Reformers urge that representation no longer earns its explanatory keep in cognitive science, and that it is time to discard this troublesome concept. In contrast, we hold that without representation cognitive science is utterly bereft of tools for explaining natural intelligence. In order to defend the latter position, we focus on the explanatory role of representation in computation. We examine how the methods of digital and analog computation are used to model a relatively simple target system, and (...) show that representation plays an in-eliminable explanatory role in both cases. We conclude that, to the extent that biologic systems engage in computation, representation is destined to play an explanatory role in cognitive science. (shrink)

Most of this work is concerned with two theories that underlie cognitive science; theories which I call "the representational theory of intentionality" and "the computational theory of cognition" . While the representational theory of intentionality asserts that mental states are about the world in virtue of a representation relation between the world and the state, the computational theory of cognition asserts that humans and others perform cognitive tasks by computing functions on these representations. CTC draws upon a rich (...) analogy between the mind and digital computers, portraying cognizers as receiving input and generating outputs . ;Since researchers within cognitive science--especially within cognitive psychology, computer science, and philosophy of mind--share a common theoretic background in their adherence to the combination of RTI and CTC, what I call "computationalism" most of the work done in philosophy of mind has been aimed at either refuting or further articulating these two theories. This work is strongly within that tradition. ;RTI and CTC combine to create an explanatory framework for explanation in cognitive science. In this work, I seek to delineate the exact structure of this framework, to determine its components and their overall relationships. In my mind, this involves making implicit aspects of computational explanation explicit, providing an overall rationale for the various components and their relationships, and defending that rationale against criticism. ;In particular, the project of this work is to argue that computationalist explanations of cognition within cognitive science need to make appeals to both representation and knowledge; to distinguish the respective roles of knowledge and of representation in an overall picture of computational explanations, and to develop and argue for definitions of representation and knowledge that would allow those concepts to play their respective roles within computational explanations in cognitive science. ;The first half of this work , therefore, is devoted to theories of representation, especially to the need for, and role of representation in computational explanations. The first half of the work culminates in chapter three with a definition of representation and a picture of the structure of computational explanations. The second half of the project then becomes to generate a definition of knowledge that defines knowledge in non-epistemic language amenable to computationalism and that carries the epistemic burdens placed upon knowledge by its use in computational explanations, as well as to offer a limited defence of that definition within the framework of traditional epistemology. (shrink)

One of the central issues in cognitive science is the nature of human representations. We argue that symbolic representations are essential for capturing human cognitive capabilities. We start by examining some common misconceptions found in discussions of representations and models. Next we examine evidence that symbolic representations are essential for capturing human cognitive capabilities, drawing on the analogy literature. Then we examine fundamental limitations of feature vectors and other distributed representations that, despite their recent successes on various practical problems, suggest (...) that they are insufficient to capture many aspects of human cognition. After that, we describe the implications for cognitive architecture of our view that analogy is central, and we speculate on roles for hybrid approaches. We close with an analogy that might help bridge the gap. (shrink)

Connectionism provides hope for unifying work in neuroscience, computer science, and cognitive psychology. This promise has met with some resistance from Classical Computionalists, which may have inspired Connectionists to retaliate with bold, inflationary claims on behalf of Connectionist models. This paper demonstrates, by examining three intimately connected issues, that these inflationary claims made on behalf of Connectionism are wrong. This should not be construed as an attack on Connectionism, however, since the inflated claims made on its behalf have the look (...) of cures for which there are no ailments. There is nothing wrong with Connectionism for its failure to solve illusory problems. (shrink)

Even the simplest known living organisms are complex chemical processing systems. But how sophisticated is the behaviour that arises from this? We present a framework in which even bacteria can be identified as capable of representing information in arbitrary signal molecules, to facilitate altering their behaviour to optimise their food supplies, for example. Known asion/Representation theory, this framework makes precise the relationship between physical systems and abstract concepts. Originally developed to answer the question of when a physical system is (...) computing, AR theory naturally extends to the realm of biological systems to bring clarity to questions of computation at the cellular level. (shrink)

Cognitive representations are typically analysed in terms of content, vehicle and format. While current work on formats appeals to intuitions about external representations, such as words and maps, in this paper we develop a computational view of formats that does not rely on intuitions. In our view, formats are individuated by the computational profiles of vehicles, i.e., the set of constraints that fix the computational transformations vehicles can undergo. The resulting picture is strongly pluralistic, it makes space for a variety (...) of different formats, and is intimately tied to the computational approach to cognition in cognitive science and artificial intelligence. (shrink)

The Epistemology Of Computer Simulation has developed as an epistemological and methodological analysis of simulative sciences using quantitative computational models to represent and predict empirical phenomena of interest. In this paper, Executable Cell Biology and Agent-Based Modelling are examined to show how one may take advantage of qualitative computational models to evaluate reachability properties of reactive systems. In contrast to the thesis, advanced by EOCS, that computational models are not adequate representations of the simulated empirical systems, it is shown how (...) the representational adequacy of qualitative models is essential to evaluate reachability properties. Justification theory, if not playing an essential role in EOCS, is exhibited to be involved in the process of advancing and corroborating model-based hypotheses about empirical systems in ECB and ABM. Finally, the practice of evaluating model-based hypothesis by testing the simulated systems is shown to constitute an argument in favour of the thesis that computer simulations in ECB and ABM can be put on a par with scientific experiments. (shrink)

In this paper a possible general framework for the representation of concepts in cognitive artificial systems and cognitive architectures is proposed. The framework is inspired by the so called proxytype theory of concepts and combines it with the heterogeneity approach to concept representations, according to which concepts do not constitute a unitary phenomenon. The contribution of the paper is twofold: on one hand, it aims at providing a novel theoretical hypothesis for the debate about concepts in cognitive sciences by (...) providing unexplored connections between different theories; on the other hand it is aimed at sketching a computational characterization of the problem of concept representation in cognitively inspired artificial systems and in cognitive architectures. (shrink)

How can computers distinguish the coherent from the unintelligible, recognize new information in a sentence, or draw inferences from a natural language passage? Computational semantics is an exciting new field that seeks answers to these questions, and this volume is the first textbook wholly devoted to this growing subdiscipline. The book explains the underlying theoretical issues and fundamental techniques for computing semantic representations for fragments of natural language. This volume will be an essential text for computer scientists, linguists, and (...) anyone interested in the development of computational semantics. (shrink)

The sense of agency – the subjective feeling of being in control of our own actions – is one central aspect of the phenomenology of action. Computational models provided important contributions toward unveiling the mechanisms underlying the sense of agency in individual action. In particular, the sense of agency is believed to be related to the match between the actual and predicted consequences of our own actions. In the study of joint action, models are even more necessary to understand the (...) mechanisms underlying the development of coordination strategies and how the subjective experiences of control emerge during the interaction. In a joint action, we not only need to predict the consequences of our own actions; we also need to predict the actions and intentions of our partner, and to integrate these predictions to infer their joint consequences. Understanding our partner and developing mutually satisfactory coordination strategies are key components of joint action and in the development of the sense of joint agency. Here we discuss a computational architecture which addresses the sense of agency during intentional, real-time joint action. We first reformulate previous accounts of the sense of agency in probabilistic terms, as the combination of prior beliefs about the action goals and constraints, and the likelihood of the predicted movement outcomes. To look at the sense of joint agency, we extend classical computational motor control concepts - optimal estimation and optimal control. Regarding estimation, we argue that in joint action the players not only need to predict the consequences of their own actions, but also need to predict partner’s actions and intentions and to integrate these predictions to infer their joint consequences. As regards action selection, we use differential game theory – in which actions develop in continuous space and time - to formulate the problem of establishing a stable form of coordination and as a natural extension of optimal control to joint action. The resulting model posits two concurrent observer-controller loops, accounting for ‘joint’ and ‘self’ action control. The two observers quantify the likelihoods of being in control alone or jointly. Combined with prior beliefs, they provide weighing signals which are used to modulate the ‘joint’ and ‘self’ motor commands. We argue that these signals can be interpreted as the subjective sense of joint and self agency. We demonstrate the model predictions by simulating a sensorimotor interactive task where two players are mechanically coupled and are instructed to perform planar movements to reach a shared final target by crossing two differently located intermediate targets. In particular, we explore the relation between self and joint agency and the information available to each player about their partner. The proposed model provides a coherent picture of the inter-relation of prediction, control, and the sense of agency in a broader range of joint actions. (shrink)

To explain why, in scientific problem solving, a diagram can be “worth ten thousand words,” Jill Larkin and Herbert Simon (1987) relied on a computer model: two representations can be “informationally” equivalent but differ “computationally,” just as the same data can be encoded in a computer in multiple ways, more or less suited to different kinds of processing. The roots of this proposal lay in cognitive psychology, more precisely in the “imagery debate” of the 1970s on whether there are image-like (...) mental representations. Simon (1972, 1978) hoped to solve this debate by thoroughly reducing the differences between forms of mental representations (e.g., between images and sentences) to differences in computational efficiency; to carry out this reduction, he borrowed from computer science the concepts of data type and of data structure. I argue that, in the end, his account amounted to nothing more than characterizing representations by the fast operations on them. This analysis then allows me to assess what Simon’s approach actually achieves when transported from psychology to the study of scientific representations, as in Larkin and Simon (1987): it allows comparing, not representations in and of themselves, but rather the computational roles they play in particular problem-solving processes—that is, representations together with a particular way of using them. (shrink)

Most cognitive scientists believe that cognitive processing (e.g., thought, speech, perception, and sensori‐motor processing) is the hallmark of intelligent systems. Aside from modeling such processes, cognitive science is in the business of mechanistically explaining how minds and other intelligent systems work. As one might expect, mechanistic explanations appeal to the causal‐functional interactions among a system's component structures. Good explanations are the ones that get the causal story right. But getting the causal story right requires positing structures that are really in (...) the system. After all, within the context of a mechanistic explanation, to posit X is to claim not only that X exists but that X is doing some causal labor. Because most cognitive scientists believe that cognitive processing requires the use, manipulation, and storage of internal representations, they characteristically posit internal representations to explain how intelligent systems work. (shrink)

In this paper, I construe scientific understanding not only as understanding the phenomena by means of some theoretical material (theory, law or model), but more fundamentally as understanding the theoretical material itself that is supposed to explain the phenomena. De Regt and Dieks (2005) emphasise the contextual aspects of the intelligibility of theories, showing that it depends on their ―virtues‖, on the historical standards of intelligibility, and on the particular ―skills‖of their users. My paper aims at continuing this proposal, first (...) by giving a more precise definition of one's understanding of a theory and then by emphasising the importance, for this issue, of the particular formats in which a theory is expressed and hence grasped by its users. To defend this, I take the example of the versions of classical mechanics and the various formats of representation of its main principles and models. What does ―understanding a theory‖ mean? At first sight, we could say that it amounts to having a clear view of the logical relations between its core principles and theorems. This kind of understanding, though global, is quite abstract: one can understand the logical structure of a theory without being able to connect it to the phenomena. Moreover, this definition depends on how one construes the structure of theories: it will vary according to whether one defines theories as logical sets of statements with interpretative rules (following the ―syntactic conception‖ of theories) or as families of models (―semantic conception‖). I thus suggest that there is another sense of ―understanding a theory‖ that itself has two aspects. To understand a theory, one has to understand both what the theory says or means and how it works; in other words, one has to understand it as representing the phenomena (representational aspect) and to be able to manipulate it and make it fit the phenomena (computational aspect). I claim that these are essentially contextual and practical matters, and that the particular format in which the theoretical content is displayed is crucial to them. Following Humphreys' proposal (2004), I claim that one never accesses to a theory as a whole. Be it a set of statements or a class of models, in practice, it is always displayed in some particular equations, statements, images, graphs, diagrams. Humphreys' proposal of the notion of ―template‖ to complement the classical ―units of analysis‖ of science, like theories and models, may be a good candidate to study the relationship between the representational and computational aspects of understanding: a template is a ―concrete piece of syntax‖ (most of the time an equation, but I suggest that Humphreys' claim could be extended to other formats) that has both a representational and computational function. With the example of classical mechanics, I show how these two functions are interrelated and, as Humphreys suggests, are sometimes in tension with each other. Adressing these issues by focusing on the particular formats that are dealt with in practice may enlight this problematic relationship. (shrink)

Cognition is commonly taken to be computational manipulation of representations. These representations are assumed to be digital, but it is not usually specified what that means and what relevance it has for the theory. I propose a specification for being a digital state in a digital system, especially a digital computational system. The specification shows that identification of digital states requires functional directedness, either for someone or for the system of which it is a part. In the case or digital (...) representations, to be a token of a representational type, where the function of the type is to represent. [An earlier version of this paper was discussed in the web-conference "Interdisciplines" https://web.archive.org/web/20100221125700/http://www.interdisciplines.org/adaptation/papers/7 ]. (shrink)

Whilst the topic of representations is one of the key topics in philosophy of mind, it has only occasionally been noted that representations and representational features may be gradual. Apart from vague allusions, little has been said on what representational gradation amounts to and why it could be explanatorily useful. The aim of this paper is to provide a novel take on gradation of representational features within the neuroscientific framework of predictive processing. More specifically, we provide a gradual account of (...) two features of structural representations: structural similarity and decoupling. We argue that structural similarity can be analysed in terms of two dimensions: number of preserved relations and state space granularity. Both dimensions can take on different values and hence render structural similarity gradual. We further argue that decoupling is gradual in two ways. First, we show that different brain areas are involved in decoupled cognitive processes to a greater or lesser degree depending on the cause (internal or external) of their activity. Second, and more importantly, we show that the degree of decoupling can be further regulated in some brain areas through precision weighting of prediction error. We lastly argue that gradation of decoupling (via precision weighting) and gradation of structural similarity (via state space granularity) are conducive to behavioural success. (shrink)

The prefrontal cortex is widely believed to play an important role in facilitating people's ability to switch performance between different tasks. We present a biologically‐based computational model of prefrontal cortex (PFC) that explains its role in task switching in terms of the greater flexibility conferred by activation‐based working memory representations in PFC, as compared with more slowly adapting weight‐based memory mechanisms. Specifically we show that PFC representations can be rapidly updated when a task switches via a dynamic gating mechanism based (...) on a temporal‐differences reward‐prediction learning mechanism. Unlike prior models of this type, the present model develops all of its internal representations via learning mechanisms as shaped by the demands of continuous periodic task switching. This advance opens up a new domain of research into the interactions between working memory task demands and the representations that develop to meet them. Results on a version of the Wisconsin card sorting task are presented for the full model and a number of comparison networks that test the importance of various model features. Furthermore, we show that a lesioned model produces perseverative errors like those seen in frontal patients. (shrink)

Much of computational cognitive science construes human cognitive capacities as representational capacities, or as involving representation in some way. Computational theories of vision, for example, typically posit structures that represent edges in the distal scene. Neurons are often said to represent elements of their receptive fields. Despite the ubiquity of representational talk in computational theorizing there is surprisingly little consensus about how such claims are to be understood. The point of this chapter is to sketch an account of the (...) nature and function of representation in computational cognitive models. (shrink)

In recent years, a number of different disciplines have begun to investigate the fundamental role context appears to play in a number of cognitive phenomena. Traditionally, linguistics, and the fields of communication and pragmatics in particular, have been the areas that have focused the most on contextual effects. Context has increasingly been studied for its role in influencing mental concepts, for some scholars being considered constitutive for most – if not all – concepts. Cognitive neuroscience is now starting to consider (...) in a systematic way how context interacts with neural responses, although this research is still scattered and concentrated in a small number of specific cases only. In this chapter, we attempt to tie these three levels together, since only from their integration can a comprehensive explanation of how context affects cognition be constructed. The way context drives language comprehension depends on the effects of context on the conceptual scaffolding of the listener, which in turn, is the result of his neural responses in combination to context. These neural responses derive from learning throughout the history of experiences of the individual, and the association between possible contexts and heard utterances. The road we take to accomplishing the multi-level integration between what appear to be distant domains, is a computational one. This approach meets with the mechanistic framework of explanation, which is currently held as the most appropriate way of approaching cognitive phenomena that is often characterized by a multiplicity of levels, as is the case with context. The core underlying concept of the neurocomputational framework here proposed, is an account of neural representation, based on structural similarity. Structural representations are still the best option on the market in cognitive science, but in their traditional form, derived from classical measurement theory, are affected by a number of serious drawbacks, including not being able to account for context. We suggest a different account of structural similarity, one informed by current neuroscience, where the homomorphic relations required for structural similarity are derived from neural population coding. In a preliminary mathematical sketch, we indicate how this approach can construct neural aggegations that are sensitive to context. (shrink)

Lattice representations are an important tool for computability theorists when they embed nondistributive lattices into degree-theoretic structures. In this expository paper, we present the basic definitions and results about lattice representations needed by computability theorists. We define lattice representations both from the lattice-theoretic and computability-theoretic points of view, give examples and show the connection between the two types of representations, discuss some of the known theorems on the existence of lattice representations that are of interest to computability theorists, and give (...) a simple example of the use of lattice representations in an embedding result. (shrink)

I analyse the double function of models (representing the phenomena, and being a tool for calculating and predicting them) from a cognitive point of view. Taking the same approach as Ronald Giere, I nevertheless argue that he is to much committed to an abstract conception of theories and that one should give more attention to the particular formats in which models are expressed and grasped. By taking the example of Classical Mechanics, I show that a model, as an abstract entity, (...) doesn't represent anything if not presented in some particular format, be it linguistic, diagrammatic, or pictorial. This format is crucial to the relation between the two main functions of models. (shrink)

In addressing the shortcomings of computationalism, we should not throw the baby out with the bathwater. That consciousness is not merely an epiphenomenon with optional access to unconscious computations does not imply that unconscious computations, in the limited domain where they do occur (e.g., occipital transformations of visual data), cannot be reformulated in a way consistent with a self-organizational view.

Linear Logic is a branch of proof theory which provides refined tools for the study of the computational aspects of proofs. These tools include a duality-based categorical semantics, an intrinsic graphical representation of proofs, the introduction of well-behaved non-commutative logical connectives, and the concepts of polarity and focalisation. These various aspects are illustrated here through introductory tutorials as well as more specialised contributions, with a particular emphasis on applications to computer science: denotational semantics, lambda-calculus, logic programming and concurrency theory. (...) The volume is rounded-off by two invited contributions on new topics rooted in recent developments of linear logic. The book derives from a summer school that was the climax of the EU Training and Mobility of Researchers project 'Linear Logic in Computer Science'. It is an excellent introduction to some of the most active research topics in the area. (shrink)

The prefrontal cortex is widely believed to play an important role in facilitating people's ability to switch performance between different tasks. We present a biologically‐based computational model of prefrontal cortex (PFC) that explains its role in task switching in terms of the greater flexibility conferred by activation‐based working memory representations in PFC, as compared with more slowly adapting weight‐based memory mechanisms. Specifically we show that PFC representations can be rapidly updated when a task switches via a dynamic gating mechanism based (...) on a temporal‐differences reward‐prediction learning mechanism. Unlike prior models of this type, the present model develops all of its internal representations via learning mechanisms as shaped by the demands of continuous periodic task switching. This advance opens up a new domain of research into the interactions between working memory task demands and the representations that develop to meet them. Results on a version of the Wisconsin card sorting task are presented for the full model and a number of comparison networks that test the importance of various model features. Furthermore, we show that a lesioned model produces perseverative errors like those seen in frontal patients. (shrink)

The terms nested sets, partitive frequencies, inside-outside view, and dual processes add little but confusion to our original analysis (Gigerenzer & Hoffrage 1995; 1999). The idea of nested set was introduced because of an oversight; it simply rephrases two of our equations. Representation in terms of chances, in contrast, is a novel contribution yet consistent with our computational analysis System 1.dual process theory” is: Unless the two processes are defined, this distinction can account post hoc for almost everything. In (...) contrast, an ecological view of cognition helps to explain how insight is elicited from the outside (the external representation of information) and, more generally, how cognitive strategies match with environmental structures. (shrink)

Recent discussions in the philosophy of psychology have examined the use and legitimacy of such notions as ‘representation’, ‘content’, ‘computation’, and ‘inference’ within a scientific psychology. While the resulting assessments have varied widely, ranging from outright rejection of some or all of these notions to full vindication of their use, there has been notable agreement on the considerations deemed relevant for making an assessment. The answer to the question of whether the notion of, say, representational content may be admitted (...) into a scientific psychology has often been made to hinge upon whether the notion can be squared with our ‘ordinary’ or ‘folk’ style of psychological explanation, with its alleged commitment to the idiom of beliefs and desires. The emphasis on ‘folk psychology’ in arguments concerning the status of various concepts within scientific psychology is unfortunate, because it runs afoul of the ideal that the philosophy of psychology, by analogy with the philosophy of biology and the philosophy of physics, should have at its core the discussion of ongoing psychological theories. For while it may be admitted that the status of appeals to ‘folk psychology’ in order to justify a role for the belief-desire idiom within psychology proper is a matter that is of interest in its own right, it is not at all clear that mainstream experimental psychology has adopted either the language or the conceptions of ‘belief-desire’ psychology. (shrink)

This paper presents a computational model that integrates a dynamically structured holographic memory system into the ACT-R cognitive architecture to explain how linguistic representations are encoded and accessed in memory. ACT-R currently serves as the most precise expression of the moment-by-moment working memory retrievals that support sentence comprehension. The ACT-R model of sentence comprehension is able to capture a range of linguistic phenomena, but there are cases where the model makes the wrong predictions, such as the over-prediction of retrieval interference (...) effects during sentence comprehension. Here, we investigate one such case involving the processing of sentences with negative polarity items and consider how a dynamically structured holographic memory system might provide a cognitively plausible and principled explanation of some previously unexplained effects. Specifically, we show that by replacing ACT-R's declarative memory with a dynamically structured memory, we can explain a wider range of behavioral data involving reading times and judgments of grammaticality. We show that our integrated model provides a better fit to human error rates and response latencies than the original ACT-R model. These results provide proof-of-concept for the unification of two independent computational cognitive frameworks. (shrink)

This paper presents a computational model that integrates a dynamically structured holographic memory system into the ACT-R cognitive architecture to explain how linguistic representations are encoded and accessed in memory. ACT-R currently serves as the most precise expression of the moment-by-moment working memory retrievals that support sentence comprehension. The ACT-R model of sentence comprehension is able to capture a range of linguistic phenomena, but there are cases where the model makes the wrong predictions, such as the over-prediction of retrieval interference (...) effects during sentence comprehension. Here, we investigate one such case involving the processing of sentences with negative polarity items and consider how a dynamically structured holographic memory system might provide a cognitively plausible and principled explanation of some previously unexplained effects. Specifically, we show that by replacing ACT-R's declarative memory with a dynamically structured memory, we can explain a wider range of behavioral data involving reading times and judgments of grammaticality. We show that our integrated model provides a better fit to human error rates and response latencies than the original ACT-R model. These results provide proof-of-concept for the unification of two independent computational cognitive frameworks. (shrink)

summaryThis paper argues that an epistemological duality between mind/brain and an external world is an uncritically held working assumption in recent computational models of cognition. In fact, epistemological dualism largely drives computational models of mentality and representation: An assumption regarding an external world of perceptual objects and distal stimuli requires the sort of mind/brain capable of representing and inferring true accounts of such objects. Hence we have two distinct ontologies, one denoting external world objects, the other cognitive events and (...) neural transformations. A basic question is then raised and explored: If the elements of these two different ontologies are so radically different, how can the one access the other?The last part of the paper sketches an alternative to ED and the associated two ontologies account. Instead of external world objects being construed as independent givens, initiating perceptual processes, they are rather interpreted as ‘computational constructs’. It is argued at length that this account is far more consistent with recent computational models of mind/brain than the two ontologies model associated with ED. It is further claimed that an ontology of cognitive and neural states, as well as an external world ontology, have no other epistemic status that as contingent working hypotheses. As such, both ontologies are in principle amenable to revision as evidence, explanatory models, and scientific practices mutually readjust and redefine one another over time. (shrink)

This paper discusses how facet-like structures occur as a commonplace feature in a variety of computer science disciplines as a means for structuring class hierarchies. The paper then focuses on a mathematical model for facets (and class hierarchies in general), called formal concept analysis, and discusses graphical representations of faceted systems based on this model.

summaryThis paper argues that an epistemological duality between mind/brain and an external world is an uncritically held working assumption in recent computational models of cognition. In fact, epistemological dualism largely drives computational models of mentality and representation: An assumption regarding an external world of perceptual objects and distal stimuli requires the sort of mind/brain capable of representing and inferring true accounts of such objects. Hence we have two distinct ontologies, one denoting external world objects, the other cognitive events and (...) neural transformations. A basic question is then raised and explored: If the elements of these two different ontologies are so radically different, how can the one access the other?The last part of the paper sketches an alternative to ED and the associated two ontologies account. Instead of external world objects being construed as independent givens, initiating perceptual processes, they are rather interpreted as ‘computational constructs’. It is argued at length that this account is far more consistent with recent computational models of mind/brain than the two ontologies model associated with ED. It is further claimed that an ontology of cognitive and neural states, as well as an external world ontology, have no other epistemic status that as contingent working hypotheses. As such, both ontologies are in principle amenable to revision as evidence, explanatory models, and scientific practices mutually readjust and redefine one another over time. (shrink)

I discuss here the definition of computer simulations, and more specifically the views of Humphreys, who considers that an object is simulated when a computer provides a solution to a computational model, which in turn represents the object of interest. I argue that Humphreys's concepts are not able to analyse fully successfully a case of contemporary simulation in physics, which is more complex than the examples considered so far in the philosophical literature. I therefore modify Humphreys's definition of simulation. I (...) allow for several successive layers of computational models, and I discuss the relations that exist between these models, the computer, and the object under study. An aim of my proposal is to clarify the distinction between computational models and numerical methods, and to better understand the representational and the computational functions of models in simulations. (shrink)

The Computational Theory of Mind (CTM) holds that cognitive processes are essentially computational, and hence computation provides the scientific key to explaining mentality. The Representational Theory of Mind (RTM) holds that representational content is the key feature in distinguishing mental from non-mental systems. I argue that there is a deep incompatibility between these two theoretical frameworks, and that the acceptance of CTM provides strong grounds for rejecting RTM. The focal point of the incompatibility is the fact that representational content is (...) extrinsic to formal procedures as such, and the intended interpretation of syntax makes no difference to the execution of an algorithm. So the unique 'content' postulated by RTM is superfluous to the formal procedures of CTM. And once these procedures are implemented in a physical mechanism, it is exclusively the causal properties of the physical mechanism that are responsible for all aspects of the system's behaviour. So once again, postulated content is rendered superfluous. To the extent that semantic content may appear to play a role in behaviour, it must be syntactically encoded within the system, and just as in a standard computational artefact, so too with the human mind/brain - it's pure syntax all the way down to the level of physical implementation. Hence 'content' is at most a convenient meta-level gloss, projected from the outside by human theorists, which itself can play no role in cognitive processing. (shrink)

What I call theoretical abduction (sentential and model-based) certainly illustrates much of what is important in abductive reasoning, especially the objective of selecting and creating a set of hypotheses that are able to dispense good (preferred) explanations of data, but fails to account for many cases of explanations occurring in science or in everyday reasoning when the exploitation of the environment is crucial. The concept of manipulative abduction is devoted to capture the role of action in many interesting situations: action (...) provides otherwise unavailable information that enables the agent to solve problems by starting and performing a suitable abductive process of generation or selection of hypotheses. Many external things, usually inert from the epistemological point of view, can be transformed into what I call epistemic mediators, which are illustrated in the last part of the paper, together with an analysis of the related notion of external representation . Finally, some examples of computational programs that simulate geometrical reasoning are illustrated. The computational embodiment generates a kind of squared epistemic mediator: geometrical construction, as an example of epistemic mediator, is further mediated. (shrink)

Recently, Horsman et al. have proposed a new framework, Abstraction/Representation theory, for understanding and evaluating claims about unconventional or non-standard computation. Among its attractive features, the theory in particular implies a novel account of what is means to be a computer. After expounding on this account, I compare it with other accounts of concrete computation, finding that it does not quite fit in the standard categorization: while it is most similar to some semantic accounts, it is not itself a (...) semantic account. Then I evaluate it according to the six desiderata for accounts of concrete computation proposed by Piccinini. Finding that it does not clearly satisfy some of them, I propose a modification, which I call Agential AR theory, that does, yielding an account that could be a serious competitor to other leading account of concrete computation. (shrink)

In 1966 Richard Levins argued that applications of mathematics to population biology faced various constraints which forced mathematical modelers to trade-off at least one of realism, precision, or generality in their approach. Much traditional mathematical modeling in biology has prioritized generality and precision in the place of realism through strategies of idealization and simplification. This has at times created tensions with experimental biologists. The past 20 years however has seen an explosion in mathematical modeling of biological systems with the rise (...) of modern computational systems biology and many new collaborations between modelers and experimenters. In this paper I argue that many of these collaborations revolve around detail-driven modeling practices which in Levins’ terms trade-off generality for realism and precision. These practices apply mathematics by working from detailed accounts of biological systems, rather than from initially idealized or simplified representations. This is possible by virtue of modern computation. The form these practices take today suggest however Levins’ constraints on mathematical application no longer apply, transforming our understanding of what is possible with mathematics in biology. Further the engagement with realism and the ability to push realistic models in new directions aligns well with the epistemological and methodological views of experimenters, which helps explain their increased enthusiasm for biological modeling. (shrink)