This essay starts at the well-known thesis according to which ‘to know something is to know its cause’, its reason. Not disagreeing with that at all, we mean to set the proper domain and reaching of the thesis once considering its limitations and effects for modern and contemporary way of thinking. Does may human thought be just representation, or not? If it does not, the thesis will actually make philosophers stay away from the originary pathos as we may show. If (...) it does, what it is left to think about but mind productions? (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)

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, (...) that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory led to transitive causal judgments. On the other hand, chains schematized as multiple chunks led to intransitive judgments despite strong intermediate links. Normative accounts of causal intransitivity could not explain these intransitive judgments. (shrink)

Some recent cognitive-scientific research suggests that a considerable amount of intelligent action is generated not by the systematic activity of internal representations, but by complex interactions involving neural, bodily, and environmental factors. Following an analysis of this threat to representational explanation, we pursue an analogy between the role of genes in the production of biological form and the role of neural states in the production of behaviour, in order to develop a notion of genic representation. In both cases an appeal (...) to normal ecological context is used to balance multi-factoral, interactive causal determination against the intuition that certain aspects of the causal nexus play a special role in promoting adaptive success. Certain worries abut this vision help us to get a better grip on the concept of genic representation itself. We end with a puzzle concerning the relation between cognition and representation. (shrink)

In “Connectionism and the fats of folk psychology”, Forster and Saidel argue that the central claim of Ramsey, Stich and Garon (1991)—that distributed connectionist models are incompatible with the causal discreteness of folk psychology—is mistaken. To establish their claim, they offer an intriguing model which allegedly shows how distributed representations can function in a causally discrete manner. They also challenge our position regarding projectibility of folk psychology. In this essay, I offer a response to their account and show how their (...) model fails to demonstrate that our original argument was mistaken. While I will discuss several difficulties with their model, my primary criticism will be that the features of their model that are causally discrete are not truly distributed, while the features that are distributed are not really discrete. Concerning the issue of projectibility, I am more inclined to agree with Forster and Saidel and I offer a revised account of what we should have said originally. (shrink)

In her landmark book, Language, Thought, and Other Biological Categories (Millikan1984),1 Ruth Garrett Millikan utilizes the idea of a biological function to solve philosophical problems associated with the phenomena of language, thought, and meaning. Language and thought are activities of biological organisms, according to Millikan, and we should treat them as such when trying to answer related philosophical questions. Of special interest is Millikan’s treatment of intentionality. Here Millikan employs the notion of a biological function to explain what it is (...) for one thing in nature, a bee dance (43), for example, to be about another, in this case, the location of a nectar source. My concern in this paper is to understand whether Millikan’s account of intentionality adequately explains how humans achieve reference, in language or thought, to individuals and groups in their environment. In bringing her theory of intentional content to bear on human activities, Millikan focuses largely on natural language. Thus, in what follows, I begin by laying out the biology-based principles that underlie Millikan’s theory of content, then proceed with an explanation of how the theory is to apply to natural language. As it appears, Millikan’s account of how content is determined for natural language terms and sentences rests on the determinacy of intentional content at the psychological level. This leads me to take a careful look at what Millikan says about the content of mental representations, in hopes of finding a sufficient basis there for the application of Millikan’s theory of content to natural language. Ultimately, I conclude that Millikan’s theory faces a problem of vacuity. If we approach the theory as a theory of intentional content, intended to explain the nature of reference, the theory is lacking in an extremely important respect: Millikan explains how it could be one of the biological functions of a mental or natural language term to refer, without telling us precisely what in the natural order constitutes the reference relation.. (shrink)

'Representation' is a concept which occurs both in cognitive science and philosophy. It has common features in both settings in that it concerns the explanation of behaviour in terms of the way the subject categorizes and systematizes responses to its environment. The prevailing model sees representations as causally structured entities correlated on the one hand with elements in a natural language and on the other with clearly identifiable items in the world. This leads to an analysis of representation and cognition (...) in terms of formal symbols and their relations. But human perception and cognition use multiple informational constraints and deal with unsystematic and messy input in a way best explained by Parallel Distributed Processing models. This undermines the claim that a formal representational theory of mind is 'the only game in town.' In particular it suggests a radically different model of brain function and its relation to epistemology from that found in current representational theories. (shrink)

Pictorial representation is a subject of interest to both cognitive science and aesthetics. Standard theories of depiction often draw on vision science, and vision science must give an account of picture perception. I offer a critical overview of standard theories of depiction and argue that none of them is adequate. I then describe ways in which new theories of perception blend elements of representationalism with an emphasis on attention and motor control. Such theories, in effect, limit the reliance on mental (...) representation in perceptual tasks. This work provides the basis for a theory of depiction in which pictorial representation is explained in terms of both mental representations and perceptual strategies. I argue that, in the case, the mental representations are most plausibly individuated by the functional and conceptual roles, rather than by causal links to the external world. (shrink)

Judea Pearl has argued that counterfactuals and causality are central to intelligence, whether natural or artificial, and has helped create a rich mathematical and computational framework for formally analyzing causality. Here, we draw out connections between these notions and various current issues in cognitive science, including the nature of mental “programs” and mental representation. We argue that programs (consisting of algorithms and data structures) have a causal (counterfactual-supporting) structure; these counterfactuals can reveal the nature of mental representations. Programs can also (...) provide a causal model of the external world. Such models are, we suggest, ubiquitous in perception, cognition, and language processing. (shrink)

Using as a framework the logical treatment of causality in the Buddhist Madhyamika, a theory of the psychology of event coherence and causal connectedness is developed, and suggestive experimental evidence is offered. The basic claim is that events are perceived as coherent and causally bound to the extent that the outcome is seen to be already contained in the ground of the event in some form and the connecting link between them is seen as the appropriate means for changing the (...) outcome-in-the-ground to the outcome-as-perceived. There are four types of such connections: the identity of the object in the ground and outcome is seen as the same ; a property is seen to be transferred from ground to outcome ; for animate beings, a cognitive representation and a state of the world are seen to match - either the representation-as-outcome coming to match the world-as-ground or the world-as-outcome coming to match the representation-as-ground ; the `essence' of a category is seen to manifest itself . The standard critique of such coherence explanations is that they are tautological. We demonstrate that `nontautological' scientific accounts become convincing coherent explanations only to he extent that the outcome is re-introduced into the ground. Explanations which are noncausal altogether, such as probability or chance, are shown to be psychologically unstable. This critique suggests some new perspectives on causal thinking both in the cognitive sciences and the folk theories of daily life. (shrink)

Much has been written on the role of causal notions and causal reasoning in the so-called 'special sciences' and in common sense. But does causal reasoning also play a role in physics? Mathias Frisch argues that, contrary to what influential philosophical arguments purport to show, the answer is yes. Time-asymmetric causal structures are as integral a part of the representational toolkit of physics as a theory's dynamical equations. Frisch develops his argument partly through a critique of anti-causal arguments and partly (...) through a detailed examination of actual examples of causal notions in physics, including causal principles invoked in linear response theory and in representations of radiation phenomena. Offering a new perspective on the nature of scientific theories and causal reasoning, this book will be of interest to professional philosophers, graduate students, and anyone interested in the role of causal thinking in science. (shrink)

This paper is aimed at identifying how a model’s explanatory power is constructed and identified, particularly in the practice of template-based modeling (Humphreys, Philos Sci 69:1–11, 2002; Extending ourselves: computational science, empiricism, and scientific method, 2004), and what kinds of explanations models constructed in this way can provide. In particular, this paper offers an account of non-causal structural explanation that forms an alternative to causal–mechanical accounts of model explanation that are currently popular in philosophy of biology and cognitive science. Clearly, (...) defences of non-causal explanation are far from new (e.g. Batterman, Br J Philos Sci 53:21–38, 2002a; The devil in the details: asymptotic reasoning in explanation, reduction, and emergence, 2002b; Pincock, Noûs 41:253–275, 2007; Mathematics and scientific representation 2012; Rice, Noûs. doi:10.1111/nous.12042, 2013; Biol Philos 27:685–703, 2012), so the targets here are focused on a particular type of robust phenomenon and how strong invariance to interventions can block a range of causal explanations. By focusing on a common form of model construction, the paper also ties functional or computational style explanations found in cognitive science and biology more firmly with explanatory practices across model-based science in general. (shrink)

La philosophie contemporaine connaît une demi-douzaine de théories de la causalité. À l'époque de Kant et de Hume leur nombre a été moindre, à l'avenir on peut s'attendre à ce que leur nombre continue d'augmenter. Parmi les affirmations faites par ces théories sur la nature de la causalité, certaines sont compatibles entre elles, mais beaucoup ne le sont pas. Par conséquent, ou bien quelques-unes de ces théories sont fausses, ou bien elles ne portent pas sur le même objet. Dans ce (...) dernier cas, il y aurait plusieurs genres de causalité : différentes théories diraient quelque chose de correct à propos de différents genres de causalité. Dans cette situation, il apparaît judicieux de commencer par poser deux questions méta-théoriques : quelle est la tâche d'une théorie de la causalité, et quelles sont les données dont une telle théorie doit tenir compte ? Dans une portion considérable de la littérature philosophique sur la causalité, on cherche en vain une réponse claire à la question de savoir si le but de la théorie est l'explication du concept de causalité sous-jacent à notre pratique effective de jugement, ou au contraire sa révision. Cela va souvent de pair avec l'absence d'une justification du fait que la relation que la théorie se propose de définir peut bien être appelée relation causale. Certaines théories de la causalité hautement élaborées sur le plan technique, comme par exemple certaines variantes probabilistes de la théorie de la régularité où les théories plus récentes du transfert, donnent parfois l'impression que c'est la physique qui détient le monopole de définition en ce qui concerne la nature de la relation causale. Un représentant de la théorie du transfert affirme avec un pathos scientiste inébranlable „que la science physique a découvert la nature de la relation causale dans un large ensemble de cas“. Mais comment les sciences de la nature pourraient-elles découvrir quelque chose de ce genre ? La clarification de l'essence de la relation causale est une tâche philosophique par excellence qui relève plus particulièrement de la métaphysique. Le fait qu'il soit souvent nécessaire, au cours d'une recherche métaphysique, de faire appel à des connaissances scientifiques, ne change rien à l'affaire. Il s'agit d'une tache de métaphysique descriptive, que Strawson a caractérisée comme l'entreprise qui consiste à dégager les traits les plus généraux de la structure effective de notre pensée sur le monde. L'idiome causal est profondément ancré dans les langues naturelles ; et le concept de causalité que nous possédons effectivement se reflète dans notre pratique de jugements causaux. Cela ne signifie pas qu’il soit possible d'extraire directement de cette pratique un concept cohérent de causalité. Notre pratique de jugement possède de nombreux aspects et n'est pas dépourvue d'éléments douteux. Pour des besoins philosophiques, notre pratique effective de jugement a besoin d'une certaine discipline ; c'est en ce sens que je parle de la pratique éclairée de jugement causal. Je considère que c'est notre pratique éclairée de jugement causal qui fournit les données dont la théorie de la causalité doit tenir compte ; et la tâche primordiale d'une telle théorie consiste à indiquer les conditions de vérité de cas non controversés d'énoncés causaux singuliers. [...] . (shrink)

A common strategy for simplifying complex systems involves partitioning them into subsystems whose behaviors are roughly independent of one another at shorter timescales. Dynamic causal models clarify how doing so reveals a system’s nonequilibrium causal relationships. Here I use these models to elucidate the idealizations and abstractions involved in representing a system at a timescale. The models reveal that key features of causal representations—such as which variables are exogenous—may vary with the timescale at which a system is considered. This has (...) implications for debates regarding which systems can be represented causally. (shrink)

Previous studies have shown a robust bias to express the goal path over the source path when describing events. Motivated by linguistic theory, this study manipulated the causal structure of events and measured the extent to which adults and 3.5- to 4-year-old English-speaking children included the goal and source in their descriptions. We found that both children's and adults’ encoding of the source increased for events in which the source caused the motion of the figure compared to nearly identical events (...) in which the source played no such causal role. However, a goal bias persisted overall for both causal and noncausal motion events. These findings suggest that although the goal bias in language is highly robust, properties of the source influence its likelihood of being encoded in language, thus shedding light on how properties of an event can influence the mapping of event components into language. (shrink)

Previous studies have shown a robust bias to express the goal path over the source path when describing events (“the bird flew into the pitcher,” rather than “… out of the bucket into the pitcher”). Motivated by linguistic theory, this study manipulated the causal structure of events (specifically, making the source cause the motion of the figure) and measured the extent to which adults and 3.5‐ to 4‐year‐old English‐speaking children included the goal and source in their descriptions. We found that (...) both children's and adults’ encoding of the source increased for events in which the source caused the motion of the figure compared to nearly identical events in which the source played no such causal role. However, a goal bias persisted overall for both causal and noncausal motion events. These findings suggest that although the goal bias in language is highly robust, properties of the source (such as causal agency) influence its likelihood of being encoded in language, thus shedding light on how properties of an event can influence the mapping of event components into language. (shrink)

Noam Chomsky and Frances Egan argue that David Marr’s computational theory of vision is not intentional, claiming that the formal scientific theory does not include description of visual content. They also argue that the theory is internalist in the sense of not describing things physically external to the perceiver. They argue that these claims hold for computational theories of vision in general. Beyond theories of vision, they argue that representational content does not figure as a topic within formal computational theories (...) in cognitive science. I demonstrate that Chomsky’s and Egan’s claims about Marr’s theory are false. Marr’s computational theory contains a mathematical theory of visual content, based on empirical psychophysical evidence. It also contains mathematical descriptions of distal physical surfaces and objects, and of their optic projection to the perceiver. Much computational research on vision contains these types of intentional and externalist components within the formal, mathematical, theories. Chomsky’s and Egan’s claims demonstrate inadequate study and understanding of Marr’s work and other research in this area. Computational theories of vision, by containing empirically based mathematical theories of visual content, to this extent present naturalizations of semantics. (shrink)

Structural representations are increasingly popular in philosophy of cognitive science. A key virtue they seemingly boast is that of meeting Ramsey's job description challenge. For this reason, structural representations appear tailored to play a clear representational role within cognitive architectures. Here, however, I claim that structural representations do not meet the job description challenge. This is because even our most demanding account of their functional profile is satisfied by at least some receptors, which paradigmatically fail the job description challenge. Hence, (...) the functional profile typically associated with structural representations does not identify representational posits. After a brief introduction, I present, in the second section of the paper, the job description challenge. I clarify why receptors fail to meet it and highlight why, as a result, they should not be considered representations. In the third section I introduce what I take to be the most demanding account of structural representations at our disposal, namely Gładziejewski's account. Provided the necessary background, I turn from exposition to criticism. In the first half of the fourth section, I equate the functional profile of structural representations and receptors. To do so, I show that some receptors boast, as a matter of fact, all the functional features associated with structural representations. Since receptors function merely as causal mediators, I conclude structural representations are mere causal mediators too. In the second half of the fourth section I make this conclusion intuitive with a toy example. I then conclude the paper, anticipating some objections my argument invites. (shrink)

Bayes nets are formal representations of causal systems that many psychologists have claimed as plausible mental representations. One purported advantage of Bayes nets is that they may provide a theory of counterfactual conditionals, such as If Calvin had been at the party, Miriam would have left early. This article compares two proposed Bayes net theories as models of people's understanding of counterfactuals. Experiments 1-3 show that neither theory makes correct predictions about backtracking counterfactuals (in which the event of the if-clause (...) occurs after the event of the then-clause), and Experiment 4 shows the same is true of forward counterfactuals. An amended version of one of the approaches, however, can provide a more accurate account of these data. (shrink)

An early, very preliminary edition of this book was circulated in 1962 under the title Set-theoretical Structures in Science. There are many reasons for maintaining that such structures play a role in the philosophy of science. Perhaps the best is that they provide the right setting for investigating problems of representation and invariance in any systematic part of science, past or present. Examples are easy to cite. Sophisticated analysis of the nature of representation in perception is to be found already (...) in Plato and Aristotle. One of the great intellectual triumphs of the nineteenth century was the mechanical explanation of such familiar concepts as temperature and pressure by their representation in terms of the motion of particles. A more disturbing change of viewpoint was the realization at the beginning of the twentieth century that the separate invariant properties of space and time must be replaced by the space-time invariants of Einstein's special relativity. Another example, the focus of the longest chapter in this book, is controversy extending over several centuries on the proper representation of probability. The six major positions on this question are critically examined. Topics covered in other chapters include an unusually detailed treatment of theoretical and experimental work on visual space, the two senses of invariance represented by weak and strong reversibility of causal processes, and the representation of hidden variables in quantum mechanics. The final chapter concentrates on different kinds of representations of language, concluding with some empirical results on brain-wave representations of words and sentences. (shrink)

Many philosophers and psychologists have attempted to elucidate the nature of mental representation by appealing to notions like isomorphism or abstract structural resemblance. The ‘structural representations’ that these theorists champion are said to count as representations by virtue of functioning as internal models of distal systems. In his 2007 book, Representation Reconsidered, William Ramsey endorses the structural conception of mental representation, but uses it to develop a novel argument against representationalism, the widespread view that cognition essentially involves the manipulation of (...) mental representations. Ramsey argues that although theories within the ‘classical’ tradition of cognitive science once posited structural representations, these theories are being superseded by newer theories, within the tradition of connectionism and cognitive neuroscience, which rarely if ever appeal to structural representations. Instead, these theories seem to be explaining cognition by invoking so-called ‘receptor representations’, which, Ramsey claims, aren’t genuine representations at all—despite being called representations, these mechanisms function more as triggers or causal relays than as genuine stand-ins for distal systems. I argue that when the notions of structural and receptor representation are properly explicated, there turns out to be no distinction between them. There only appears to be a distinction between receptor and structural representations because the latter are tacitly conflated with the ‘mental models’ ostensibly involved in offline cognitive processes such as episodic memory and mental imagery. While structural representations might count as genuine representations, they aren’t distinctively mental representations, for they can be found in all sorts of non-intentional systems such as plants. Thus to explain the kinds of offline cognitive capacities that have motivated talk of mental models, we must develop richer conceptions of mental representation than those provided by the notions of structural and receptor representation. (shrink)

Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice (...) shows the need for quantitative, probabilistic models of mechanisms, too. In this paper we argue that the formal framework of causal graph theory is well-suited to provide us with models of biological mechanisms that incorporate quantitative and probabilistic information. On the ba-sis of an example from contemporary biological practice, namely feedback regulation of fatty acid biosynthesis in Brassica napus, we show that causal graph theoretical models can account for feedback as well as for the multi-level character of mechanisms. However, we do not claim that causal graph theoretical representations of mechanisms are advantageous in all respects and should replace common qualitative models. Rather, we endorse the more balanced view that causal graph theoretical models of mechanisms are useful for some purposes, while being insufficient for others. (shrink)

BACKGROUND: Representing is about theories and theory formation. Philosophy of science has a long-standing interest in representing. At least since Ian Hacking's modern classic Representing and Intervening analytical philosophers have struggled to combine that interest with a study of the roles of intervention studies. With few exceptions this focus of philosophy of science has been on physics and other natural sciences. In particular, there have been few attempts to analyse the use of the notion of intervention in other disciplines where (...) intervention studies are important, such as in nursing research. One unintended consequence of this is that the relations between representing and intervening tend to be less understood outside the natural sciences. OBJECTIVES AND DESIGN: This article highlights a number of possible topics on which nursing science and analytic philosophy of science can fruitfully interact. The basic idea is simple. Building on a characterisation of interventions in terms of what is intervened on and with respect to what, we suggest that interventions in nursing research typically are a blend of varieties belonging to the three dimensions of agency, epistemology, and ontology. The details of the blend determine the relation of the particular intervention study to traditional representational categories such as inductivism and hypothetico-deductive method, and have a bearing on its explanatory power and other more theory independent features of research as well. The framework we suggest should be relevant for nurse researchers who want to adopt a more general and analytically entrenched perspective on representing and intervening than the methodological boundaries in nursing research typically allow. (shrink)

This paper responds to critical commentaries on my book, Perceiving Reality (OUP, 2012), by Laura Guerrero, Matthew MacKenzie, and Anand Vaidya. Guerrero focuses on the metaphysics of causation, and its role in the broader question of whether the ‘two truths’ framework of Buddhist philosophy can be reconciled with the claim that science provides the best account of our experienced world. MacKenzie pursues two related questions: (i) Is reflexive awareness (svasaṃvedana) identical with the subjective pole of a dual-aspect cognition or are (...) there alternative, perhaps better, ways of understanding this self-intimating character of mental states? (ii) Is perception constitutively intentional or is it representational? Vaidya argues that, in so far as Husserlian phenomenology and Buddhism differ in terms of their fundamental ontological commit- ments, they must be incompatible, thus rendering any cross-cultural philosophical project that seeks their rapprochement tenuous. One of my aims in Perceiving Reality is to show how accounts of perception informed by metaphysical realism can be problematic on both metaphysical and epistemological grounds, especially when relying on conceptions of consciousness that ignore its properly phenomenological features. (shrink)

A formal theory of causal reasoning is presented that encompasses both Pearl’s approach to causality and several key formalisms of nonmonotonic reasoning in Artificial Intelligence. This theory will be derived from a single rationality principle of causal acceptance for propositions. However, this principle will also set the theory of causal reasoning apart from common representational approaches to reasoning formalisms.

Functionalism faces a problem in accounting for the semantic powers of beliefs and other mental states. Simple causal considerations will not solve this problem, nor will any appeal to the social utility of semantic interpretations. The correct analysis of semantic representation is a teleological one, in terms of the biological purposes of mental states: whereas functionalism focuses, so to speak, only on the structure of the cognitive mechanism, the semantic perspective requires in addition that we consider the purposes of the (...) cognitive mechanism's parts. (shrink)

Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser (2016) pointed out—they have problems with capturing relevant spatial and structural information. In this paper we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automata. Our approach can incorporate spatial and structural information while, at the same time, it comes with all (...) the merits of a CBN representation of mechanisms. (shrink)

Martin and Deutscher’s causal theory of remembering holds that a memory trace serves as a necessary causal link between any genuine episode of remembering and the event it enables one to recall. In recent years, the causal theory has come under fire from researchers across philosophy and cognitive science, who argue that results from the scientific study of memory are incompatible with the kinds of memory traces that Martin and Deutscher hold essential to remembering. Of special note, these critics observe, (...) is that a single memory trace can be shaped by multiple past experiences. This appears to prevent traces from underwriting Martin and Deutscher’s distinction between remembering an event and merely forming an accurate representation of it. This paper accepts such criticisms of the standard causal theory and, through considering the phenomenon forgetting through repetition, raises several others. A substantially revised causal theory is then developed, compatible with the thesis that individual memory traces are shaped by multiple past experiences. The key strategy is to conceive of episodic remembering not as the simple retrieval and projection of a static memory trace, but as a complex quasi-inferential process that makes use of multiple forms of information and cues—“prop-like” memory traces included—in generating the experience known as episodic remembering. When remembering is understood as a multi-componential process, there are a variety of ways in which a representation of the past may be appropriately causally dependent upon a prior experience of the event remembered. (shrink)

An increasing number of arguments for causal pluralism invoke empirical psychological data. Different aspects of causal cognition—specifically, causal perception and causal inference—are thought to involve distinct cognitive processes and representations, and they thereby distinctively support transference and dependency theories of causation, respectively. We argue that this dualistic picture of causal concepts arises from methodological differences, rather than from an actual plurality of concepts. Hence, philosophical causal pluralism is not particularly supported by the empirical data. Serious engagement with cognitive science reveals (...) that the connection between psychological concepts of causation and philosophical notions is substantially more complicated than is traditionally presumed. (shrink)

It is natural to think of remembering in terms of causation: I can recall a recent dinner with a friend because I experienced that dinner. Some fifty years ago, Martin and Deutscher (1966) turned this basic thought into a full-fledged theory of memory, a theory that came to dominate the landscape in the philosophy of memory. Remembering, Martin and Deutscher argue, requires the existence of a specific sort of causal connection between the rememberer's original experience of an event and his (...) later representation of that event: a causal connection sustained by a memory trace. In recent years, it has become apparent that this reference to memory traces may be out of step with memory science. Contemporary proponents of the causal theory are thus confronted with the question: is it possible to develop an empirically adequate version of the theory, or is it time to move beyond it? This chapter traces the recent history of the causal theory, showing how increased awareness of the theory’s problems has led to the development of modified version of the causal theory and ultimately to the emergence of postcausal theories. (shrink)

Nominal concepts represent things as tokens of types. Recent research suggests that we represent principled connections between the type of thing something is (e.g., DOG) and some of its properties (k‐properties; e.g., having four legs for dogs) but not other properties (t‐properties; e.g., being brown for dogs). Principled connections differ from logical, statistical, and causal connections. Principled connections license (i) the expectation that tokens of the type will generally possess their k‐properties, (ii) formal explanations (i.e., explanation of the presence of (...) k‐properties in tokens of a type by reference to the type of thing it is), and (iii) normative expectations concerning the presence of k‐properties in tokens of the type. The present paper investigates the hypothesis that representing principled connections requires representing properties as aspects of being the relevant kind of thing (Aspect Hypothesis). Experiment 1 provides a direct test of the Aspect Hypothesis. Experiments 2 and 3 provide indirect tests of the Aspect Hypothesis. All three experiments provide support for the Aspect Hypothesis. Experiment 4 investigates a prediction of the Aspect Hypothesis concerning the manner in which formal explanations are licensed by principled connections. Finally, Experiment 5 investigates a prediction of the Aspect Hypothesis concerning the nature of the normative expectations licensed by principled connections. Together these results provide strong evidence for the idea that representing principled connections involves representing a property as being an aspect of being a given kind of thing. The results also help clarify the manner in which formal explanation differs from other modes of explanation. Finally, the results of the experiments are used to motivate a proposal concerning the formal structure of the conceptual representations implicated by principled connections. This structure provides a domain‐general way of structuring our concepts and embodies the perspective we take when we think and talk of things as being instances of a kind. (shrink)

& Carnegie Mellon University Abstract The rationality of human causal judgments has been the focus of a great deal of recent research. We argue against two major trends in this research, and for a quite different way of thinking about causal mechanisms and probabilistic data. Our position rejects a false dichotomy between "mechanistic" and "probabilistic" analyses of causal inference -- a dichotomy that both overlooks the nature of the evidence that supports the induction of mechanisms and misses some important probabilistic (...) implications of mechanisms. This dichotomy has obscured an alternative conception of causal learning: for discrete events, a central adaptive task is to induce causal mechanisms in the environment from probabilistic data and prior knowledge. Viewed from this perspective, it is apparent that the probabilistic norms assumed in the human causal judgment literature often do not map onto the mechanisms generating the probabilities. Our alternative conception of causal judgment is more congruent with both scientific uses of the notion of causation and observed causal judgments of untutored reasoners. We illustrate some of the relevant variables under this conception, using a framework for causal representation now widely adopted in computer science and, increasingly, in statistics. We also review the formulation and evidence for a theory of human causal induction (Cheng, 1997) that adopts this alternative conception. (shrink)

The application of the formal framework of causal Bayesian Networks to children’s causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system. Three experiments examined whether children are able to make correct inferences about interventions on different causal structures. The first two experiments examined whether children’s causal structure and intervention judgments were consistent with one another. In Experiment 1, children (...) aged between 4 and 8 years made causal structure judgments on a three-component causal system followed by counterfactual intervention judgments. In Experiment 2, children’s causal structure judgments were followed by intervention judgments phrased as future hypotheticals. In Experiment 3, we explicitly told children what the correct causal structure was and asked them to make intervention judgments. The results of the three experiments suggest that the representations that support causal structure judgments do not easily support simple judgments about interventions in children. We discuss our findings in light of strong interventionist claims that the two types of judgments should be closely linked. (shrink)

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)

A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are (...) directly connected by causal relationships. These expectations also included sensitivity to higher‐order feature interactions that emerge from the asymmetries inherent in causal relationships. Quantitative fits of causal‐model theory were superior to those obtained with extensions to traditional similarity‐based models that represent causal knowledge either as higher‐order relational features or “prior exemplars” stored in memory. (shrink)

Many philosophers have understood the representational dimension of affective states along the model of perceptual experiences. This paper argues affective experiences involve a kind of personal level affective representation disanalogous from the representational character of perceptual experiences. The positive thesis is that affective representation is a non-transparent, non-sensory form of evaluative representation, whereby a felt valenced attitude represents the object of the experience as minimally good or bad, and one experiences that evaluative standing as having the power to causally motivate (...) the relevant attitude. I show how this view can make sense of distinctive features of affective experiences, such as their valence and connection to value in a way which moves beyond current evaluativist views of affect. (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)

How do we make causal judgments? Many studies have demonstrated that people are capable causal reasoners, achieving success on tasks from reasoning to categorization to interventions. However, less is known about the mental processes used to achieve such sophisticated judgments. We propose a new process model—the mutation sampler—that models causal judgments as based on a sample of possible states of the causal system generated using the Metropolis–Hastings sampling algorithm. Across a diverse array of tasks and conditions encompassing over 1,700 participants, (...) we found that our model provided a consistently closer fit to participant judgments than standard causal graphical models. In particular, we found that the biases introduced by mutation sampling accounted for people's consistent, predictable errors that the normative model by definition could not. Moreover, using a novel experimental methodology, we found that those biases appeared in the samples that participants explicitly judged to be representative of a causal system. We conclude by advocating sampling methods as plausible process‐level accounts of the computations specified by the causal graphical model framework and highlight opportunities for future research to identify not just what reasoners compute when drawing causal inferences, but also how they compute it. (shrink)

Over the past few decades, the dominant approach to explaining intentionality has been a naturalistic approach, one appealing only to non-mental ingredients condoned by the natural sciences. Karen Neander’s A Mark of the Mental (2017) is the latest installment in the naturalist project, proposing a detailed and systematic theory of intentionality that combines aspects of several naturalistic approaches, invoking causal relations, teleological functions, and relations of second-order similarity. In this paper, we consider the case of perceptual representations of colors, which (...) is a challenging case for Neander’s theory. This case will brings out a general methodological concern with Neander’s and other naturalistic theories: these theories generally rest on the assumption that the mental intentionality we are acquainted with in everyday life—the phenomenon exhibited by desires for cups of coffee, perceptual experiences of dogs playing in yards, and thoughts about the weather—is the very same kind of phenomenon that cognitive science studies under labels such as “mental representation” and, in some cases, “information processing.” This assumption is dubious, as the case of Neander’s theory illustrates. (shrink)

This volume addresses fundamental issues in the philosophy of science in the context of two most intriguing fields: biology and economics. Written by authorities and experts in the philosophy of biology and economics, Mechanism and Causality in Biology and Economics provides a structured study of the concepts of mechanism and causality in these disciplines and draws careful juxtapositions between philosophical apparatus and scientific practice. By exploring the issues that are most salient to the contemporary philosophies of biology and economics and (...) by presenting comparative analyses, the book serves as a platform not only for gaining mutual understanding between scientists and philosophers of the life sciences and those of the social sciences, but also for sharing interdisciplinary research that combines both philosophical concepts in both fields. -/- The book begins by defining the concepts of mechanism and causality in biology and economics, respectively. The second and third parts investigate philosophical perspectives of various causal and mechanistic issues in scientific practice in the two fields. These two sections include chapters on causal issues in the theory of evolution; experiments and scientific discovery; representation of causal relations and mechanism by models in economics. The concluding section presents interdisciplinary studies of various topics concerning extrapolation of life sciences and social sciences, including chapters on the philosophical investigation of conjoining biological and economic analyses with, respectively, demography, medicine and sociology. (shrink)

Let’s consider the following paradox (Fodor [1989], Jackson and Petit [1988] [1992], Drestke [1988], Block [1991], Lepore and Loewer [1987], Lewis [1986], Segal and Sober [1991]): i) The intentional content of a thought (or any other intentional state) is causally relevant to its behavioural (and other) effects. ii) Intentional content is nothing but the meaning of internal representations. But, iii) Internal processors are only sensitive to the syntactic structures of internal representations, not their meanings. Therefore it seems that if we (...) want to defend the idea -absolutely plausible from an intuitive point of view- that mental / intentional states are causally responsible for behavioural outputs and we want to do it on the physicalist basis of any scientific methodology, we will have to give up the conviction that such intentional states qua intentional, i. e. as having a particular meaning, are the ones causally responsible for our behaviour. The path that takes us to mental epiphenomenalism is clear: 1) the causal powers of any event are completely determined by its physical properties; 2) although intentional properties supervene on physical properties, they can’t be identified with them; 3) intentional properties, as intentional, are not causally responsible for behaviour, because they don’t take part in the causal powers of the states to which they belong, i. e., intentional properties are epiphenomenal. Let’s consider now a different yet parallel position to the one just described. There is an important debate in cognitive science about whether the class of mechanisms to which we belong and which the computational modelling project of cognitive processes refers to is best represented by classical or connectionist approaches (McClelland, Rumelhart et. al [1986], Smolensky [1987] [1988], Fodor and Pylyshyn [1988], Pinker and Prince [1988], Clark [1989], Ramsey, Stich and Rumelhart [1991], Clark and Karmiloff-Smith [forthcoming]).In classical, serial processing models, information is encoded in terms of rules that have a linguistic character. In connectionist or parallel distributed processing (PDP) models, the causal relationships among the units that constitute the system determine how the information is processes by the network, although these units don’t have a direct semantic interpretation. But, for both, classical and connectionist models, all the computations can be explained without any reference to the content of the processed information, i.e., in both cases the properties that seem to be responsible for the system’s behaviour are ultimately physical properties nor intentional ones. This situation mirrors thus, within cognitive science, the philosophical discussion concerning the causal efficacy of semantic properties. Now, if in this debate we opt for the classical paradigm, there is a way of finding a solution to the computational version of the epiphenomenalism paradox. This solution is based mainly on the notion of supervenience or, more precisely, on the notion of mereological supervenience (Kim [1984] [1988])1 . The idea of intentional properties supervening on physical properties makes sense within the classical context because there exists an easily isolable supervenience base comprising the syntactic items in the socalled language of thought. But, what happens if we opt for the connectionist paradigm?. The situation here doesn’t seem to favour the use of the same supervenience strategy. For it has been argued (Ramsey, Stich and Garon [1991]) that beliefs, desires, and other mental states are nor, in the connectionist paradigm, individuable as weight or activation states of the system. This is because information is encoded by the network in distributed and superpositional representations, i.e., there are no straightforwardly isolable vehicles at the physical level that can be identified as the articulated supervenience base on which the semantic properties supervene2 . If this is true, then the connectionist not only loses the battle against epiphenomenalism but more drastically, seems to offer a standing invitation to eliminativism, since talk of beliefs and desires, etc. now seems to be floating free of any acceptable scientific underpinning, i.e., she has lost the necessary theoretical apparatus for supporting the intuitive idea that propositional attitudes -beliefs, desires and any mental states with semantic content- are physically realized. This second line of argumentation doesn’t take us to a paradox but to a dilemma: either we accept eliminativism, if connectionist hypothesis are correct or we defend the causal efficacy of mental states with semantic content by showing that, after all, connectionist networks are not plausible cognitive models (Davies [1991]). From my point of view, however, both lines of argumentation need to be revised. The aim of this paper is to find an account of the causal efficacy of content that avoids the aforementioned epiphenomenalist objections and that doesn’t require the discovery of inner symbols in the computational modelling of such contentful mental states. In short, the aim is to find a meeting point where a philosophical story about content and cause and the connectionist computational model can be brought together (Cfr. Clark [1989]). (shrink)

We explore the contribution made by oscillatory, synchronous neural activity to representation in the brain. We closely examine six prominent examples of brain function in which neural oscillations play a central role, and identify two levels of involvement that these oscillations take in the emergence of representations: enabling (when oscillations help to establish a communication channel between sender and receiver, or are causally involved in triggering a representation) and properly representational (when oscillations are a constitutive part of the representation). We (...) show that even an idealized informational sender–receiver account of representation makes the representational status of oscillations a non-trivial matter, which depends on rather minute empirical details. (shrink)

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. (...) We replicated these findings with less‐informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge. (shrink)

It is consistent with the evidence in The Origin of Concepts to conjecture that infants' causal representations, like their numerical representations, are not continuous with adults', so that bootstrapping is needed in both cases.

Although Newton carefully eschews questions about gravity’s causal basis in the published Principia, the original version of his masterwork’s third book contains some intriguing causal language. “These forces,” he writes, “arise from the universal nature of matter.” Such remarks seem to assert knowledge of gravity’s cause, even that matter is capable of robust and distant action. Some commentators defend that interpretation of the text—a text whose proper interpretation is important since Newton’s reasons for suppressing it strongly suggest that he continued (...) to endorse its ideas. This article argues that the surface appearance of Newton’s causal language is deceptive. What does New- ton intend with his causal language if not a full causal hypothesis? His remarks actually indicate a way of considering the force mathematically, something he contrasts to the structure of the force as it really is in nature. In explaining that, he identifies a significant disjunction between the physical force itself and mathematical ways of considering it, and the text’s significance lies in its view of the force’s structure and in the questions raised about the relationship between mathematical representations and the physical world. (shrink)

Some proponents of structural representations (henceforth, structuralists) claim that no other theory of representation can legitimatize the explanatory appeals that cognitive science makes to mental content. Because other naturalistic approaches to representation purportedly posit an arbitrary relation between representing vehicles and representational content, these approaches must appeal to the role played by a representation, i.e., how it is used by the system in which it is embedded, to ground its content. This is in supposed contrast to structural representations, in which (...) the relation of resemblance results in a non-arbitrary relationship between vehicles and content. Structuralists argue that, as a result, approaches that posit structural representations can, and alternative approaches cannot, explain how representational content can be causally relevant in the production of behavior. In this paper, I will argue that structural representations are susceptible to the very same critiques that proponents level against what they sometimes refer to as “use” theories. This, I contend, is not surprising given that a theory of structural representations is, in fact, just as much a use-theory as alternative approaches. (shrink)