Descriptive accounts of the nature of explanation in neuroscience and the global goals of such explanation have recently proliferated in the philosophy of neuroscience and with them new understandings of the experimental practices of neuroscientists have emerged. In this paper, I consider two models of such practices; one that takes them to be reductive; another that takes them to be integrative. I investigate those areas of the neuroscience of learning and memory from which the examples used to substantiate these models (...) are culled, and argue that the multiplicity of experimental protocols used in these research areas presents specific challenges for both models. In my view, these challenges have been overlooked largely because philosophers have hitherto failed to pay sufficient attention to fundamental features of experimental practice. I demonstrate that when we do pay attention to such features, evidence for reduction and integrative unity in neuroscience is simply not borne out. I end by suggesting some new directions for the philosophy of neuroscience that pertain to taking a closer look at the nature of neuroscientific experiments. (shrink)

Teleosemantic theories of representation are often criticized as being “too liberal”, i.e. as categorizing states as representations which are not representational at all. Recently, a powerful version of this objection has been put forth by Tyler Burge. Focusing on perception, Burge defends the claim that all teleosemantic theories apply too broadly, thereby missing what is distinctive about representation. Contra Burge, I will argue in this paper that there is a teleosemantic account of perceptual states that does not fall prey to (...) this problem, and that we can arrive at this account by combining some of Burge’s insights with a producer-oriented version of teleosemantics. The resulting theory turns out to be attractive and perfectly coherent. By contrast, the coherence of Burge’s own anti-teleosemantic approach becomes quite doubtful under closer examination—or so I will argue. (shrink)

Skyrms, building on the work of Dretske, has recently developed a novel information-theoretic account of propositional content in simple signalling systems. Information-theoretic accounts of content traditionally struggle to accommodate the possibility of misrepresentation, and I show that Skyrms’s account is no exception. I proceed to argue, however, that a modified version of Skyrms’s account can overcome this problem. On my proposed account, the propositional content of a signal is determined not by the information that it actually carries, but by the (...) information that it would carry at the nearest separating equilibrium of the underlying evolutionary dynamics. I show that this amended account yields reasonable ascriptions of false propositional content in a well-known formal model of the evolution of communication , and close with a discussion of the serious but perhaps not insuperable difficulties we face in applying the account to examples of signalling in the real world. (shrink)

An important conceptual shift can be discerned within contemporary philosophy of perception. Whereas proponents of the idea that perceptual experience is contentful used to relate perceptual content to truth conditions, authors nowadays prefer to think of perception as evaluable for accuracy. This transition from truth to accuracy becomes particularly clear in the influential work of Susanna Siegel. Importantly, Siegel actually provides an extensive argument for this shift. Yet this article argues that this transition from truth to accuracy conditions is ill‐motivated, (...) confused and pernicious for the so‐called Content View, the view that all experience has truth or accuracy evaluable content. (shrink)

The theoretical construct of functional reference is the main tool used by animal communication researchers to explore how animals refer to the world in the absence of a language. Functionally referential signals are commonly defined as signals elicited by a specific class of stimuli and capable of causing behaviors adaptive to such stimuli in the absence of contextual cues. I will argue that this definition is conceptually flawed and propose an alternative definition according to which signals can functionally refer to (...) things that rarely cause them while relying on the essential contribution of contextual cues. (shrink)

Abstract: According to the Veridicality Thesis, information requires truth. On this view, smoke carries information about there being a fire only if there is a fire, the proposition that the earth has two moons carries information about the earth having two moons only if the earth has two moons, and so on. We reject this Veridicality Thesis. We argue that the main notions of information used in cognitive science and computer science allow A to have information about the obtaining of (...) p even when p is false. (shrink)

By virtue of what do alarm calls and facial expressions carry natural information? The answer I defend in this paper is that they carry natural information by virtue of changing the probabilities of various states of affairs, relative to background data. The Probabilistic Difference Maker Theory of natural information that I introduce here is inspired by Dretske's [1981] seminal analysis of natural information, but parts ways with it by eschewing the requirements that information transmission must be nomically underwritten, mind-independent, and (...) knowledge-yielding. PDMT includes both a qualitative account of information transmission and a measure of natural information in keeping with the basic principles of Shannon's communication theory and Bayesian confirmation theory. It also includes a new account of the informational content of a signal, understood as the combination of the incremental and overall support that the signal provides for all states of affairs at the source. Finally, I compare and.. (shrink)

Neural systems process information. This platitude contains an interesting ambiguity between multiple senses of the term “information.” According to a popular thought, the ambiguity is best resolved by reserving semantic concepts of information for the explication of neural activity at a high level of organization, and quantitative concepts of information for the explication of neural activity at a low level of organization. This article articulates the justification behind this view, and concludes that it is an oversimplification. An analysis of the (...) meaning of claims about Shannon information rates in the spiking activity of neurons is then developed. On the basis of that analysis, it is shown that quantitative conceptions of information are more intertwined with semantic concepts than they seem to be, and, partially for that reason, are also more philosophically interesting. (shrink)

Neural systems process information. This platitude contains an interesting ambiguity between multiple senses of the term “information.” According to a popular thought, the ambiguity is best resolved by reserving semantic concepts of information for the explication of neural activity at a high level of organization, and quantitative concepts of information for the explication of neural activity at a low level of organization. This article articulates the justification behind this view, and concludes that it is an oversimplification. An analysis of the (...) meaning of claims about Shannon information rates in the spiking activity of neurons is then developed. On the basis of that analysis, it is shown that quantitative conceptions of information are more intertwined with semantic concepts than they seem to be, and, partially for that reason, are also more philosophically interesting. (shrink)

A fascinating research program in neurophysiology attempts to quantify the amount of information transmitted by single neurons. The claims that emerge from this research raise new philosophical questions about the nature of information. What kind of information is being quantified? Do the resulting quantities describe empirical magnitudes like those found elsewhere in the natural sciences? In this article, it is argued that neural information quantities have a relativisitic character that makes them distinct from the kinds of information typically discussed in (...) the philosophical literature. It is also argued that despite this relativistic character, there are cases in which neural information quantities can be viewed as robustly objective empirical properties. (shrink)

This paper sets out a novel response to the ‘screening off problem’ for naïve realism. The aim is to resist the claim (which many naïve realists accept) that the kind of experience involved in hallucinating also occurs during perception, by arguing that there are causal constraints that must be met if an hallucinatory experience is to occur that are never met in perceptual cases. Notably, given this response, it turns out that, contra current orthodoxy, naïve realists need not adopt any (...) particular view about the psychological nature of hallucinatory experience to handle the screening off problem. Consequently, room opens up for naïve realists to endorse whatever theory of hallucinatory experience seems to best capture the distinctive nature of such episodes. (shrink)

The debate between the defenders of explanatory unification and explanatory pluralism has been ongoing from the beginning of cognitive science and is one of the central themes of its philosophy. Does cognitive science need a grand unifying theory? Should explanatory pluralism be embraced instead? Or maybe local integrative efforts are needed? What are the advantages of explanatory unification as compared to the benefits of explanatory pluralism? These questions, among others, are addressed in this Synthese’s special issue. In the introductory paper, (...) we discuss the background of the questions, distinguishing integrative theorizing from building unified theories. On the one hand, integrative efforts involve collaboration between various disciplines, fields, approaches, or theories. These efforts could even be quite temporary, without establishing any long-term institutionalized fields or disciplines, but could also contribute to developing new interfield theories. On the other hand, unification can rely on developing complete theories of mechanisms and representations underlying all cognition, as Newell’s “unified theories of cognition”, or may appeal to grand principles, as predictive coding. Here, we also show that unification in contemporary cognitive science goes beyond reductive unity, and may involve various forms of joint efforts and division of explanatory labor. This conclusion is one of the themes present in the content of contributions constituting the special issue. (shrink)

In this paper, I argue that even if the Hard Problem of Content, as identified by Hutto and Myin, is important, it was already solved in natu- ralized semantics, and satisfactory solutions to the problem do not rely merely on the notion of information as covariance. I point out that Hutto and Myin have double standards for linguistic and mental representation, which leads to a peculiar inconsistency. Were they to apply the same standards to basic and linguistic minds, they would (...) either have to embrace representationalism or turn to semantic nihilism, which is, as I argue, an unstable and unattractive position. Hence, I conclude, their book does not offer an alternative to representation- alism. At the same time, it reminds us that representational talk in cognitive science cannot be taken for granted and that information is different from men- tal representation. Although this claim is not new, Hutto and Myin defend it forcefully and elegantly. (shrink)

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. (shrink)

The semantic concept of information is one of the most important, and one of the most problematical concepts in biology. I suggest a broad definition of biological information: a source becomes an informational input when an interpreting receiver can react to the form of the source (and variations in this form) in a functional manner. The definition accommodates information stemming from environmental cues as well as from evolved signals, and calls for a comparison between information‐transmission in different types of inheritance (...) systems—the genetic, the epigenetic, the behavioral, and the cultural‐symbolic. This comparative perspective highlights the different ways in which information is acquired and transmitted, and the role that such information plays in heredity and evolution. Focusing on the special properties of the transfer of information, which are very different from those associated with the transfer of materials or energy, also helps to uncover interesting evolutionary effects and suggests better explanations for some aspects of the evolution of communication. (shrink)

It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be (...) explained, the kind of unification afforded by the Bayesian framework to cognitive science does not necessarily reveal aspects of a mechanism. Bayesian unification, nonetheless, can place fruitful constraints on causal–mechanical explanation. 1 Introduction2 What a Great Many Phenomena Bayesian Decision Theory Can Model3 The Case of Information Integration4 How Do Bayesian Models Unify?5 Bayesian Unification: What Constraints Are There on Mechanistic Explanation?5.1 Unification constrains mechanism discovery5.2 Unification constrains the identification of relevant mechanistic factors5.3 Unification constrains confirmation of competitive mechanistic models6 ConclusionAppendix. (shrink)

Drawing on models of communication due to Lewis and Skyrms, I contrast sender-receiver systems as they appear within and between organisms, and as they function in the bridging of space and time. Within the organism, memory can be seen as the sending of messages over time, communication between stages as opposed to spatial parts. Psychological memory and genetic memory are compared with respect to their relations to a sender-receiver model. Some puzzles about “genetic information” can be resolved by seeing the (...) genome as a cell-level memory with no sender. (shrink)

Floridi’s Theory of Strongly Semantic Information posits the Veridicality Thesis. One motivation is that it can serve as a foundation for information-based epistemology being an alternative to the tripartite theory of knowledge. However, the Veridicality thesis is false, if ‘information’ is to play an explanatory role in human cognition. Another motivation is avoiding the so-called Bar-Hillel/Carnap paradox. But this paradox only seems paradoxical, if ‘information’ and ‘informativeness’ are synonymous, logic is a theory of inference, or validity suffices for rational inference; (...) a, b, and c are false. (shrink)

The idea that the brain is an information processing system raises some challenging questions about whether information exists independently of brains. Answering these questions is relevant for clarifying the theoretical foundations of the sciences of mind and brain, but also for appropriately interpreting and evaluating the evidence about how brains—and other biological systems—work. This article claims that (1) informational descriptions in the sciences of mind and brain can be genuinely explanatory, despite assuming a mind-dependent notion of information; and (2) that (...) either Popperian objectivity or response-dependence in normal receivers is sufficient for grounding the explanatory role that information often plays in these sciences. Hierarchical predictive processing, which has become a central theoretical framework for neurocognitive research, is used as a case study for supporting these two claims. (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)

It is widely agreed that perceptual experience is a form of intentionality, i.e., that it has representational content. Many philosophers take this to mean that like belief, experience has propositional content, that it can be true or false. I accept that perceptual experience has intentionality; but I dispute the claim that it has propositional content. This claim does not follow from the fact that experience is intentional, nor does it follow from the fact that experiences are accurate or inaccurate. I (...) end by considering the relationship between this question and the question of whether experience has non-conceptual content. (shrink)

A widely shared view in the cognitive sciences is that discovering and assessing explanations of cognitive phenomena whose production involves uncertainty should be done in a Bayesian framework. One assumption supporting this modelling choice is that Bayes provides the best approach for representing uncertainty. However, it is unclear that Bayes possesses special epistemic virtues over alternative modelling frameworks, since a systematic comparison has yet to be attempted. Currently, it is then premature to assert that cognitive phenomena involving uncertainty are best (...) explained within the Bayesian framework. As a forewarning, progress in cognitive science may be hindered if too many scientists continue to focus their efforts on Bayesian modelling, which risks to monopolize scientific resources that may be better allocated to alternative approaches. (shrink)

The brain is often taken to be a paradigmatic example of a signaling system with semantic and representational properties, in which neurons are senders and receivers of information carried in action potentials. A closer look at this picture shows that it is not as appealing as it might initially seem in explaining the function of the brain. Working from several sender-receiver models within the teleosemantic framework, I will first argue that two requirements must be met for a system to support (...) genuine semantic information: 1. The receiver must be competent —that is, it must be able to extract rewards from its environment on the basis of the signals that it receives. 2. The receiver must have some flexibility of response relative to the signal received. In the second part of the paper, this initial framework will be applied to neural processes, pointing to the surprising conclusion that signaling at the single-neuron level is only weakly semantic at best. Contrary to received views, neurons will have little or no access to semantic information (though their patterns of activity may carry plenty of quantitative, correlational information) about the world outside the organism. Genuine representation of the world requires an organism - level receiver of semantic information, to which any particular set of neurons makes only a small contribution. (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)

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)

Why does the world look to us as it does? As Nico Orlandi argues, it is simply because of how the world is. This answer emerges from understanding vision as situated in a structured environment, and it contrasts with the view that visual perception involves an inference.

In the book, I argue that the mind can be explained computationally because it is itself computational—whether it engages in mental arithmetic, parses natural language, or processes the auditory signals that allow us to experience music. All these capacities arise from complex information-processing operations of the mind. By analyzing the state of the art in cognitive science, I develop an account of computational explanation used to explain the capacities in question.