This chapter defends a view of semantics on which developing a semantic theory closely resembles developing a scale of measurement. The view helps explain how semantics has made so much progress despite deep disagreements about the target of semantic theorizing (e.g., between those who maintain that semantics is characterizing something psychological, and those who maintain that it is characterizing something social), how appeals to set-theoretic abstracta make sense despite Benacerraf-style worries and despite the fact that set-theoretic entities fit badly with (...) standard (e.g., causal) metasemantic views, and why the threat of radical context sensitivity does not undermine the semantic project. (shrink)

Reasoning with diagrams is considered to be a peculiar form of reasoning. Diagrams are often associated with imagistic representations conveyed by spatial arrangements of lines, points, figures, or letters that can be manipulated to obtain knowledge on a subject matter. Reasoning with diagrams is not just ‘peculiar’ because reasoners use spatially arranged characters to obtain knowledge – diagrams apparently have cognitive surplus: they enable a quasi-intuitive form of knowledge. The present paper analyses the issue of diagrams’ cognitive value by enquiring (...) into the tradition of symbolic cognition developed by Leibniz, Lambert, and Kant. The proposal resulting from this enquiry is to question the idea that the cognitive value of diagrams lies solely in allowing evidence for inferences. The imaginative dimension of diagrams connects reasoning to doxastic attitudes of meditation and enquiry. (shrink)

Neural structural representations are cerebral map- or model-like structures that structurally resemble what they represent. These representations are absolutely central to the “cognitive neuroscience revolution”, as they are the only type of representation compatible with the revolutionaries’ mechanistic commitments. Crucially, however, these very same commitments entail that structural representations can be observed in the swirl of neuronal activity. Here, I argue that no structural representations have been observed being present in our neuronal activity, no matter the spatiotemporal scale of observation. (...) My argument begins by introducing the “cognitive neuroscience revolution” (Sect. 1 ) and sketching a prominent, widely adopted account of structural representations (Sect. 2 ). Then, I will consult various reports that describe our neuronal activity at various spatiotemporal scales, arguing that none of them reports the presence of structural representations (Sect. 3 ). After having deflected certain intuitive objections to my analysis (Sect. 4 ), I will conclude that, in the absence of neural structural representations, representationalism and mechanism can’t go together, and so the “cognitive neuroscience revolution” is forced to abandon one of its commitments (Sect. 5 ). (shrink)

Purity is known as an ideal of proof that restricts a proof to notions belonging to the ‘content’ of the theorem. In this paper, our main interest is to develop a conception of purity for formal (natural deduction) proofs. We develop two new notions of purity: one based on an ontological notion of the content of a theorem, and one based on the notions of surrogate ontological content and structural content. From there, we characterize which (classical) first-order natural deduction proofs (...) of a mathematical theorem are pure. Formal proofs that refer to the ontological content of a theorem will be called ‘fully ontologically pure’. Formal proofs that refer to a surrogate ontological content of a theorem will be called ‘secondarily ontologically pure’, because they preserve the structural content of a theorem. We will use interpretations between theories to develop a proof-theoretic criterion that guarantees secondary ontological purity for formal proofs. (shrink)

Talk of ”mental representations” is ubiquitous in the philosophy of mind, psychology, and cognitive science. A slogan common to many different approaches says that representations ”stand in for” the things they represent. This slogan also attaches to most talk of "internal models" in cognitive science. We argue that this slogan is either false or uninformative. We then offer a new slogan that aims to do better. The new slogan ties the role of representations to the cognitive role played by the (...) deliverances of perception. After clarifying the new slogan and warding off some misunderstandings, we discuss how the new slogan still captures the seed of truth in the old, point to some specific misunderstandings that can be avoided, and then suggest some ways that the new slogan is useful in the project of giving a satisfying philosophical theory of representation. (shrink)

Scientific research is constrained by limited resources, so it is imperative that it be conducted efficiently. This paper introduces the notion of epistemic expression, a kind of representation that expedites the solution of research problems. Epistemic expressions are representations that (i) contain information in a way that enables more reliable information to place the most stringent constraints on possible solutions and (ii) make new information readily extractible by biasing the search through that space. I illustrate these conditions using historical and (...) contemporary examples of biomolecular structure determination. Then, I argue that the notion of epistemic expression parts ways with pragmatic accounts of scientific representation and an understanding of models as artifacts, neither of which require models to accurately represent. Explicating epistemic expression thus fills a gap in our understanding of scientific practice, extending Morrison and Morgan's (1999) conception of models as investigative instruments. (shrink)

Inductive reasoning aims at constructing rules and models of general applicability from a restricted set of observations. Induction is a keystone in natural sciences, and it influences diverse application fields such as engineering, medicine and economics. More generally, induction plays a major role in the way humans learn and operate in their everyday life. The level of reliability that a model achieves depends on how informative the observations are relative to the flexibility of the process by which the model is (...) constructed. When the process is articulated so that the model can incorporate descriptive details and subtleties, a large set of informative observations are required to reliably tune the model, whereas models obtained from simple procedures can be tuned with fewer observations. This article introduces the concept of “dominance”, which refers to the situation in which a reduced subset of observations suffices to reconstruct the model. A mathematical framework is presented to quantify the reliability of learning procedures as a function of the size of the subset of dominant observations. Although limited in scope, we believe that our study can contribute to the understanding of some fundamental mechanisms by which knowledge is generated from observations in inductive reasoning. (shrink)

(1) An introduction to the principles of conceptual modelling, combinatorial heuristics and epistemological history; (2) the examination of a number of perennial epistemological-methodological schemata: conceptual spaces and blending theory; ars inveniendi and ars demonstrandi; the two modes of analysis and synthesis and their relationship to ars inveniendi; taxonomies and typologies as two fundamental epistemic structures; extended cognition, cognitio symbolica and model-based reasoning; (3) Plato’s notions of conceptual spaces, conceptual blending and hypothetical-analogical models (paradeigmata); (4) Ramon Llull’s concept analysis and combinatoric (...) spaces; (5) Gottfried Leibniz’s development of compositional analysis and synthesis as a general modelling method and a paradigm for ars inveniendi; (6) Fritz Zwicky’s revival of the morphological method of analysis and construction, and its subsequent computerised applications. (shrink)

A general view in philosophy of science says that the appropriateness of an object to act as a surrogate depends on the user’s decision to utilize it as such. This paper challenges this claim by examining the role of surrogative reasoning in high-throughput sequencing technologies as they are used in contemporary microbiome science. Drawing on this, we argue that, in technology-driven surrogates, knowledge about the type of inference practically permitted and epistemically justified by the surrogate constrains their use and thus (...) puts a limit to the user’s intentions to use any object as a surrogate for what they please. Ignoring this leads to a serious epistemic misalignment, which ultimately prevents surrogative reasoning. Thus, we conclude that knowledge about the type of surrogate reasoning that the technologies being used allow is fundamental to avoid misinterpreting the consequences of the data obtained with them, the hypothesis this data supports, and what these technologies are surrogates of. (shrink)

In the philosophy of perception, representationalism is the view that all phenomenological differences among mental states are representational differences, in other words, differences in content. In this paper I defend an alternative view which I call external sortalism, inspired by traditional adverbialism, and according to which experiences are not essentially representational. The central idea is that the external world serves as a model for sorting, conceptualizing, and reasoning surrogatively about perceptual experience. On external sortalism, contents are construed as a kind (...) of gloss on experiences themselves. We can retain what is attractive about representationalism, namely, that perceptual experiences can be evaluated for accuracy, without problematic commitment to the idea that they bear a substantive, representational relation to external objects and properties and that this relation determines the phenomenal character of experience. (shrink)

Theoretical models are widely held as sources of knowledge of reality. Imagination is vital to their development and to the generation of plausible hypotheses about reality. But how can imagination, which is typically held to be completely free, effectively instruct us about reality? In this paper I argue that the key to answering this question is in constrained uses of imagination. More specifically, I identify make-believe as the right notion of imagination at work in modelling. I propose the first overarching (...) taxonomy of types of constraints on scientific imagination that enables knowledge of reality. And I identify two main kinds of knowledge enabled by models, knowledge of the imaginary scenario specified by models and knowledge of reality. (shrink)

This paper develops a theory of analog representation. We first argue that the mark of the analog is to be found in the nature of a representational system’s interpretation function, rather than in its vehicles or contents alone. We then develop the rulebound structure theory of analog representation, according to which analog systems are those that use interpretive rules to map syntactic structural features onto semantic structural features. The theory involves three degree-theoretic measures that capture three independent ways in which (...) a system can be more or less analog. We explain how our theory improves upon prior accounts of analog representation, provides plausible diagnoses for novel challenge cases, extends to hybrid systems that are partially analog and partially symbolic, and accounts for some of the advantages and disadvantages of representing analogically versus symbolically. (shrink)

Claims that science may become 'self-fulfilling' through its impact on objects of study have recently risen to prominence. Despite radical statements about the supposed consequences of such accounts, however, the central notion of scientific self-fulfillment has remained obscure, leading to skewed views of its actual prevalence and significance. -/- Self-Fulfilling Science illuminates this underexplored phenomenon, drawing on insights from philosophy of science to address questions of its conceptualization, prevalence, and significance. The book critically engages with the popular notion that economic (...) theories of homo economicus exhibit self-fulfillment, and explores its relevance to various metaphysical, ethical, and epistemic issues. Extreme claims of fundamental incompatibility with our usual notions of scientific success are ill-founded. Instead, self-fulfillment’s true epistemic significance lies in more local, nuanced philosophical issues such as theory evaluation and the thesis of underdetermination. -/- In presenting a novel framework, this book facilitates deeper engagement with the developing field of self-fulfilling science and is of interest to philosophers of science, social scientists, and social constructionists. (shrink)

This paper examines two parallel discussions of scientific modeling which have invoked experimentation in addressing the role of models in scientific inquiry. One side discusses the experimental character of models, whereas the other focuses on their exploratory uses. Although both relate modeling to experimentation, they do so differently. The former has considered the similarities and differences between models and experiments, addressing, in particular, the epistemic value of materiality. By contrast, the focus on exploratory modeling has highlighted the various kinds of (...) exploratory functions of models in the early stages of inquiry. These two perspectives on modeling are discussed through a case study in the field of synthetic biology. The research practice in question explores biological control by making use of an ensemble of different epistemic means: mathematical models and simulations, synthetic genetic circuits and intracellular measuring devices, and finally electronic circuits. We argue that the study of exploratory modeling should trace the ways different epistemic means, in different materialities, are being combined over time. Finally, the epistemic status of such novel investigative objects as synthetic genetic circuits is evaluated, with the conclusion that they can function as both experiments and models. (shrink)

Inferentialists about scientific representation hold that an apparatus’s representing a target system consists in the apparatus allowing “surrogative inferences” about the target. I argue that a serious problem for inferentialism arises from the fact that many scientific theories and models contain internal inconsistencies. Inferentialism, left unamended, implies that inconsistent scientific models have unlimited representational power, since an inconsistency permits any conclusion to be inferred. I consider a number of ways that inferentialists can respond to this challenge before suggesting my own (...) solution. I develop an analogy to exploitable glitches in a game. Even though inconsistent representational apparatuses may in some sense allow for contradictions to be generated within them, doing so violates the intended function of the apparatus’s parts and hence violates representational “gameplay.”. (shrink)

Empiricist modal epistemologies can be attractive, but are often limited in the range of modal knowledge they manage to secure. In this paper, I argue that one such account – similarity-based modal empiricism – can be extended to also cover justification of many scientifically interesting possibility claims. Drawing on recent work on modelling in the philosophy of science, I suggest that scientific modelling is usefully seen as the creation and investigation of relevantly similar epistemic counterparts of real target systems. On (...) the basis of experiential knowledge of what is actually the case with the models, one can draw justified conclusions about what is de re possible for the target systems. (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)

This thesis starts with three challenges to the structuralist accounts of applied mathematics. Structuralism views applied mathematics as a matter of building mapping functions between mathematical and target-ended structures. The first challenge concerns how it is possible for a non-mathematical target to be represented mathematically when the mapping functions per se are mathematical objects. The second challenge arises out of inconsistent early calculus, which suggests that mathematical representation does not require rigorous mathematical structures. The third challenge comes from renormalisation group (...) (RG) explanations of universality. It is argued that the structural mapping between the world and a highly abstract minimal model adds little value to our understanding of how RG obtains its explanatory force. I will address the first and second challenges from the similarity perspective. The similarity account captures representations as similarity relations, providing a more flexible and broader conception of representation than structuralism. It is the specification of the respect and degree of similarity that forges mathematics into a context of representation and directs it to represent a specific system in reality. Structuralism is treatable as a tool for explicating similarity rela-tions set-theoretically. The similarity account, combined with other approaches (e.g., Nguyen and Frigg’s extensional abstraction account and van Fraassen’s pragmatic equivalence), can dissolve the first challenge. Additionally, I will make a structuralist response to the second challenge, and suggestions regarding the role of infinitesimals from the similarity perspective. In light of the similarity account, I will propose the “hotchpotch picture” as a method-ological reflection of our study of representation and explanation. Its central insight is to dissect a representation or an explanation into several aspects and use different theories (that are usually thought of competing) to appropriate each of them. Based on the hotchpotch picture, RG explanations can be dissected to the “indexing” and “inferential” conceptions of explanation, which are captured or characterised by structural mappings. Therefore, structuralism accommodates RG explanations, and the third challenge is resolved. (shrink)

Correctly understood, teleosemantics is the claim that “representation” is a function term. Things are called “representations” if they have a certain kind of function or telos and perform it in a certain kind of way. This claim is supported with a discussion and proposals about the function of a representation and of how representations perform that function. These proposals have been retrieved by putting together current descriptions from the literature on neural representations with earlier explorations of the features common to (...) most things we are inclined to call representations as these were assessed in Millikan. Of interest is the degree to which these independent sources converge. I conclude that there is no need to employ any new or technical sense of the term “representation” for it to play an important role in neuroscience. (shrink)

Two metascientific concepts that have been ― and still are ― object of philosophical analysis are the concepts of model and theory. But while the concept of scientific theory was one of the concepts to which philosophers of science devoted most attention during the 20th century, it is only in recent decades that the concept of scientific model has come to occupy a central position in philosophical reflection. However, it has done so in such a way that, at present, as (...) Jim Bogen states in the back cover of the book Scientific Models in the Philosophy of Science, by Daniela Bailer-Jones, “[t]he standard philosophical literature on the role of models in scientific reasoning is voluminous, disorganized, and confusing”. In spite of this, one of the axes that would allow us to organize at least part of this literature, and with which Bailer-Jones’ book closes, is that which is identified as one of the “contemporary philosophical issues: how theories and models relate to each other” (Bailer-Jones 2009, p. 208).That is why, in this introduction to the special issues of Metatheoria devoted to the topic of “Models and Theories in Biology”, we will present the main advances that have been made in the philosophical analysis of the concepts of model and theory in general and in biology in particular, and we will also do the same with the answers that have been given to the problem of "how theories and models relate to each other”. (shrink)

Representationalism (aka intentionalism) has been the most significant weapon in the late twentieth century defence of direct realism. However, although the representationalist objection to the Phenomenal Principle might provide an effective response to the arguments from illusion and hallucination, plausible representationalist theories of perception are, when fleshed-out, incompatible with metaphysical direct realism’s directness-claim. Indeed within cognitive science, direct perception is the avowedly-radical anti-representationalist heterodoxy. Drawing on both the philosophy and cognitive science, we develop a robust argument against representationalist direct realism (...) which does not rest on illusory cases or the notion of an object of immediate awareness. None of the representations so far posited, or in prospect, can supply the content required of a representation in a direct realist theory of veridical perception. (shrink)

In recent years, the family of algorithms collected under the term ``deep learning'' has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where a physiological structure becomes applicable for a (...) function different from that for which it was selected. In this paper, it will be argued that it is time for cognitive science to seriously come to terms with deep learning, and we try to spell out the reasons why this is the case. First, the path of the evolution of deep learning from the connectionist project is traced, demonstrating the remarkable continuity, and the differences as well. Then, it will be considered how deep learning models can be useful for many cognitive topics, especially those where it has achieved performance comparable to humans, from perception to language. It will be maintained that deep learning poses questions that cognitive sciences should try to answer. One of such questions is the reasons why deep convolutional models that are disembodied, inactive, unaware of context, and static, are by far the closest to the patterns of activation in the brain visual system. (shrink)

Je défends ici la nécessité, et ébauche une première version, d’une théorie iconique des propositions. Selon celle-ci, les propositions sont comme les objets de représentation, ou similaires à eux. Les propositions, suivant cette approche, sont des propriétés que l’esprit instancie lorsqu’il modélise le monde. Je connecte cette théorie aux récents développements de la littérature académique sur les propositions, ainsi qu’à une branche de recherches en sciences cognitives, qui explique certains types de représentations mentales en termes d’iconicité. I motivate the need (...) for, and then sketch, an iconic theory of propositions according to which propositions are like or similar to their objects of representation. Propositions on this theory are properties that the mind instantiates when it models the world. I connect the theory to recent developments in the propositions literature as well as to a strain of cognitive science that explains some kinds of mental representation in terms of iconicity. (shrink)

The recent discussion of fictional models has focused on imagination, implicitly considering fictions as something nonconcrete. We present two cases from synthetic biology that can be viewed as concrete fictions. Both minimal cells and alternative genetic systems are modal in nature: they, as well as their abstract cousins, can be used to study unactualized possibilia. We approach these synthetic constructs through Vaihinger’s notion of a semi-fiction and Goodman’s notion of semifactuality. Our study highlights the relative existence of such concrete fictions. (...) Before their realizations neither minimal cells nor alternative genetic systems were any well-defined objects, and the subsequent experimental work has given more content to these originally schematic imaginings. But it is as yet unclear whether individual members of these heterogeneous groups of somewhat functional synthetic constructs will eventually turn out to be fully realizable, remain only partially realizable, or prove outright impossible. (shrink)

Similarity-based cognition is commonplace. It occurs whenever an agent or system exploits the similarities that hold between two or more items—e.g., events, processes, objects, and so on—in order to perform some cognitive task. This kind of cognition is of special interest to cognitive neuroscientists. This paper explicates how similarity-based cognition can be understood through the lens of radical enactivism and why doing so has advantages over its representationalist rival, which posits the existence of structural representations or S-representations. Specifically, it is (...) argued that there are problems both with accounting for the content of S-representations and with understanding how neurally-based structural similarities can work as representations in guiding intelligent behavior. Finally, with these clarifications in place, it is revealed how radical enactivism can commit to an account of similarity-based cognition in its understanding of neurodynamics. (shrink)

The article develops and justifies, on the basis of the epistemological argumentation theory, two central pieces of the theory of evaluative argumentation interpretation: 1. criteria for recognizing argument types and 2. rules for adding reasons to create ideal arguments. Ad 1: The criteria for identifying argument types are a selection of essential elements from the definitions of the respective argument types. Ad 2: After presenting the general principles for adding reasons (benevolence, authenticity, immanence, optimization), heuristics are proposed for finding missing (...) reasons, for deductive arguments, e.g., semantic tableaux are suggested. (shrink)

How do models represent reality? There are two conditions that scientific models must satisfy to be representations of real systems, the aboutness condition and the epistemic condition. In this article, I critically assess the two main fictionalist theories of models as representations, the indirect fiction view and the direct fiction view, with respect to these conditions. And I develop a novel proposal, what I call ‘the new fiction view of models’. On this view, models are akin to fictional stories; they (...) represent real-world phenomena if they stand in a denotation relation with reality; and they enable knowledge of reality via the generation of theoretical hypotheses, model–world comparisons and direct attributions. (shrink)

This paper is concerned with representational explanations of how one experiences and acts with one’s body as an integrated whole. On the standard view, accounts of bodily experience and action must posit a corresponding representational structure: a representation of the body as an integrated whole. The aim of this paper is to show why we should instead favour the minimal view: given the nature of the body, and representation of its parts, accounts of the structure of bodily experience and action (...) need not appeal to a representation of the body as an integrated whole. The argument proceeds by distinguishing two kinds of explanatory roles for representations: standing-in for absent targets and structuring ambiguous sensory information concerning a target. Representations of body-parts are suited to fulfil both kinds of explanatory role, whereas a representation of the body as an integrated whole is only suited to fulfil the latter, as a means of coordinating representations of body-parts. It is then argued that the structure of the body can itself serve as a means of coordinating body-part representations, rendering representation of the body as an integrated whole explanatorily superfluous. (shrink)

A promising strategy for defending the role that representation plays in explanations of cognition frames the concept in terms of internal models or map‐like mechanisms. “Structural representation” offers an account of representation that is grounded in well‐specified, empirical criteria. However, anti‐representationalists continue to press the issue of how to account for the paradigmatic semantic properties of representation at the subpersonal level. In this paper, I offer an account of how the proponent of structural representation should think about content. There are (...) really two problems of content any account of representation must overcome: the “hard problem of content” and the “content determination problem.” I argue that the structural representation account has the resources to provide a naturalistically respectable notion of content that overcomes both problems. (shrink)

This essay presents a heterodox reading of the issue of solipsism in Wittgenstein’s Tractatus Logico-Philosophicus, out of which the whole of the TLP can be re-read. Inspired by, though not dependent on, the themes of virtuality and singularity found in Deleuze’s ‘transcendental empiricism’, Wittgenstein’s concept of ‘logical space’ is here complexly related to the paradoxes of the ‘metaphysical subject’ and ‘solipsism,’ within which the strictures of sense are defined, and through which the logico-pictorial scaffolding of the TLP precipitates its own (...) systematic dissolution. It shows how nonsense envelopes not only not idle chatter, and metaphysical confusion, but sense itself. (shrink)

How can we think about things in the outside world? There is still no widely accepted theory of how mental representations get their meaning. In light of pioneering research, Nicholas Shea develops a naturalistic account of the nature of mental representation with a firm focus on the subpersonal representations that pervade the cognitive sciences.

Scientific models are frequently discussed in philosophy of science. A great deal of the discussion is centred on approximation, idealisation, and on how these models achieve their representational function. Despite the importance, distinct nature, and high presence of toy models, they have received little attention from philosophers. This paper hopes to remedy this situation. It aims to elevate the status of toy models: by distinguishing them from approximations and idealisations, by highlighting and elaborating on several ways the Kac ring, a (...) simple statistical mechanical model, is used as a toy model, and by explaining why toy models can be used to successfully carry out important work without performing a representational function. (shrink)

The historical debate on representation in cognitive science and neuroscience construes representations as theoretical posits and discusses the degree to which we have reason to posit them. We reject the premise of that debate. We argue that experimental neuroscientists routinely observe and manipulate neural representations in their laboratory. Therefore, neural representations are as real as neurons, action potentials, or any other well-established entities in our ontology.

In this paper we defend structural representations, more specifically neural structural representation. We are not alone in this, many are currently engaged in this endeavor. The direction we take, however, diverges from the main road, a road paved by the mathematical theory of measure that, in the 1970s, established homomorphism as the way to map empirical domains of things in the world to the codomain of numbers. By adopting the mind as codomain, this mapping became a boon for all those (...) convinced that a representation system should bear similarities with what was being represented, but struggled to find a precise account of what such similarities mean. The euforia was brief, however, and soon homomorphism revealed itself to be affected by serious weaknesses, the primary one being that it included systems embarrassingly alien to representations. We find that the defense attempts that have followed, adopt strategies that share a common format: valid structural representations come as “homomorphism plus X”, with various “X”, provided in descriptive format only. Our alternative direction stems from the observation of the overlooked departure from homomorphism as used in the theory of measure and its later use in mental representations. In the former case, the codomain or the realm of numbers, is the most suited for developing theorems detailing the existence and uniqueness of homomorphism for a wide range of empirical domains. In the latter case, the codomain is the realm of the mind, possibly more vague and more ill-defined than the empirical domain itself. The time is ripe for articulating the mapping between represented domains and the mind in formal terms, by exploiting what is currently known about coding mechanisms in the brain. We provide a sketch of a possible development in this direction, one that adopts the theory of neural population coding as codomain. We will show that our framework is not only not in disagreement with the “plus X” proposals, but can lead to natural derivation of several of the “X”. (shrink)

We situate the debate on intentionality within the rise of cognitive neuroscience and argue that cognitive neuroscience can explain intentionality. We discuss the explanatory significance of ascribing intentionality to representations. At first, we focus on views that attempt to render such ascriptions naturalistic by construing them in a deflationary or merely pragmatic way. We then contrast these views with staunchly realist views that attempt to naturalize intentionality by developing theories of content for representations in terms of information and biological function. (...) We echo several other philosophers by arguing that these theories over-generalize unless they are constrained by a theory of the functional role of representational vehicles. This leads to a discussion of the functional roles of representations, and how representations might be realized in the brain. We argue that there’s work to be done to identify a distinctively mental kind of representation. We close by sketching a way forward for the project of naturalizing intentionality. This will not be achieved simply by ascribing the content of mental states to generic neural representations, but by identifying specific neural representations that explain the puzzling intentional properties of mental states. (shrink)

Science provides us with representations of atoms, elementary particles, polymers, populations, genetic trees, economies, rational decisions, aeroplanes, earthquakes, forest fires, irrigation systems, and the world’s climate. It's through these representations that we learn about the world. This entry explores various different accounts of scientific representation, with a particular focus on how scientific models represent their target systems. As philosophers of science are increasingly acknowledging the importance, if not the primacy, of scientific models as representational units of science, it's important to (...) stress that how they represent plays a fundamental role in how we are to answer other questions in the philosophy of science. This entry begins by disentangling ‘the’ problem of scientific representation, before critically evaluating the current options available in the literature. (shrink)

According to the phenomenal intentionality research program, a state’s intentional content is fixed by its phenomenal character. Defenders of this view have little to say about just how this grounding is accomplished. I argue that without a robust account of representation, the research program promises too little. Unfortunately, most of the well-developed accounts of representation – asymmetric dependence, teleosemantics, and the like – ground representation in external relations such as causation. Such accounts are inconsistent with the core of the phenomenal (...) intentionality program. I argue that, however counter-intuitive it may seem, the best prospect for explaining how phenomenal character represents appeals to resemblance. (shrink)

This book presents an original theory of the nature of pictorial representation. The most influential recent theory of depiction, put forward by Nelson Goodman, holds that the relation between depictions and what they represent is entirely conventional. Flint Schier argues to the contrary that depiction involves resemblance to the things depicted, providing a sophisticated defence of our basic intuitions on the subject. Canvassing an attractive theory of 'generativity' rather than resemblance, Dr Schier provides a detailed account of depiction, showing how (...) it illuminates and resolves many of the enigmas of pictorial representation while remaining true to our basic intuitions on the subject. Philosophers, psychologists and art theorists will find this a sophisticated and stimulating treatment of one of the central topics in aesthetics. (shrink)

Recent work in the philosophy of science has generated an apparent conflict between theories attempting to explicate the nature of scientific representation. On one side, there are what one might call 'informational' views, which emphasize objective relations (such as similarity, isomorphism, and homomorphism) between representations (theories, models, simulations, diagrams, etc.) and their target systems. On the other side, there are what one might call 'functional' views, which emphasize cognitive activities performed in connection with these targets, such as interpretation and inference. (...) The main sources of the impression of conflict here are arguments by some functionalists to the effect that informational theories are flawed: it is suggested that relations typically championed by informational theories are neither necessary nor sufficient for scientific representation, and that any theory excluding functions is inadequate. In this paper I critically examine these arguments, and contend that, as it turns out, informational and functional theories are importantly complementary. (shrink)

This book defends a two-tiered account of epistemic representation--the sort of representation relation that holds between representations such as maps and scientific models and their targets. It defends a interpretational account of epistemic representation and a structural similarity account of overall faithful epistemic representation.

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)

The notion of scientific representation plays a central role in current debates on modeling in the sciences. One or maybe the major epistemic virtue of successful models is their capacity to adequately represent specific phenomena or target systems. According to similarity views of scientific representation, models should be similar to their corresponding targets in order to represent them. In this paper, Suárez’s arguments against similarity views of representation will be scrutinized. The upshot is that the intuition that scientific representation involves (...) similarity is not refuted by the arguments. The arguments do not make the case for the strong claim that similarity between vehicles and targets is neither necessary nor sufficient for scientific representation. Especially, one claim that a similarity view wants to uphold, still, is the following thesis: only if a vehicle is similar to a target in relevant respects and to a specific degree of similarity then the vehicle is a scientific representation of that target. (shrink)

According to the predictive coding theory of cognition , brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of sorts, in (...) the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status—that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures—namely action-guiding, detachable, structural models that afford representational error detection—that play genuinely representational functions within the cognitive system. (shrink)

Despite the fact that the notion of internal representation has - at least according to some - a fundamental role to play in the sciences of the mind, not only has its explanatory utility been under attack for a while now, but it also remains unclear what criteria should an explanation of a given cognitive phenomenon meet to count as a representational explanation in the first place. The aim of this article is to propose a solution to this latter problem. (...) I will assume that representational explanations should be construed as a form of mechanistic explanations and proceed by proposing a general sketch of a functional architecture of a representational cognitive mechanism. According to the view on offer here, representational mechanisms are mechanisms that meet four conditions: the structural resemblance condition, the action-guidance condition, the decouplability condition, and the error-detection condition. (shrink)

The aim of this article is to critically examine what I call Action-Centric Theories of Representation (ACToRs). I include in this category theories of representation that (1) reject construing representation in terms of a relation that holds between representation itself (the representational vehicle) and what is represented, and instead (2) try to bring the function that representations play for cognitive systems to the center stage. Roughly speaking, according to proponents of ACToRs, what makes a representation (that is, what is constitutive (...) of it being a representation) is its being functionally involved in preselecting or guiding the actions of cognitive systems. I intend to argue that while definitely valuable, ACToRs are underconstrained and thus not entirely satisfying, since there exist structures that would count as representations according to ACToRs, but which do not play functional roles that could be nontrivially or in an explanatorily valuable way classified as representing something for a cognitive system. I outline a remedy for this theoretical situation by postulating that a fully satisfying theory of representation in cognitive science should have two factors; i.e., it should combine the pragmatic, action-oriented aspect present in ACToRs with an element that emphasizes the importance of the relation holding between a representational vehicle and what is represented. (shrink)

Is it possible to construct a machine that can act of its own accord? There are a number of skeptical arguments which conclude that autonomous machine agency is impossible. Yet if autonomous machine agency is impossible, then serious doubt is cast on the possibility of autonomous human action, at least on the widely held assumption that some form of materialism is true. The purpose of this dissertation is to show that autonomous machine agency is possible, thereby showing that the autonomy (...) of human action is compatible with materialism. ;I proceed as follows. Chapter 1 casts the problem of autonomous machine agency. Chapter 2 sets out the skeptic's case against autonomous machine agency by canvassing arguments against the possibility of machine agency and arguments against the possibility of autonomous machine agency. Chapter 3 begins work on a theory of autonomous machine agency by developing and defending axioms of a theory of agency from thought experiments and examples. Chapter 4 expands the theory of agency to a theory of autonomous agency by adding axioms of autonomy. ;The axioms of autonomous agency contain the primitive terms 'belief', 'desire', and 'deliberation', which require further explication if the theory is to be of any use in arguing for the possibility of autonomous machine agency. Chapters 5 and 6 take up this challenge by developing mathematically tractable axioms of deliberation, belief, and desire. ;Chapter 7 completes the theory by developing axioms of mechanism derived from the substantial literature in the foundations of computer science. Chapter 8 employs the resulting theory of autonomous machine agency to show just how the functional prerequisites of autonomous agency can in principle be met by machines, thereby demonstrating the possibility of autonomous machine agency. ;Chapter 9 serves as a cautionary postscript. In spite of having shown that autonomous machine agency is possible, the implications are not always happy for current efforts in artificial intelligence or current philosophical theories of autonomous human agency. Nevertheless, the argument for autonomous machine agency suggests several research programs at the nexus of Philosophy of Mind and Artificial Intelligence. (shrink)