Causal representation in shamanic consciousness is compared with causal representation in ordinary waking consciousness. Causal representation in shamanic experience and in ordinary waking experience can engage strategies involving attribution of intentionality , heuristics , and magical thinking . Such strategies have consequences involving social biases , locus of control, authorship of actions, and supernaturalizing of social life. Similarities of causal representation in shamanic experience and in ordinary waking experience have implications for theories (...) of mind and theories of causal representation, and these implications involve use of metaphor and analogy, modular processing of social information, use of behavioural criteria for mental states, differences between physical causality and social causality, property transmission in causal representation, and whether causal representation involves general or state-specific processes. Principles of causal representation in shamanic consciousness appear consistent with principles of causal representation in ordinary waking consciousness. (shrink)

Recent debates over the nature of causation, casual inference, and the uses of causal models in counterfactual analysis, involving inter alia Nancy Cartwright (Hunting Causes and Using Them), James Woodward (Making Things Happen), and Judea Pearl (Causation), hinge on how causality is represented in models. Economists’ indigenous approach to causal representation goes back to the work of Herbert Simon with the Cowles Commission in the early 1950s. The paper explicates a scheme for the representation of (...) class='Hi'>causal structure, inspired by Simon, and shows how this representation sheds light on some important debates in the philosophy of causation. This structural account is compared to Woodward’s manipulability account. It is used to evaluate the recent debates – particularly, with respect to the nature of causal structure, the identity of causes, causal independence, and modularity. Special attention is given to modeling issues that arise in empirical economics. (shrink)

There are simple mechanical systems that elude causal representation. We describe one that cannot be represented in a single directed acyclic graph. Our case suggests limitations on the use of causal graphs for causal inference and makes salient the point that causal relations among variables depend upon details of causal setups, including values of variables.

In experimental settings, scientists often “make” new things, in which case the aim is to intervene in order to produce experimental objects and processes—characterized as ‘effects’. In this discussion, I illuminate an important performative function in measurement and experimentation in general: intervention-based experimental production (IEP). I argue that even though the goal of IEP is the production of new effects, it can be informative for causal details in scientific representations. Specifically, IEP can be informative about causal relations in: (...) regularities under study; ‘intervention systems’, which are measurement/experimental systems; and new technological systems. (shrink)

In experimental settings, scientists often “make” new things, in which case the aim is to intervene in order to produce experimental objects and processes—characterized as ‘effects’. In this discussion, I illuminate an important performative function in measurement and experimentation in general: intervention-based experimental production. I argue that even though the goal of IEP is the production of new effects, it can be informative for causal details in scientific representations. Specifically, IEP can be informative about causal relations in: regularities (...) under study; ‘intervention systems’, which are measurement/experimental systems; and new technological systems. (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)

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)

Erratum to: Synthese 191:1925–1930 DOI:10.1007/s11229-013-0380-3 The authors were unaware that points in their article appeared in “Caveats for Causal Reasoning with Equilibrium Models,” by Denver Dash and Marek Druzdzel, published in S. Benferhat and P. Besnard : European Conferences on Symbolic and Quantitative Approaches to Reasoning with Uncertainty 2001, Lecture Notes in Artificial Intelligence 2143, pp. 192–203. The authors were unaware of this essay and would like to apologize to the authors for failing to cite their excellent work.

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)

This paper centers around the notion that internal, mental representations are grounded in structural similarity, i.e., that they are so-called S-representations. We show how S-representations may be causally relevant and argue that they are distinct from mere detectors. First, using the neomechanist theory of explanation and the interventionist account of causal relevance, we provide a precise interpretation of the claim that in S-representations, structural similarity serves as a “fuel of success”, i.e., a relation that is exploitable for the (...) class='Hi'>representation using system. Then, we discuss crucial differences between S-representations and indicators or detectors, showing that—contrary to claims made in the literature—there is an important theoretical distinction to be drawn between the two. (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)

Especially in the appendix to the opening part of his Ethics, Spinoza discusses teleology in a manner that has earned him the status of a staunch critic of final causes. Much of the recent lively discussion concerning this complex and difficult issue has revolved around the writings of Jonathan Bennett who maintains that Spinoza does, in fact, reject all teleology. Especially important has been the argument claiming that because of his basic ontology, Spinoza cannot but reject thoughtful teleology, that is, (...) teleology involved in the actions of conscious cognitive beings who have thoughts of future states of affairs. For Spinoza, a particular idea is a modification of the thinking substance the object of which is a certain modification of the extended substance, and Bennett’s central argumentative move is to claim that there is no room in Spinoza’s system for a key ingredient in thoughtful teleology, the tenet that representative content of ideas is causally efficacious. In what follows, I begin by presenting Bennett’s argument. As his position has received much criticism, I then take up the ways in which it has been discussed and found wanting. I think that Bennett’s position really is something that should not be endorsed; however, and despite the lively discussion, it also seems to me that there is more to be said about what is at stake here. Thus, I aim at offering an analysis of the nature of Bennett’s argument and the ensuing discussion with the aim of discerning the philosophical source from which Bennett’s interpretation draws its force. (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)

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)

I defend a claim, central to much work in psycholinguistics, that constructing mental representations of syntactic structures is a necessary step in language comprehension. Call such representations “mental phrase markers”. Several theorists in psycholinguistics, AI, and philosophy have cast doubt on the usefulness of positing MPMs. I examine their proposals and argue that they face major empirical and conceptual difficulties. My conclusions tell against the broader skepticism that persists in philosophy—e.g., in the embodied cognition literature —about the usefulness of positing (...) mental representations in psychological models. Using my discussion of sentence parsing as a case study, I propose several conditions on an appropriate appeal to mental representations. Finally, I point out that the familiar arguments from productivity, systematicity, and inferential coherence suffice to establish that MPMs themselves have a c.. (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)

Displacements in the remembered location of stimuli in displays based on Michotte’s (1946/1963) launching effect and entraining effect were examined. A moving object contacted an initially stationary target, and the target began moving. After contacting the target, the mover became stationary (launching trials) or continued moving in the same direction and remained adjacent to the target (entraining trials). In launching trials, forward displacement was smaller for targets than for movers; in entraining trials, forward displacement was smaller for movers than for (...) targets. Also, forward displacement was smaller for targets in launching trials than for targets in entraining trials. Data are consistent with a hypothesis that the launching effect involves an attribution that the mover imparted to the target a dissipating impetus that was responsible for target motion. Introspective experience of a perception of physical causality in the launching effect might result because behavior of movers and targets is consistent with that predicted by an impetus heuristic. (shrink)

Three experiments investigated reasoners' beliefs about causal powers; that is, their beliefs about the capacity of a putative cause to produce a given effect. Covariation-based theories (e.g., Cheng, 1997; Kelley, 1973; Novick & Cheng, 2004) posit that beliefs in causal power are represented in terms of the degree of covariation between the cause and its effect; covariation is defined in terms of the degree to which the effect occurs in the presence of the cause, and fails tooccur in (...) the absence of the cause. To test the degree to which beliefs incausal power are reflected in beliefs about covariation information, participants in three experiments rated their beliefs that putative causes have the capacity to produce a given effect (i.e., possess the causal power to produce an effect) as well as their beliefs regarding the degree to which the putative cause and effectcovary. A strong positive correlation was discovered between participants' beliefs in causal power and their beliefs that the effect occurs in the presence of the cause. However, no direct relationship was found between participants' beliefs in causal power and their belief that the effect will fail tooccur in the absence of the cause. These findings were replicated using bothwithin- (Experiments 1 and 3) and between-subject designs (Experiment 2). In Experiment 3, we extended these analyses to measures of familiarity, imageability, and detailedness of the representation. We found that participants' beliefs in causal power were strongly associated with familiarity, and imageability, but not the perceived detailedness of the cause and effect relationship. These data provide support for a multidimensional account of causal knowledge whereby people's representations of causation include, but are not limited to, the covariation, familiarity, and imageability of cause and effect relationships. (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)

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

This chapter explains the screening‐off rule in the psychological laboratory. The Markov assumption states that any variable in a set is independent in probability of all its ancestors in the set conditional on its own parents. The screening‐off rule is also critical to allow Bayes nets to make an inference of the state of an unknown variable in a causal structure from the states of other variables in that structure. The chapter examines which causal representations people use to (...) make predictions and whether people conform to the screening‐off rule with respect to a causal model they have in mind. It analyzes what leads them to modify their causal model and whether or not people are causally oriented when making predictions. Making probability judgments is hard and requires careful deliberation. People are capable of such deliberation although they avoid it until they cannot, until the facts require more careful thought. (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)

Causal theories of mental content attempt to explain how thoughts can be about things. They attempt to explain how one can think about, for example, dogs. These theories begin with the idea that there are mental representations and that thoughts are meaningful in virtue of a causal connection between a mental representation and some part of the world that is represented. In other words, the point of departure for these theories is that thoughts of dogs are about (...) dogs because dogs cause the mental representations of dogs. (shrink)

Commentators generally agree that Foucher presumes the resemblance theory of representation and uses it to substantiate external world skepticism. In this paper, I challenge this picture. First, I argue that he does not assume that representation is reducible to, or even just works through, resemblance between representation and object. Indeed, his functional-similarity theory primarily appeals to resemblance between the respective effects the representation and the object (would) have on our minds. I also propose that his argument (...) for the resemblance-requirement of representation depends on the causal likeness principle, and clarify its role in Foucher’s theory. Second, I show that his main interest lies with representation in the sense of truthfully making the object present. Accordingly, when Foucher concludes that we can only represent our own ideas, he merely means that our ideas reveal the ways objects affect us through our senses, as opposed to how they are in themselves. (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)

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)

This paper engages critically with anti-representationalist arguments pressed by prominent enactivists and their allies. The arguments in question are meant to show that the “as-such” and “job-description” problems constitute insurmountable challenges to causal-informational theories of mental content. In response to these challenges, a positive account of what makes a physical or computational structure a mental representation is proposed; the positive account is inspired partly by Dretske’s views about content and partly by the role of mental representations in contemporary (...) cognitive scientific modeling. (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)

'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)

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)

The goal of this paper is two-fold: first, to emphasize causality in disease ontology and knowledge representation, presenting a general and cursory discussion of causality and causal chains; and second, to clarify and develop the River Flow Model of Diseases (RFM). The RFM is an ontological account of disease, representing the causal structure of pathology. It applies general knowledge of causality using the concept of causal chains. The river analogy of disease is explained, formal descriptions are (...) offered, and the RFM disease definition is refined by describing causal chains in terms of causal relations. The definition is updated to coincide with the actual RFM classes found in its upper-level ontology, YAMATO, which brings to light some challenges in developing both YAMATO and the RFM. The RFM is also discussed in relation to another ontological account of disease. Strategies are offered toward interoperability between these theories. (shrink)

Causal models have proven extremely useful in offering formal representations of causal relationships between a set of variables. Yet in many situations, there are non-causal relationships among variables. For example, we may want variables LDL, HDL, and TOT that represent the level of low-density lipoprotein cholesterol, the level of lipoprotein high-density lipoprotein cholesterol, and total cholesterol level, with the relation LDL+HDL=TOT. This cannot be done in standard causal models, because we can intervene simultaneously on all three (...) variables. The goal of this paper is to extend standard causal models to allow for constraints on settings of variables. Although the extension is relatively straightforward, to make it useful we have to define a new intervention operation that disconnects a variable from a causal equation. We give examples showing the usefulness of this extension, and provide a sound and complete axiomatization for causal models with constraints. (shrink)