wave informational theories which have a special focus on cognitive science. In particular, we argue that these theories face four important difficulties: they do not fully solve the problem of error, fall prey to the wrong distality attribution problem, have serious difficulties accounting for ambiguous and redundant representations and fail to deliver a metasemantic theory of representation. (...) Furthermore, we argue that these difficulties derive from their exclusive reliance on the notion of information, so we suggest that pure informational accounts should be complemented with functional approaches. (shrink)

In this essay we discuss recent attempts to analyse the notion of representation, as it is employed in cognitive science, in purely informational terms. In particular, we argue that recent informational theories cannot accommodate the existence of metarepresentations. Since metarepresentations play a central role in the explanation of many cognitive abilities, this is a serious shortcoming of these proposals.

In this paper, we argue for a theoretical separation of the free-energy principle from Helmholtzian accounts of the predictive brain. The free-energy principle is a theoretical framework capturing the imperative for biological self-organization in information-theoretic terms. The free-energy principle has typically been connected with a Bayesian theory of predictive coding, and the latter is often taken to support a Helmholtzian theory of perception as unconscious inference. If our interpretation is right, however, a Helmholtzian view of perception is incompatible with Bayesian (...) predictive coding under the free-energy principle. We argue that the free energy principle and the ecological and enactive approach to mind and life make for a much happier marriage of ideas. We make our argument based on three points. First we argue that the free energy principle applies to the whole animal–environment system, and not only to the brain. Second, we show that active inference, as understood by the free-energy principle, is incompatible with unconscious inference understood as analagous to scientific hypothesis-testing, the main tenet of a Helmholtzian view of perception. Third, we argue that the notion of inference at work in Bayesian predictive coding under the free-energy principle is too weak to support a Helmholtzian theory of perception. Taken together these points imply that the free energy principle is best understood in ecological and enactive terms set out in this paper. (shrink)

In this talk I present the main results from Anta (2021), namely, that the theoretical division between Boltzmannian and Gibbsian statistical mechanics should be understood as a separation in the epistemic capabilities of this physical discipline. In particular, while from the Boltzmannian framework one can generate powerful explanations of thermal processes by appealing to their microdynamics, from the Gibbsian framework one can predict observable values in a computationally effective way. Finally, I argue that this statistical mechanical schism contradicts the Hempelian (...) (1958) thesis that the predictive power of a scientific theory is directly proportional to its explanatory potential, and vice versa. (shrink)

Anil Gupta’s Conscious Experience: A Logical Inquiry provides an impressive and novel account of rational justification based on conscious experience which is used as a foundation for a new theory of empiricism. In this critical notice, I argue that Gupta’s project is fascinating, but is often hampered by a lack of sufficient philosophical justification and clarity regarding some essential features of his project, as well as a lack of engagement with relevant scientific domains that would directly bear on it, such (...) as computational neuroscience, cognitive neuroscience, and cognitive psychology. This limits the sort of logical inquiry available to him in problematic ways. (shrink)

In this paper I propose an account of self-knowledge based on a framework of predictive processing. Predictive processing understands the brain as a prediction-action machine that tries to minimize error in its predictions about the world. For this view to evolve into a complete account of human cognition we ought to provide an idea how it can account for self-knowledge – knowledge of one’s own mental states. I provide an attempt for such an account starting from remarks on introspection made (...) by Hohwy. I develop Hohwy’s picture into a general model for knowledge of one’s mental states, discussing how predictions about oneself can be used to capture self-knowledge. I further explore empirical predictions, and thereby argue that the model provides a good explanation for failure of self-knowledge in cases involving motor aftereffects, such as the broken escalator phenomenon. I conclude that the proposed account is incomplete, but provides a valuable first step to connect research on predictive processing with the epistemology of self-knowledge. (shrink)

Natural information refers to the information carried by natural signs such as that smoke is thought to carry natural information about fire. A number of influential philosophers have argued that natural information can also be utilized in a theory of mental content. The most widely discussed account of natural information holds that it results from an extremely strong relation between sign and signified. Critics have responded that it is doubtful that there are many strong relations of this sort in the (...) natural world due to variability between signs and signified. In light of this observation, a promising suggestion is that much of the interesting natural information carried by natural signs is really information with a probabilistic content. However, Dretske’s theory cannot account for this information because it would require implausible second order objective probabilities. Given the most plausible understanding of the probabilities involved here, I argue that it is only sequences of traditional natural signs that carry this probabilistic information. Several implications of this idea will be explored. (shrink)

In this paper, I use the predictive processing theory of perception to tackle the question of how perceptual states can be rationally involved in cognition by justifying other mental states. I put forward two claims regarding the epistemological implications of PP. First, perceptual states can confer justification on other mental states because the perceptual states are themselves rationally acquired. Second, despite being inferentially justified rather than epistemically basic, perceptual states can still be epistemically responsive to the mind-independent world. My main (...) goal is to elucidate the epistemology of perception already implicit in PP. But I also hope to show how it is possible to peacefully combine central tenets of foundationalist and coherentist accounts of the rational powers of perception while avoiding the well-recognized pitfalls of either. (shrink)

Unsatisfied with stringent statistical theories of information such as Dretske's unity theory, Millikan (2001, 2004, 2007) and Shea (2007) have independently introduced ?soft? statistical notions of information. I argue here that these soft statistical notions do not present viable alternative senses of information to that proposed by Dretske. Furthermore, what appears to be the primary motivation for ?soft? information can be undercut.