This paper explores an approach for autonomous generation of symbolic representations from an agent's subsymbolic activities within the agent-environment interaction. The paper describes a psychologically plausible general framework and its various methods for autonomously creating symbolic representations. The symbol generation is accomplished within, and is intrinsic to, a generic and comprehensive cognitive architecture for capturing a wide variety of psychological processes (namely, CLARION). This work points to ways of obtaining more psychologically/cognitively realistic symbolic and subsymbolic representations within the (...) framework of a cognitive architecture, and accentuates the relevance of such an approach to cognitive science and psychology. (shrink)

A distributed neural network model called SPEC for processing sentences with recursive relative clauses is described. The model is based on separating the tasks of segmenting the input word sequence into clauses, forming the case‐role representations, and keeping track of the recursive embeddings into different modules. The system needs to be trained only with the basic sentence constructs, and it generalizes not only to new instances of familiar relative clause structures but to novel structures as well. SPEC exhibits plausible memory (...) degradation as the depth of the center embeddings increases, its memory is primed by earlier constituents, and its performance is aided by semantic constraints between the constituents. The ability to process structure is largely due to a central executive network that monitors and controls the execution of the entire system. This way, in contrast to earlier subsymbolic systems, parsing is modeled as a controlled high‐level process rather than one based on automatic reflex responses. (shrink)

PDP networks that use nonmonotonic activation functions often produce hidden unit regularities that permit the internal structure of these networks to be interpreted (Berkeley et al., 1995; McCaughan, 1997; Dawson, 1998). In particular, when the responses of hidden units to a set of patterns are graphed using jittered density plots, these plots organize themselves into a set of discrete stripes or bands. In some cases, each band is associated with a local interpretation. On the basis of these observations, Berkeley (2000) (...) has suggested that these bands are both subsymbolic and symbolic in nature, and has used the analysis of one network to support the claim that there are fewer differences between symbols and subsymbols than one might expect. We suggest below that this conclusion is premature. First, in many cases the local interpretation of each band is difficult to relate to the interpretation of a network's response; a more appropriate relationship only emerges when a band associated with one hidden unit is considered in the context of other bands associated with other hidden units (i.e., interpretations of distributed representations are more useful than interpretations of local representations). Second, the content that a band designates to an external observer (i.e., the interpretation assigned to a band by the researcher) can be quite different from the content that a band designates to the output units of the network itself.. We use two different network simulations – including the one described by Berkeley (2000) – to illustrate these points. We conclude that current evidence involving interpretations of nonmonotonic PDP networks actually illustrates the differences between symbolic and subsymbolic processing. (shrink)

To deal with reactive sequential decision tasks we present a learning model Clarion which is a hybrid connectionist model consisting of both localist and dis tributed representations based on the two level ap proach proposed in Sun The model learns and utilizes procedural and declarative knowledge tapping into the synergy of the two types of processes It uni es neural reinforcement and symbolic methods to perform on line bottom up learning Experiments in various situations are reported that shed light on (...) the working of the model.. (shrink)

The representational dynamics of the brain is a subsymbolic process, and it has to be conceived as an "agent-free" type of dynamical self-organization. However, in generating a coherent internal world-model, the brain decomposes target space in a certain way. In doing so, it defines an "ontology": to have an ontology is to interpret a world. In this paper we argue that the brain, viewed as a representational system aimed at interpreting the world, possesses an ontology too. It decomposes target (...) space in a way that exhibits certain invariances, which in turn are functionally significant. A challenge for empirical research is to determine which are the functional regularities guiding this decomposition process. What are the explicit and implicit assumptions about the structure of reality, which at the same time shape the causal profile of the brain's motor output and the representational deep structure of the conscious mind arising from it (its "phenomenal output")? How do they constrain high-level phenomena like conscious experience, the emergence of a first-person perspective, or social cognition? By reviewing a series of neuroscientific results, we focus on the contribution the motor system makes to this process. As it turns out, the motor system constructs goals, actions, and intending selves as basic constituents of the world it interprets. It does so by assigning a single, unified causal role to them. Empirical evidence now clearly shows how the brain actually codes movements and action goals in terms of multimodal representations of organism-object relations. Under a representationalist analysis, this process can be interpreted as an internal representation of the intentionality relation itself. We try to show how such a more complex form of representational content, once it is in place, can later function as the building block for social cognition and a for more complex, consciously experienced representation of the first-person perspective as well. The motor system may therefore play a decisive role in understanding how the functional ontology of the human brain could be gradually extended into the subjective and social domains. (shrink)

The ability to anticipate team members' actions enables joint action towards a common goal. Task knowledge and mental simulation allow for anticipating other agents' actions and for making inferences about their underlying mental representations. In human–AI teams, providing AI agents with anticipatory mechanisms can facilitate collaboration and successful execution of joint action. This paper presents a computational cognitive model demonstrating mental simulation of operators' mental models of a situation and anticipation of their behavior. The work proposes two successive steps: (1) (...) A hierarchical cluster algorithm is applied to recognize patterns of behavior among pilots. These behavioral clusters are used to derive commonalities in situation models from empirical data (N = 13 pilots). (2) An ACT-R (adaptive control of thought - rational) cognitive model is implemented to mentally simulate different possible outcomes of action decisions and timing of a pilot. model tracing of ACT-R allows following up on operators' individual actions. Two models are implemented using the symbolic representations of ACT-R: one simulating normative behavior and the other by simulating individual differences and using subsymbolic learning. Model performance is analyzed by a comparison of both models. Results indicate the improved performance of the individual differences over the normative model and are discussed regarding implications for cognitive assistance capable of anticipating operator behavior. (shrink)

Theoretical gaps of the cognitive science. First of all the gap-thesis is based on a criticism 1. of the computer-orientated cognitive science (it confuses information with the information carrier), 2. of connectivism (its linguistic borrowing from the neurobiology is not appropriate), 3. of Varelas production model (the elimination of the function of representation results in the loss of the cognitive ability). From the context of meaning and time, then the author sketches a cognitive theoretical approach, in which thinking as (...) a (symbolic and/or subsymbolic) representation of meaning is introduced, which develops in a three-digit relation between world, language and substrate on the basis of isomorphy of time. (shrink)

Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, (...) have both combinatorial and gradient structure. They are processed through Subsymbolic Optimization–Quantization, in which an optimization process favoring representations that satisfy well-formedness constraints operates in parallel with a distributed quantization process favoring discrete symbolic structures. We apply a particular instantiation of this framework, λ-Diffusion Theory, to phonological production. Simulations of the resulting model suggest that Gradient Symbol Processing offers a way to unify accounts of grammatical competence with both discrete and continuous patterns in language performance. (shrink)

ACT is compared with a particular type of connectionist model that cannot handle symbols and use nonbiological operations which do not learn in real time. This focus continues an unfortunate trend of straw man debates in cognitive science. Adaptive Resonance Theory, or ART-neural models of cognition can handle both symbols and subsymbolic representations, and meet the Newell criteria at least as well as connectionist models.

This paper outlines the process of verbal communication of emotion as this occurs through the phases of the referential process, including arousal of an emotion schema; detailed and specific descriptions of images and episodes that are exemplars of emotion schemas; and reflection and reorganization, which may include emotion labels and other types of categorical terms. The concepts of emotion schemas and the referential process are defined in the theoretical framework of multiple code theory which includes subsymbolic sensory, visceral and (...) motoric processes, symbolic images and words. Emotion schemas are defined as clusters of representations of events incorporating similar bodily, sensory and motoric processes activated in relation to different people in different contexts. Through the referential process subsymbolic components of a schema that have been activated in a speaker or writer and that may be connected only partially to words may be evoked in a listener or reader. The concept of the emotion schemas is examined in relation to current work in emotion theory and neuroscience. The unique effects of detailed descriptions of episodes in conveying complex aspects of emotional experience are discussed, as recognized by writers, and as demonstrated in empirical research. Computerized measures of the phases of the referential process are presented, focusing particularly on the central measure, the Weighted Referential Activity Dictionary which identifies points of narrative and imagery. The operation of the function words that dominate the WRAD are examined in relation to the structure of narrative expression underlying the verbal representation of emotion schemas. (shrink)

According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the (...) class='Hi'>representation and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individual's existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction. Key Words: concept learning; conceptual development; features; perceptual learning; stimulus encoding. (shrink)

There is a gap between two different modes of computation: the symbolic mode and the subsymbolic (neuron-like) mode. The aim of this paper is to overcome this gap by viewing symbolism as a high-level description of the properties of (a class of) neural networks. Combining methods of algebraic semantics and non-monotonic logic, the possibility of integrating both modes of viewing cognition is demonstrated. The main results are (a) that certain activities of connectionist networks can be interpreted as non-monotonic inferences, (...) and (b) that there is a strict correspondence between the coding of knowledge in Hopfield networks and the knowledge representation in weight-annotated Poole systems. These results show the usefulness of non-monotonic logic as a descriptive and analytic tool for analyzing emerging properties of connectionist networks. Assuming an exponential development of the weight function, the present account relates to optimality theory – a general framework that aims to integrate insights from symbolism and connectionism. The paper concludes with some speculations about extending the present ideas. (shrink)

Marinov''s critique I argue, is vitiated by its failure to recognize the distinctive role of superposition within the distributed connectionist paradigm. The use of so-called subsymbolic distributed encodings alone is not, I agree, enough to justify treating distributed connectionism as a distinctive approach. It has always been clear that microfeatural decomposition is both possible and actual within the confines of recognizably classical approaches. When such approaches also involve statistically-driven learning algorithms — as in the case of ID3 — the (...) fundamental differences become even harder to spot. To see them, it is necessary to consider not just the nature of an acquired input-output function but the nature of the representational scheme underlying it. Differences between such schemes make themselves best felt outside the domain of immediate problem solving. It is in the more extended contexts of performance DURING learning and cognitive change as a result of SUBSEQUENT training on new tasks (or simultaneous training on several tasks) that the effects of superpositional storage techniques come to the fore. I conclude that subsymbols, distribution and statistically driven learning alone are indeed not of the essence. But connectionism is not just about subsymbols and distribution. It is about the generation of whole subsymbol SYSTEMS in which multiple distributed representations are created and superposed. (shrink)

More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous _Chinese Room_ argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital properties of human mentality as intentionality, subjectivity, and understanding. The AI research program is based on the underlying assumption that all important aspects of (...) human cognition may in principle be captured in a computational model. This assumption stems from the belief that beyond a certain level, implementational details are irrelevant to cognition. According to this belief, neurons, and biological wetware in general, have no preferred status as the substrate for a mind. As it happens, the best examples of minds we have at present have arisen from a carbon-based substrate, but this is due to constraints of evolution and possibly historical accidents, rather than to an absolute metaphysical necessity. As a result of this belief, many cognitive scientists have chosen to focus not on the biological substrate of the mind, but instead on the abstract causal structure_ _that the mind embodies (at an appropriate level of abstraction). The view that it is abstract causal structure that is essential to mentality has been an implicit assumption of the AI research program since Turing (1950), but was first articulated explicitly, in various forms, by Putnam (1960), Armstrong (1970) and Lewis (1970), and has become known as _functionalism_. From here, it is a very short step to _computationalism_, the view that computational structure is what is important in capturing the essence of mentality. This step follows from a belief that any abstract causal structure can be captured computationally: a belief made plausible by the Church–Turing Thesis, which articulates the power. (shrink)

In 1988, Smolensky proposed that connectionist processing systems should be understood as operating at what he termed the `subsymbolic' level. Subsymbolic systems should be understood by comparing them to symbolic systems, in Smolensky's view. Up until recently, there have been real problems with analyzing and interpreting the operation of connectionist systems which have undergone training. However, recently published work on a network trained on a set of logic problems originally studied by Bechtel and Abrahamsen (1991) seems to offer (...) the potential to provide a detailed, empirically based answer to questions about the nature of subsymbols. In this paper, a network analysis procedure and the results obtained using it are discussed. This provides the basis for an insight into the nature of subsymbols, which is surprising. (shrink)

L’addition, dans la cinquième édition en 1683 de La Logique ou L’Art de penser, d’un chapitre consacré à la définition générale du signe et de plusieurs chapitres relevant spécifiquement d’une analyse des signes linguistiques, a été parfois interprétée comme une apparition tardive du “problème du langage” dans le traité d’Arnauld et Nicole. Parce que la plupart de ces chapitres supplémentaires sont la transposition de passages auparavant destinés dans la Perpétuité de la foi (1669-1674) à réfuter le sens calviniste de Ceci (...) est mon corps, on a été enclin à attribuer l’importance de la question du signe et du “problème du langage” dans l’édition de 1683 à une origine théologique. J’essaie au contraire de montrer que, dès la première édition de L’Art de penser en 1662, la problématisation des écarts des expressions linguistiques avec une correspondance terme à terme entre les mots et les idées a été indissociable pour Port-Royal de l’analyse des opérations de l’esprit. Selon moi, dans la Perpétuité de la foi, la critique du sens calviniste de la formule eucharistique constitue elle-même une application de cette analyse logique du langage : les passages incorporés ensuite dans L’Art de penser sont dans le prolongement des réflexions de 1662 sur les difficultés d’adéquation entre le dire et le vouloir-dire. Je montre pour finir que la définition générale du signe à Port-Royal suppose une restructuration de la définition augustinienne du De doctrina christiana, et fonde en 1683 une identification de la relation de signification linguistique à un rapport de représentation des mots aux idées, qui vient redoubler le rapport de représentation des idées aux choses établi en 1662. (shrink)

Cognitive science and its philosophy have been far too long consumed with representation. This concern is indicative of a creeping Cartesianism that many scientists and philosophers wish to evade. However, their naturalism is often insufficiently evolutionary to fully appreciate the lessons of pragmatism. If cognitive neuroscience and pragmatism are to be mutually beneficial, the representational-friendly scientists and the anti-representational pragmatists need an alternative to representation that still accounts for what many find so attractive about representation, namely intentionality. (...) I propose that instead of representations we philosophers and scientists begin thinking in terms of cultural affordances. Like Gibsonian affordances, cultural affordances are opportunities for action. However, unlike Gibsonian affordances, which are merely biological and available for immediate action in the immediately present environment, cultural affordances also present opportunities for thinking about the past and acting into the future—tasks typically attributed to representations. (shrink)

Representation theorems are often taken to provide the foundations for decision theory. First, they are taken to characterize degrees of belief and utilities. Second, they are taken to justify two fundamental rules of rationality: that we should have probabilistic degrees of belief and that we should act as expected utility maximizers. We argue that representation theorems cannot serve either of these foundational purposes, and that recent attempts to defend the foundational importance of representation theorems are unsuccessful. As (...) a result, we should reject these claims, and lay the foundations of decision theory on firmer ground. (shrink)

This book is not a conventional introduction to the philosophy of mind, nor is it a contribution to the physicalist/ dualist debate. Instead The Representational Theory of Mind demonstrates that we can construct physicalist theories of important aspects of our mental life. Its aim is to explain and defend a physicalist theory of intelligence in two parts: the first six chapters consist of an exposition, elaboration and defence of human sentience (the functionalist theory of mind), and the second part considers (...) rivals and objections to this theory. Kim Sterelny aims to introduce people to this area of philosophy by exemplifying it, to show that philosophical and empirical investigations can be synthesized to the benefit of both. --From publisher's description. (shrink)

This is a chapter from my introductory book *Perception* covering the representational view of experience. I use the Ramsey-Lewis method to define the theoretical term "experiential representation". I clarify and discuss various questions for representationalists, for instance, "how rich is the content of experience?" and "is the content of visual experience singular or general?" Finally, I address some objections to representationalism - in particular, that it cannot explain perceptual presence (John Campbell), and that it cannot explain the "laws of (...) appearance" (constraints on how things can appear). (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.

Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing-in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand-ins in DST models, but I argue that (...) they are mistaken. Nonetheless, DST does offer new ideas as to the format of representations employed in cognitive systems. With respect to explanation, I argue that some DST models are better seen as conforming to the covering-law conception of explanation than to the mechanistic conception of explanation implicit in most cognitive science research. But even here, I argue, DST models are a valuable complement to more mechanistic cognitive explanations. (shrink)

Intelligent systems are faced with the problem of securing a principled (ideally, veridical) relationship between the world and its internal representation. I propose a unified approach to visual representation, addressing both the needs of superordinate and basic-level categorization and of identification of specific instances of familiar categories. According to the proposed theory, a shape is represented by its similarity to a number of reference shapes, measured in a high-dimensional space of elementary features. This amounts to embedding the stimulus (...) in a low-dimensional proximal shape space. That space turns out to support representation of distal shape similarities which is veridical in the sense of Shepard's (1968) notion of second-order isomorphism (i.e., correspondence between distal and proximal similarities among shapes, rather than between distal shapes and their proximal representations). Furthermore, a general expression for similarity between two stimuli, based on comparisons to reference shapes, can be used to derive models of perceived similarity ranging from continuous, symmetric, and hierarchical, as in the multidimensional scaling models (Shepard, 1980), to discrete and non-hierarchical, as in the general contrast models (Tversky, 1977; Shepard and Arabie, 1979). (shrink)

Cet article traite du problème de l'imaginaire dans les mathématiques et du débat opposant Husserl à K. Twardowski sur les représentations sans objet.

Cognitive representation is the single most important explanatory notion in the sciences of the mind and has served as the cornerstone for the so-called 'cognitive revolution'. This book critically examines the ways in which philosophers and cognitive scientists appeal to representations in their theories, and argues that there is considerable confusion about the nature of representational states. This has led to an excessive over-application of the notion - especially in many of the fresher theories in computational neuroscience. Representation (...) Reconsidered shows how psychological research is actually moving in a non-representational direction, revealing a radical, though largely unnoticed, shift in our basic understanding of how the mind works. (shrink)

"This is an important new Cummins work.

The ontic conception of explanation is predicated on the proposition that “explanation is a relation between real objects in the world” and hence, according to this approach, scientific explanation cannot take place absent such a premise. Despite the fact that critics have emphasized several drawbacks of the ontic conception, as for example its inability to address the so-called “abstract explanations”, the debate is not settled and the ontic view can claim to capture cases of explanation that are non-abstract, such as (...) causal relations between events. However, by eliminating the distinction between abstract and non-abstract explanations, it follows that ontic and epistemic proposals can no longer contend to capture different cases of explanation and either all are captured by the ontic view or all are captured by the epistemic view. On closer inspection, it turns out that the ontic view deals with events that fall outside the scientists’ scope of observation and that it does not accommodate common instances of explanation such as explanations from false propositions and hence it cannot establish itself as the dominant philosophical stance with respect to explanation. On the contrary, the epistemic conception does account for almost all episodes of explanation and can be described as a relation between representations, whereby the explanans transmit information to the explanandum and that this information can come, dependent on context, in the form of any of the available theories of explanation (law-like, unificatory, causal and non-causal). The range of application of the ontic view thus is severely restricted to trivial cases of explanation that come through direct observation of the events involved in an explanation and explanation is to be mostly conceived epistemically. (shrink)

In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furnish new forms of explanation. The second thesis is that network representations are particularly helpful in explaining the properties (...) of non-decomposable systems. Where part-whole decomposition is not possible, network science provides a much-needed alternative method of compressing information about the behavior of complex systems, and does so without succumbing to problems associated with combinatorial explosion. The article concludes with a comparison between the uses of network representation analyzed in the main discussion, and an entirely distinct use of network representation that has recently been discussed in connection with mechanistic modeling. (shrink)

Hilary Putnam, who may have been the first philosopher to advance the notion that the computer is an apt model for the mind, takes a radically new view of his...

This chapter analyzes aspects of the relationship between consciousness and intentionality. It focuses on the phenomenal character and the intentional content of perceptual states, canvassing various possible relations among them. It argues that there is a good case for a sort of representationalism, although this may not take the form that its advocates often suggest. By mapping out some of the landscape, the chapter tries to open up territory for different and promising forms of representationalism to be explored in the (...) future. In particular, it argues for a nonreductive, narrow, and Fregean variety of representationalism, which contrasts strongly with more widely explored varieties. It concludes with some words about the fundamental relationship between consciousness and intentionality. (shrink)

La 4e de couverture indique : "Si la philosophie la plus contemporaine s'articule autour de la critique de la représentation et dénonce dans la métaphysique classique la volonté d'unir en un seul concept (celui de "représenter") la multiplicité des dimensions de la pensée, elle tend parfois à négliger le fait que cette assomption de la représentation fut souvent accompagnée de sa remise en question. L'idéalisme allemand est l'un des moments de la crise de la représentation, l'une des figures les plus (...) radicales de sa critique. C'est, sur la base d'une étude de la philosophie de Fichte, ce que cet ouvrage vise à démontrer. En effet, Fichte substitue à la pensée comme représentation (Kant), la pensée comme acte, à la notion de connaissance, celle de savoir, à la notion de figuration, celle de réflexion. Ce modèle de la réflexion a ceci de spécifique que le problème, qui préside à son élaboration n'est pas, comme dans la philosophie classique de la subjectivité, de savoir comment le sujet peut revenir sur lui-même, mais comment le philosophe peut dire ce qu'il dit sans se contredire en le disant. Cette question du statut du discours du philosophe et la nécessité, pour lui, de dégager, dans tout ce qu'il dit, la forme et l'instance de son dire explique les thèmes les plus marquants de la doctrine de la science (acte, savoir et infini), la structure - totalement inédite - de son argumentation et en garantit la cohérence ; cohérence tant entre les différentes versions de la doctrine de la science qu'entre cette doctrine et les disciplines particulières (éthique, droit, religion). Dépasser la dimension traditionnellement spéculaire de la pensée et, pour ce faire, passer de la connaissance au savoir, de la validité objective à la vérité réflexive, de l'objectivation qui limite à la raison qui infinitise, tel est donc le sens de cette philosophie qu'il devient loisible de comparer aux figures les plus actuelles de la critique de la représentation.". (shrink)

The Enhanced Indispensability Argument appeals to the existence of Mathematical Explanations of Physical Phenomena to justify mathematical Platonism, following the principle of Inference to the Best Explanation. In this paper, I examine one example of a MEPP—the explanation of the 13-year and 17-year life cycle of magicicadas—and argue that this case cannot be used defend the EIA. I then generalize my analysis of the cicada case to other MEPPs, and show that these explanations rely on what I will call ‘optimal (...) representations’, which are representations that capture all that is relevant to explain a physical phenomenon at a specified level of description. In the end, because the role of mathematics in MEPPs is ultimately representational, they cannot be used to support mathematical Platonism. I finish the paper by addressing the claim, advanced by many EIA defendants, that quantification over mathematical objects results in explanations that have more theoretical virtues, especially that they are more general and modally stronger than alternative explanations. I will show that the EIA cannot be successfully defended by appealing to these notions. (shrink)

In this book Han Thomas Adriaenssen offers the first comparative exploration of the sceptical reception of representationalism in medieval and early modern philosophy. Descartes is traditionally credited with inaugurating a new kind of scepticism by saying that the direct objects of perception are images in the mind, not external objects, but Adriaenssen shows that as early as the thirteenth century, critics had already found similar problems in Aquinas's theory of representation. He charts the attempts of philosophers in both periods (...) to grapple with these problems, and shows how in order to address the challenges of scepticism and representation, modern philosophers in the wake of Descartes often breathed new life into old ideas, remoulding them in ways that we are just beginning to understand. His book will be valuable for historians interested in the medieval background to early modern thought, and to medievalists looking at continuity with the early modern period. -/- . (shrink)

According to a standard representationalist view cognitive capacities depend on internal content-carrying states. Recent alternatives to this view have been met with the reaction that they have, at best, limited scope, because a large range of cognitive phenomena—those involving absent and abstract features—require representational explanations. Here we challenge the idea that the consideration of cognition regarding the absent and the abstract can move the debate about representationalism along. Whether or not cognition involving the absent and the abstract requires the positing (...) of representations depends upon whether more basic forms of cognition require the positing of representations. (shrink)