We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides insight into the common (...) but little-discussed practices of iteratively reusing and repurposing data, which result in many datasets’ having a phylogeny—an origin and complex evolutionary history—that is relevant to their evaluation and future use. We relate these insights to the open-data and data-rescue movements, and highlight several future avenues of research that build on the PR view of data. (shrink)

Representation has been one of the main themes in the recent discussion of models. Several authors have argued for a pragmatic approach to representation that takes users and their interpretations into account. It appears to me, however, that this emphasis on representation places excessive limitations on our view of models and their epistemic value. Models should rather be thought of as epistemic artifacts through which we gain knowledge in diverse ways. Approaching models this (...) way stresses their materiality and media-specificity. Focusing on models as multi-functional artifacts loosens them from any pre-established and fixed representational relationships and leads me to argue for a two-fold approach to representation. (shrink)

Could relativist theses about scientific theories be coherent with realist theses about the relationship between such theories and the physical world? This is the central issue of this paper that we approach, mainly, on Giere’s perspectival realism. We consider that his epistemological relativist theses are plausible and sustainable, but his realist thesis about the representational role that plays the theoretical models with respect to real systems as well as his thesis about true hypotheses are not. After trying to show (...) that, we delineate an epistemological version of conceptual relativism which becomes cognate with Giere’s perspectival claims but that it does not comprise realist claims. (shrink)

This monograph offers a critical introduction to current theories of how scientific models represent their target systems. Representation is important because it allows scientists to study a model to discover features of reality. The authors provide a map of the conceptual landscape surrounding the issue of scientific representation, arguing that it consists of multiple intertwined problems. They provide an encyclopaedic overview of existing attempts to answer these questions, and they assess their strengths and weaknesses. The book also (...) presents a comprehensive statement of their alternative proposal, the DEKI account of representation, which they have developed over the last few years. They show how the account works in the case of material as well as non-material models; how it accommodates the use of mathematics in scientific modelling; and how it sheds light on the relation between representation in science and art. The issue of representation has generated a sizeable literature, which has been growing fast in particular over the last decade. This makes it hard for novices to get a handle on the topic because so far there is no book-length introduction that would guide them through the discussion. Likewise, researchers may require a comprehensive review that they can refer to for critical evaluations. This book meets the needs of both groups. (shrink)

This paper offers a modeling account of episodic representation. I argue that the episodic system constructsmental models: representations that preserve the spatiotemporal structure of represented domains. In prototypical cases, these domains are events: occurrences taken by subjects to have characteristic structures, dynamics and relatively determinate beginnings and ends. Due to their simplicity and manipulability, mental event models can be used in a variety of cognitive contexts: in remembering the personal past, but also in future-oriented and counterfactual imagination. (...) As structural representations, they allow surrogative reasoning, supporting inferences about their constituents which can be used in reasoning about the represented events. (shrink)

The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given (...) the focus on the relationship between a single model and its supposed target system, and the neglect of the actual representational means with which scientists construct models. I therefore suggest an alternative account of models as epistemic tools. This amounts to regarding them as concrete artefacts that are built by specific representational means and are constrained by their design in such a way that they facilitate the study of certain scientific questions, and learning from them by means of construction and manipulation. (shrink)

A general account of modeling in physics is proposed. Modeling is shown to involve three components: denotation, demonstration, and interpretation. Elements of the physical world are denoted by elements of the model; the model possesses an internal dynamic that allows us to demonstrate theoretical conclusions; these in turn need to be interpreted if we are to make predictions. The DDI account can be readily extended in ways that correspond to different aspects of scientific practice.

Models as Make-Believe offers a new approach to scientific modelling by looking to an unlikely source of inspiration: the dolls and toy trucks of children's games of make-believe.

The traditional model of human cognition (TMHC) postulates an ontological and/or structural gap between conscious and unconscious mental representations. By and large, it sees higher-level mental processes as commonly conceptual or symbolic in nature and therefore conscious, whereas unconscious, lower-level representations are conceived as non-conceptual or sub-symbolic. However, experimental evidence belies this model, suggesting that higher-level mental processes can be, and often are, carried out in a wholly unconscious way and/or without conceptual representations, and that these can be processed unconsciously. (...) This entails that the TMHC, as well as the theories on mental representation it motivates and that in turn support it, is wrong. (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)

Scientific models consist of fictitious elements and assumptions. Various attempts have been made to answer the question of how a model, which is sometimes viewed as a fiction, can explain or predict the target phenomenon adequately. I examine two accounts of models-as-fictions which are aiming at disentangling the myth of representing the reality by fictional models. I argue that both views have their own weaknesses in spite of many virtues. I propose to re-evaluate the problems of (...) class='Hi'>representation from a novel perspective in which some of the model representations can be regarded as fictional representations. I argue that this type of model representation is credible despite being a fictional representation of the reality. (shrink)

ABSTRACT Is there something specific about modelling that distinguishes it from many other theoretical endeavours? We consider Michael Weisberg’s thesis that modelling is a form of indirect representation through a close examination of the historical roots of the Lotka–Volterra model. While Weisberg discusses only Volterra’s work, we also study Lotka’s very different design of the Lotka–Volterra model. We will argue that while there are elements of indirect representation in both Volterra’s and Lotka’s modelling approaches, they are largely due (...) to two other features of contemporary model construction processes that Weisberg does not explicitly consider: the methods-drivenness and outcome-orientedness of modelling. 1Introduction 2Modelling as Indirect Representation 3The Design of the Lotka–Volterra Model by Volterra 3.1Volterra’s method of hypothesis 3.2The construction of the Lotka–Volterra model by Volterra 4The Design of the Lotka–Volterra Model by Lotka 4.1Physical biology according to Lotka 4.2Lotka’s systems approach and the Lotka–Volterra model 5Philosophical Discussion: Strategies and Tools of Modelling 5.1Volterra’s path from the method of isolation to the method of hypothesis 5.2The template-based approach of Lotka 5.3Modelling: methods-driven and outcome-oriented 6Conclusion. (shrink)

I argue against an account of scientific representation suggested by the semantic, or structuralist, conception of scientific theories. Proponents of this conception often employ the term “model” to refer to bare “structures”, which naturally leads them to attempt to characterize the relation between models and reality as a purely structural one. I argue instead that scientific models are typically “representations”, in the pragmatist sense of the term: they are inherently intended for specific phenomena. Therefore in general scientific (...) models are not (merely) structures. I then explore some consequences of this pragmatist account of representation, and argue that it sheds light upon the distinction between theories and models. I finish by briefly addressing some critical comments due to Bas Van Fraassen. (shrink)

Predictive processing theories are increasingly popular in philosophy of mind; such process theories often gain support from the Free Energy Principle —a normative principle for adaptive self-organized systems. Yet there is a current and much discussed debate about conflicting philosophical interpretations of FEP, e.g., representational versus non-representational. Here we argue that these different interpretations depend on implicit assumptions about what qualifies as representational. We deploy the Free Energy Principle instrumentally to distinguish four main notions of representation, which focus on (...) organizational, structural, content-related and functional aspects, respectively. The various ways that these different aspects matter in arriving at representational or non-representational interpretations of the Free Energy Principle are discussed. We also discuss how the Free Energy Principle may be seen as a unified view where terms that traditionally belong to different ontologies—e.g., notions of model and expectation versus notions of autopoiesis and synchronization—can be harmonized. However, rather than attempting to settle the representationalist versus non-representationalist debate and reveal something about what representations are simpliciter, this paper demonstrates how the Free Energy Principle may be used to reveal something about those partaking in the debate; namely, what our hidden assumptions about what representations are—assumptions that act as sometimes antithetical starting points in this persistent philosophical debate. (shrink)

Judea Pearl (2000) was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure but also to considerations of normality. In Halpern and Hitchcock (2011), we offer a definition of actual causation using extended causal models, which include information about both causal structure and normality. Extended causal models are potentially very complex. In this study, we (...) show how it is possible to achieve a compact representation of extended causal models. (shrink)

This paper examines two recent approaches to the nature and functioning of economic models: models as isolating representations and models as credible constructions. The isolationist view conceives of economic models as surrogate systems that isolate some of the causal mechanisms or tendencies of their respective target systems, while the constructionist approach treats them rather like pure constructions or fictional entities that nevertheless license different kinds of inferences. I will argue that whereas the isolationist view is still (...) tied to the representationalist understanding of models that takes the model-target dyad as the basic unit of analysis, the constructionist perspective can better accommodate the way we actually acquire knowledge through them. Using the example of Tobin’s ultra-Keynesian model I will show how many of the epistemic characteristics of modelling tend to go unrecognised if too much focus is placed on the model-target dyad. (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.

Using an example of a computer simulation of the convective structure of a red giant star, this paper argues that simulation is a rich inferential process, and not simply a "number crunching" technique. The scientific practice of simulation, moreover, poses some interesting and challenging epistemological and methodological issues for the philosophy of science. I will also argue that these challenges would be best addressed by a philosophy of science that places less emphasis on the representational capacity of theories (and ascribes (...) that capacity instead to models) and more emphasis on the role of theory in guiding (rather than determining) the construction of models. (shrink)

Epistemic justifications for democracy have been offered in terms of two different aspects of decision-making: voting and deliberation, or ‘votes’ and ‘talk.’ The Condorcet Jury Theorem is appealed to as a justification in terms votes, and the Hong-Page “Diversity Trumps Ability” result is appealed to as a justification in terms of deliberation. Both of these, however, are most plausibly construed as models of direct democracy, with full and direct participation across the population. In this paper, we explore how these (...) results hold up if we vary the model so as to reflect the more familiar democratic structure of a representative hierarchy. We first recount extant analytic work that shows that representation inevitably weakens the voting results of the Condorcet Jury Theorem, but we question the ability of that result to shine light on real representative systems. We then show that, when we move from votes to talk, as modeled in Hong-Page, representation holds its own and even has a slight edge. (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)

Discussions of representation in science tend to draw on examples from art. However, such examples need to be handled with care given a) the differences between works of art and scientific theories and b) the accommodation of these examples within certain philosophies of art. I shall examine the claim that isomorphism is neither necessary nor sufficient for representation and I shall argue that there exist accounts of representation in both art and science involving isomorphism which accommodate the (...) apparent counterexamples and, moreover, allow us to understand how “impossible” artistic objects and inconsistent scientific theories can be said to represent. (shrink)

Predictive processing theories are increasingly popular in philosophy of mind; such process theories often gain support from the Free Energy Principle (FEP)—a nor- mative principle for adaptive self-organized systems. Yet there is a current and much discussed debate about conflicting philosophical interpretations of FEP, e.g., repre- sentational versus non-representational. Here we argue that these different interpre- tations depend on implicit assumptions about what qualifies (or fails to qualify) as representational. We deploy the Free Energy Principle (FEP) instrumentally to dis- tinguish (...) four main notions of representation, which focus on organizational, struc- tural, content-related and functional aspects, respectively. The various ways that these different aspects matter in arriving at representational or non-representational interpretations of the Free Energy Principle are discussed. We also discuss how the Free Energy Principle may be seen as a unified view where terms that tradition- ally belong to different ontologies—e.g., notions of model and expectation versus notions of autopoiesis and synchronization—can be harmonized. However, rather than attempting to settle the representationalist versus non-representationalist debate and reveal something about what representations are simpliciter, this paper demon- strates how the Free Energy Principle may be used to reveal something about those partaking in the debate; namely, what our hidden assumptions about what represen- tations are—assumptions that act as sometimes antithetical starting points in this persistent philosophical debate. (shrink)

Mental model construction is supposed to be a useful cognitive devise for learning. Beyond human capacity of constructing mental models, scientists construct complex explanations about phenomena, named scientific or theoretical models. In this work we revisit three vissions: the first one concern about the polisemic term “model”. Our proposal is to discriminate between “mental models” and “explicit models”, being the former those “imaginistic” ideas constructed in scientists’—o teachers—minds, and the latter those teaching devices expressed in different (...) languages that tend to communicate any “scientific model”. From this point of view, the class is considered a place where teachers’ mental models should be learned by novice students by decoding their teaching devices which are expressed in different languages. Other proposal of this work claims to distinguish the term “representation” with respect to its artistical or instrumental origin, highlighting that they are types of teaching devices and that artistical representations are always analogies. Finally, data about the construction of freshmen’s wrong mental models related to the use of the analogy between the chemical combustion and the global process of cellular respiration from glucose is presented to reinforce previous epistemological reflections. (shrink)

My aims here are, firstly, to suggest a minor amendment to R. I. G. Hughes’ DDI account of modeling, so that it could be viewed as a plausible epistemological “model” of how scientific models represent and secondly, to distinguish between two epistemological kinds of models that I call “descriptive” and “constitutive”. This aim is achieved by criticizing Michael Weisberg’s distinction between models and abstract direct representations and by following, at the same time, his own methodological approach for (...) such a distinction. (shrink)

Although scientific models and simulations differ in numerous ways, they are similar in so far as they are posing essentially philosophical problems about the nature of representation. This collection is designed to bring together some of the best work on the nature of representation being done by both established senior philosophers of science and younger researchers. Most of the pieces, while appealing to existing traditions of scientific representation, explore new types of questions, such as: how understanding (...) can be developed within computational science; how the format of representations matters for their use, be it for the purpose of research or education; how the concepts of emergence and supervenience can be further analyzed by taking into account computational science; or how the emphasis upon tractability--a particularly important issue in computational science--sheds new light on the philosophical analysis of scientific reasoning. (shrink)

I argue that if we make explicit the role of the user of scientific representations not only in the application but also in the construction of a model or representation, then inconsistent modeling assumptions do not pose an insurmountable obstacle to our representational practices.

This eprint has been removed by the author's request.

I argue that there are no plausible non-representational explanations of episodes of hallucination. To make the discussion more specific, I focus on visual hallucinations in Charles Bonnet syndrome. I claim that the character of such hallucinatory experiences cannot be explained away non-representationally, for they cannot be taken as simple failures of cognizing or as failures of contact with external reality—such failures being the only genuinely non-representational explanations of hallucinations and cognitive errors in general. I briefly introduce a recent computational model (...) of hallucination, which relies on generative models in the brain, and argue that the model is a prime example of a representational explanation referring to representational mechanisms. The notion of the representational mechanism is elucidated, and it is argued that hallucinations—and other kinds of representations—cannot be exorcised from the cognitive sciences. (shrink)

Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations: representational vehicles that represent their targets by being structurally similar to them. Here, I challenge this assumption, claiming that, at present, it lacks an adequate justification. I examine the only (...) argument offered to establish that generative models are structural representations, and argue that it does not substantiate the desired conclusion. Having so done, I consider a number of alternative arguments aimed at showing that the relevant structural similarity obtains, and argue that all these arguments are unconvincing for a variety of reasons. I then conclude the paper by briefly highlighting three themes that might be relevant for further investigation on the matter. (shrink)

Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and (...) powerful models of memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks. (shrink)

I argue against an account of scientific representation suggested by the semantic, or structuralist, conception of scientific theories. Proponents of this conception often employ the term “model” to refer to bare “structures”, which naturally leads them to attempt to characterize the relation between models and reality as a purely structural one. I argue instead that scientific models are typically “representations”, in the pragmatist sense of the term: they are inherently intended for specific phenomena. Therefore in general scientific (...) models are not (merely) structures. I then explore some consequences of this pragmatist account of representation, and argue that it sheds light upon the distinction between theories and models. I finish by briefly addressing some critical comments due to Bas Van Fraassen. (shrink)

This paper argues that investigations into how to best make something often provide researchers with an objectual understanding of their target phenomena. This argument starts with an extended investigation into the non-representational uses of models. In particular, we identify a special sort of “design model” whose aim is to guide the production of phenomena. Clarifying how these design models are evaluated shows that they are evaluated in different ways than representational models. Once the character of design (...) class='Hi'>models has been fixed, we argue that grasping design models can provide objectual understanding of phenomena. This argument proceeds through a critical engagement with Dellsén’s ( 2020 ) position that a grasp of a good representational model of dependencies provides objectual understanding of a phenomenon. We agree with Dellsén that this is one way to achieve understanding, but maintain that grasping a good design model is another way to achieve understanding. The paper concludes by considering some important objections to our proposal and also by noting some of the broader questions about understanding and knowledge in both science and engineering. (shrink)

Several prominent philosophers of science, most notably Ron Giere, propose that scientific theories are collections of models and that models represent the objects of scientific study. Some, including Giere, argue that models represent in the same way that pictures represent. Aestheticians have brought the picturing relation under intense scrutiny and presented important arguments against the tenability of particular accounts of picturing. Many of these arguments from aesthetics can be used against accounts of representation in philosophy of (...) science. I rely on Dominic Lopes' recent summary of arguments against various views of picturing and reformulate some of them to fit the philosophy of science context. My specific targets here are Giere and Steven French. I go on to argue that assuming all scientific models and images represent in the same way is not the best guide to understanding scientific practice. (shrink)

Understanding the role of ‘‘representations’’ in cognitive science is a fundamental problem facing the emerging framework of embodied, situated, dynamical cognition. To make progress, I follow the approach proposed by an influential representational skeptic, Randall Beer: building artificial agents capable of minimally cognitive behaviors and assessing whether their internal states can be considered to involve representations. Hence, I operationalize the concept of representing as ‘‘standing in,’’ and I look for representations in embodied agents involved in simple categorization tasks. In a (...) first experiment, no representation can be found, but the relevance of the task is undermined by the fact that agents with no internal states can reach high performance. A simple modification makes the task more “representationally hungry,” and in this case, agents’ internal states are found to qualify as representations. I conclude by discussing the benefits of reconciling the embodied-dynamical approach with the notion of representation. (shrink)

I argue that there are no plausible non-representational explanations of episodes of hallucination. To make the discussion more specific, I focus on visual hallucinations in Charles Bonnet syndrome. I claim that the character of such hallucinatory experiences cannot be explained away non-representationally, for they cannot be taken as simple failures of cognizing or as failures of contact with external reality—such failures being the only genuinely non-representational explanations of hallucinations and cognitive errors in general. I briefly introduce a recent computational model (...) of hallucination, which relies on generative models in the brain, and argue that the model is a prime example of a representational explanation referring to representational mechanisms. The notion of the representational mechanism is elucidated, and it is argued that hallucinations—and other kinds of representations—cannot be exorcised from the cognitive sciences. (shrink)

Models are generally used by scientists to obtain predictions and to provide explanations about phenomena. Their predictive and explanatory power is generally thought of as depending on their representative power. It is still not clear, though, in virtue of which features models allow scientists to draw inferences about the system they stand for. In this paper, I focus on a special kind of models, namely imaginary models (I-models) such as the simple pendulum. The main question (...) I address is: how do scientists use I-models in representing target systems? First, I propose a clarification of the very notion of representation, by emphasizing the importance of what I call the format of a representation to the inferences cognitive agents can draw from it. Then, I analyze the various representational relationships that are in play in the use of I-models. I finally conclude that there is no special semantics to be applied to I-models, and that the study of the representational power of models in general should instead focus on the variety of the formats that are used in scientific practice. (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)

RÉSUMÉ: Pour plusieurs, la troisième notion primitive, celle de l'union de l'esprit et du corps, est un ajout obscur et inexplicable dans la philosophie de Descartes, et qui est venu après coup. Je soutiens, pour ma part, que nous pouvons réconcilier la conception que se fait Descartes de cette troisième notion primitive avec l'approche dualiste des Méditations par le biais d'un modèle non représentationnaliste des passions. Je montre, pour y parvenir, que les passions, qui sont des manifestations de la troisième (...) notion primitive, sont autoréflexives et présentent ainsi la même structure que la conscience du cogito. (shrink)

Conceptual understanding of representations and fluency in using representations are important aspects of expertise. However, little is known about how these competencies interact: does representational understanding facilitate learning of fluency, or does fluency enhance learning of representational understanding? We analyze log data obtained from an experiment that investigates the effects of intelligent tutoring systems support for understanding and fluency in connection-making between fractions representations. The experiment shows that instructional support for both representational understanding and fluency are needed for students to (...) benefit from the ITS. In analyzing the ITS log data, we contrast the understanding-first hypothesis and the fluency-first hypothesis, testing whether errors made during the learning phase mediate the effect of experimental condition. Finding that a simple statistical model does not the fit data, we searched over all plausible causal path analysis models. Our results support the understanding-first hypothesis but not the fluency-first hypothesis. (shrink)

This is the preface of the special Issue: Formal Representations in Model-based Reasoning and Abduction, published at the Logic Jnl IGPL (2012) 20 (2): 367-369. doi: 10.1093/jigpal/jzq055 First published online: December 20, 2010.

The detection and categorization of animate motions is a crucial task underlying social interaction and perceptual decision making. Neural representations of perceived animate objects are partially located in the primate cortical region STS, which is a region that receives convergent input from intermediate-level form and motion representations. Populations of STS cells exist which are selectively responsive to specific animated motion sequences, such as walkers. It is still unclear how and to what extent form and motion information contribute to the generation (...) of such representations and what kind of mechanisms are involved in the learning processes. The article develops a cortical model architecture for the unsupervised learning of animated motion sequence representations. We demonstrate how the model automatically selects significant motion patterns as well as meaningful static form prototypes characterized by a high degree of articulation. Such key poses are selectively reinforced during learning through a cross talk between the motion and form processing streams. Furthermore, we show how sequence-selective representations are learned in STS by fusing static form and motion input from the segregated bottom-up driving input streams. Cells in STS, in turn, feed their activities recurrently to their input sites along top-down signal pathways. We show how such learned feedback connections enable predictions about future input as anticipation generated by sequence-selective STS cells. Network simulations demonstrate the computational capacity of the proposed model by reproducing several experimental findings from neurosciences and by accounting for recent behavioral data. (shrink)

This paper erects a framework for analyzing some idealized models as (what I call) inferentially veridical representations. It adopts a version of the semantic view of theories that focuses on properties, and mobilizes conceptual resources associated with properties and the way that properties are related in various ways. The outcome is an elaboration of some aspects of the analysis of Jones (2005).