Mitchell et al. argue that conditioning phenomena may be better explained by high-level, rational processes, rather than by non-cognitive associative mechanisms. This commentary argues that this viewpoint is compatible with neuroscientific data, may extend to nonhuman animals, and casts computational models of reinforcement learning in a new light.

The systems studied in the special sciences are often said to be causally autonomous, in the sense that their higher-level properties have causal powers that are independent of the causal powers of their more basic physical properties. This view was espoused by the British emergentists, who claimed that systems achieving a certain level of organizational complexity have distinctive causal powers that emerge from their constituent elements but do not derive from them. More recently, non-reductive physicalists have espoused a similar view (...) about the causal autonomy of special-science properties. They argue that since these properties can typically have multiple physical realizations, they are not identical to physical properties, and further they possess causal powers that differ from those of their physical realisers. Despite the orthodoxy of this view, it is hard to find a clear exposition of its meaning or a defence of it in terms of a well-motivated account of causation. In this paper, we aim to address this gap in the literature by clarifying what is implied by the doctrine of the causal autonomy of special-science properties and by defending the doctrine using a prominent theory of causation from the philosophy of science. (shrink)

Safety purports to explain why cases of accidentally true belief are not knowledge, addressing Gettier cases and cases of belief based on statistical evidence. However, problems arise for using safety as a condition on knowledge: safety is not necessary for knowledge and cannot always explain the Gettier cases and cases of statistical evidence it is meant to address. In this paper, I argue for a new modal condition designed to capture the non-accidental relationship between facts and evidence required for knowledge: (...) causal safety. I argue that possible errors in belief can be captured by accounting for deviations in causal relationships and that there is a natural way to characterize which causal errors are relevant in an epistemic situation. Using this, I develop a causal analogue to safety, where one’s belief in _p_ is causally safe if it is true in all causally relevant worlds where one believes _p_. Causal safety, I argue, can better explain the cases safety is meant to address and can avoid the arguments raised against the necessity of safety. (shrink)

La tesis aborda uno de los temas centrales en la filosofía de la mente y las ciencias cognitivas: los conceptos como unidades básicas de la cognición humana. La tesis central que se defiende es que el contenido de los conceptos es determinado parcialmente por las comunidades a las que pertenecen los sujetos cognitivos, en la medida en la que dichas comunidades guían y constriñen las interacciones entre el sistema cognitivo conceptual y el entorno del que forma conceptos. La novedad de (...) la tesis es doble. Por una parte, cuestiona las teorías dominantes sobre el contenido de los conceptos —las teorías descriptivistas y las teorías naturalistas—, no solamente a partir de desafíos filosóficos que muestran sus limitaciones, sino también a partir de evidencia empírica sobre el efecto de las interacciones sociales en el contenido de los conceptos y, por tanto, en la cognición. Por otra parte, proporciona evidencia empírica sobre infantes ferales (i.e. niños salvajes), cuyo sistema cognitivo conceptual no ha recibido la guía de ninguna comunidad humana en sus interacciones con el entorno. Dicha evidencia permite sugerir que una explicación puramente naturalizada del contenido, sin la presencia de las comunidades, no puede explicar el desarrollo cognitivo conceptual de dichos infantes. Así, la tesis logra proponer un papel no trivial de las comunidades en el debate sobre la determinación del contenido semántico de los conceptos. (shrink)

Theory of illness causation is an important issue in all biomedical sciences, and solid etiological explanations are needed in order to develop therapeutic approaches in medicine and preventive interventions in public health. Until now, the literature about the theoretical underpinnings of illness causation research has been scarce and fragmented, and lacking a convenient summary. This interdisciplinary book provides a convenient and accessible distillation of the current status of research into this developing field, and adds a personal flavor to the discussion (...) by proposing the etiological stance as a comprehensive approach to identify modifiable causes of illness. (shrink)

Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates familiar principles of counterfactual (...) logic. This combines the interventionist intuitions of the causal approach with the logical advantages of the similarity approach. (shrink)

This paper presents corpus and experimental data that problematize the traditional analysis of must as a strong necessity modal, as recently revived and defended by von Fintel and Gillies :351–383, 2010). I provide naturalistic examples showing that must p can be used alongside an explicit denial of knowledge of p or certainty in p, and that it can be conjoined with an expression indicating that p is not certain or that not-p is possible. I also report the results of an (...) experiment involving lotteries, where most participants endorsed a sentence of the form must not-p despite being instructed that p is a possibility. Crucially, endorsement was much higher for must in this context than for matched sentences with knowledge or certainty expressions. These results indicate that the requirements for felicitous use of must are weaker than for know and certain rather than being at least as strong, as the epistemic necessity theory would predict. However, it is possible to account for these data while retaining the key insights of von Fintel and Gillies’ analysis of the evidential component of must. I discuss several existing accounts that could be construed in this way and explain why none is completely satisfactory. I then propose a new model that embeds an existing scalar theory into a probabilistic model of informational dynamics structured around questions and answers. (shrink)

Causal Bayes nets have been developed in philosophy, statistics, and computer sciences to provide a formalism to represent causal structures, to induce causal structure from data and to derive predictions. Causal Bayes nets have been used as psychological theories in at least two ways. They were used as rational, computational models of causal reasoning and they were used as formal models of mental causal models. A crucial assumption made by them is the Markov condition, which informally states that variables are (...) independent of other variables that are not their direct or indirect effects conditional on their immediate causes. Whether people’s inferences conform to the causal Markov and the faithfulness condition has recently been investigated empirically. A review of respective research indicates that inferences frequently violate these conditions. This finding challenges some uses of causal Bayes nets in psychology. They entail that causal Bayes nets may not be appropriate to derive predictions for causal model theories of causal reasoning. They also question whether causal Bayes nets as a rational model are empirically descriptive. They do not challenge, however, causal Bayes nets as normative models and their usage as formal models of causal reasoning. (shrink)

Tim Crane's ‘Cosmic Hermeneutics vs. Emergence: The Challenge of the Explanatory Gap’ claims that non‐reductive physicalism must either close the explanatory gap, addressing the challenge famously posed by Levine's argument, or become identical to emergentism. Since no way to close the gap is available, the result is that there can be no interesting philosophical position intermediate between physicalism and emergentism. This chapter argues that if we look at the relation between physicalism and emergentism from the vantage point of reduction, Crane's (...) analysis is rather persuasive. However, if we switch from reduction to causality, its conclusions appear more disputable. In particular, if we shift from ‘novelty and explanation’ to ‘novelty and causality’ as key features of emergent properties, we may introduce a distinction between two kinds of emergentism (moderate and radical) based on their attitude towards the causal inheritance principle. If the causal inheritance principle is accepted (some versions of) moderate emergentism may be considered as non‐trivial forms of non‐reductive physicalism. (shrink)

Children and adults commonly produce more generic noun phrases (e.g., birds fly) about animals than artifacts. This may reflect differences in participants’ generic knowledge about specific animals/artifacts (e.g., dogs/chairs), or it may reflect a more general distinction. To test this, the current experiments asked adults and preschoolers to generate properties about novel animals and artifacts (Experiment 1: real animals/artifacts; Experiments 2 and 3: matched pairs of maximally similar, novel animals/artifacts). Data demonstrate that even without prior knowledge about these items, the (...) likelihood of producing a generic is significantly greater for animals than artifacts. These results leave open the question of whether this pattern is the product of experience and learned associations or instead a set of early-developing theories about animals and artifacts. (shrink)

In this review article of Dan Hutto's bok Folk Psychological Narratives: The Sociocultural Basis of Understanding Reasons, I argue that we can take a functional approach to FP that identifies it with the practice of explaining behaviour -- that is, we can understand folk psychology as having the purpose of explaining behaviour and promoting social cohesion by making others’ behaviour comprehensible, without thinking that this ability must be limited to those with linguistic abilities. One reason for thinking that language must (...) be implicated in FP explanations arises from the history of theorizing about the nature of scientific explanation. I will show that there are other models of explanation that are free from the metaphysical linguistic baggage of the traditional models, and argue that such models can be profitably used to make sense of an explanation-centred FP that need not involve the attribution of propositional attitudes or a functioning linguistic competence. Further, I will argue that there is evidence that pre-linguistic human children engage in explanatory practices, and that some of these explanations may be seen as narrative explanations in an important sense. (shrink)

Both philosophers and psychologists have argued for the existence of distinct kinds of explanations, including teleological explanations that cite functions or goals, and mechanistic explanations that cite causal mechanisms. Theories of causation, in contrast, have generally been unitary, with dominant theories focusing either on counterfactual dependence or on physical connections. This paper argues that both approaches to causation are psychologically real, with different modes of explanation promoting judgments more or less consistent with each approach. Two sets of experiments isolate the (...) contributions of counterfactual dependence and physical connections in causal ascriptions involving events with people, artifacts, or biological traits, and manipulate whether the events are construed teleologically or mechanistically. The findings suggest that when events are construed teleologically, causal ascriptions are sensitive to counterfactual dependence and relatively insensitive to the presence of physical connections, but when events are construed mechanistically, causal ascriptions are sensitive to both counterfactual dependence and physical connections. The conclusion introduces an account of causation, an "exportable dependence theory," that provides a way to understand the contributions of physical connections and teleology in terms of the functions of causal ascriptions. (shrink)

Methods and goals in philosophy are discussed by first describing an ideal, and then looking at how the ideal might be approached. David Lewis’s work in metaphysics is critically examined and compared to analogous work by Mackie and Carnap. Some large-scale philosophical systematic work, especially in metaphysics, is best treated as model-building, in a sense of that term that draws on the philosophy of science. Models are constructed in a way that involves deliberate simplification, or other imaginative modification of reality, (...) in order to make relationships visible or problems tractable. (shrink)

The metaphor of a branching tree of future possibilities has a number of important philosophical and logical uses. In this paper we trace this metaphor through some of its uses and argue that the metaphor works the same way in physics as in philosophy. We then give an overview of formal systems for branching possibilities, viz., branching time and (briefly) branching space-times. In a next step we describe a number of different notions of possibility, thereby sketching a landscape of possibilities. (...) In the final section of the paper we look at the place of branching-based possibilities in that larger landscape of possibilities. Our main message is that far from being an outlandish metaphysical extravagancy, branching-based possibilities are epistemically as well as metaphysically basic. (shrink)

The term “historical epistemology” can be read in two different ways: (1) as referring to a program of ‘historicizing’ epistemology, in the sense of a critique of traditional epistemology’s tendency to gloss over historical context, or (2) as a manifesto of ‘epistemologizing’ history, i.e. as a critique of radical historicist and relativist approaches. In this paper I will defend a position in this second sense. I show that one can account for the historical development and diversity of science without disavowing (...) the relevance of a (normatively understood) epistemology and without denying the existence of human cognitive universals across historical and cultural differences. In support of my thesis, I draw on cognitive scientific research on causal and symbolic cognition, arguing that causal understanding constitutes a basic part of science, which, in the course of its development, becomes more and more superimposed by a culturally and historically variable symbolic superstructure. (shrink)

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which (...) participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account. (shrink)

Debates concerning the character, scope, and warrant of abductive inference have been active since Peirce first proposed that there was a third form of inference, distinct from induction and deduction. Abductive reasoning has been dubbed weak, incoherent, and even nonexistent. Part, at least, of the problem of articulating a clear sense of abductive inference is due to difficulty in interpreting Peirce. Part of the fault must lie with his critics, however. While this article will argue that Peirce indeed left a (...) number of puzzles for interpreters, it will also contend that interpreters should be careful to distinguish discussion of the formal and strictly epistemic question of whether and how abduction is a sound form of inference from discussions of the practical goals of abduction, as Peirce understood them. This article will trace a history of critics and defenders of Peirce’s notion of abduction and discuss how Peirce both fueled the confusion and in fact anticipated and responded to several recurring objections. (shrink)

We report the results of an experiment in which human subjects were trained to perform a perceptual matching task. Subjects were asked to manipulate comparison objects until they matched target objects using the fewest manipulations possible. An unusual feature of the experimental task is that efficient performance requires an understanding of the hidden or latent causal structure governing the relationships between actions and perceptual outcomes. We use two benchmarks to evaluate the quality of subjects’ learning. One benchmark is based on (...) optimal performance as calculated by a dynamic programming procedure. The other is based on an adaptive computational agent that uses a reinforcement-learning method known as Q-learning to learn to perform the task. Our analyses suggest that subjects were successful learners. In particular, they learned to perform the perceptual matching task in a near-optimal manner (i.e., using a small number of manipulations) at the end of training. Subjects were able to achieve near-optimal performance because they learned, at least partially, the causal structure underlying the task. In addition, subjects’ performances were broadly consistent with those of model-based reinforcement-learning agents that built and used internal models of how their actions influenced the external environment. We hypothesize that people will achieve near-optimal performances on tasks requiring sequences of action—especially sensorimotor tasks with underlying latent causal structures—when they can detect the effects of their actions on the environment, and when they can represent and reason about these effects using an internal mental model. (shrink)

The focus of this research is to explore the developmental trajectory of the propensity to see meaning in unexpected or chance events, and more specifically, to explore the origin and development of nonmaterial or supernatural explanations. Sixty-seven children aged 8, 10 and 12, along with 22 adults, were presented with scenarios describing unusual or unexpected events. They were first asked to provide explanations for why they thought the events occurred and then asked to rate different supernatural explanations as they pertained (...) to each scenario. Results indicated that adults spontaneously appealed to supernatural explanations more frequently than did children, and that this tendency to appeal to supernatural concepts increased with age. Participants of all ages frequently endorsed multiple explanations for the same events and were more likely to endorse supernatural explanations for positively valenced than for negatively valenced stories. Religiosity affected both spontaneous explanations and ratings. Findings are discussed in terms of how children acquire the explanatory systems of their culture. (shrink)

Rational constructivism is one of the leading theories in developmental psychology. But it is not a purely psychological theory: rational constructivism also makes a number of substantial epistemological claims about both the nature of human rationality and several normative principles that fall squarely into the ambit of epistemology. The aim of this paper is to clarify and defend both theses and several other epistemological claims, as they represent the essential epistemological dimensions of rational constructivism.

Children learn from people and about people simultaneously; that is, children consider evidentiary qualities of human actions which cross traditional domain boundaries. We propose that Knobe's moral asymmetries are a natural consequence of this learning process: the way gather evidence for causation, intention, and morality through early social experiences.

Recent work in rational probabilistic modeling suggests that a kind of propositional reasoning is ubiquitous in cognition and especially in cognitive development. However, there is no reason to believe that this type of computation is necessarily conscious or resource-intensive.

Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This article addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates familiar principles of counterfactual (...) logic. This combines the interventionist intuitions of the causal approach with the logical advantages of the similarity approach. (shrink)

This paper has two main parts. In the first part, we motivate a kind of indeterminate, suppositional credences by discussing the prospect for a subjective interpretation of a causal Bayesian network, an important tool for causal reasoning in artificial intelligence. A CBN consists of a causal graph and a collection of interventional probabilities. The subjective interpretation in question would take the causal graph in a CBN to represent the causal structure that is believed by an agent, and interventional probabilities in (...) a CBN to represent suppositional credences. We review a difficulty noted in the literature with such an interpretation, and suggest that a natural way to address the challenge is to go for a generalization of CBN that allows indeterminate credences. In the second part, we develop a decision-theoretic foundation for such indeterminate suppositional credences, by generalizing a theory of coherent choice functions to accommodate some form of act-state dependence. The upshot is a decision-theoretic framework that is not only rich enough to, so to speak, ground the probabilities in a subjectively interpreted causal network, but also interesting in its own right, in that it accommodates both act-state dependence and imprecise probabilities. (shrink)

My research program proposes that children have representations and learning mechanisms that can be characterized as causal models of the world Bayesian Fundamentalism.”.

Bullot & Reber (B&R) put forth a design stance to fuse psychological and art historical accounts of visual thinking into a single theory. We argue that this aspect of their proposal needs further fine-tuning. Issues of transgression and coherence are necessary to provide stability to the design stance. We advocate looking to Art Education for such fundamentals of picture understanding.

How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses spike-time (...) dependent plasticity, long-term depression, and heterosynaptic competition rules to implement Rescorla–Wagner-like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well-known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening-off. These models can then be run as emulators for state inference. Furthermore, this mechanism is capable of copying synaptic connectivity patterns between neuronal networks by observing the spontaneous spike activity from the neuronal circuit that is to be copied, and it thereby provides a powerful method for transmission of circuit functionality between brain regions. (shrink)

Russo and Williamson claim that establishing causal claims requires mechanistic and difference-making evidence. In this article, I will argue that Russo and Williamson's formulation of their thesis is multiply ambiguous. I will make three distinctions: mechanistic evidence as type vs object of evidence; what mechanism or mechanisms we want evidence of; and how much evidence of a mechanism we require. I will feed these more precise meanings back into the Russo–Williamson thesis and argue that it is both true and false: (...) two weaker versions of the thesis are worth supporting, while the stronger versions are not. Further, my distinctions are of wider concern because they allow us to make more precise claims about what kinds of evidence are required in particular cases. (shrink)

In this paper, I examine the comparatively neglected intuition of production regarding causality. I begin by examining the weaknesses of current production accounts of causality. I then distinguish between giving a good production account of causality and a good account of production. I argue that an account of production is needed to make sense of vital practices in causal inference. Finally, I offer an information transmission account of production based on John Collier’s work that solves the primary weaknesses of current (...) production accounts: applicability and absences. (shrink)

Young children spend a large portion of their time pretending about non-real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative representation of reality, and keeping this representation separate from reality. In turn, according to causal models accounts, counterfactual reasoning is a crucial tool that children need to plan for the future and learn (...) about the world. Both planning with causal models and learning about them require the ability to create false premises and generate conclusions from these premises. We argue that pretending allows children to practice these important cognitive skills. We also consider the prevalence of unrealistic scenarios in children's play and explain how they can be useful in learning, despite appearances to the contrary. (shrink)

Many of our decisions pertain to causal systems. Nevertheless, only recently has it been claimed that people use causal models when making judgments, decisions and predictions, and that causal Bayes nets allow us to formally describe these inferences. Experimental research has been limited to simple, artificial problems, which are unrepresentative of the complex dynamic systems we successfully deal with in everyday life. For instance, in social interactions, we can explain the actions of other's on the fly and we can generalize (...) from limited observations to predict future actions and their consequences. Our main argument is that none of these inferences (i.e., induction, generalization, explanation, and prediction) can be achieved without causal reasoning. As a case in point we use the popular television series desperate housewives and show how causal Bayes nets are able to explain the inferences made in social contexts. Crucially, causal Bayes nets also allow us to understand why we can infer so much from so little when making sense of a protagonist's behavior. (shrink)

My dissertation establishes the basis for a systematic outlook on the role language plays in human cognition. It is an investigation based on a cognitive conception of language, as opposed to communicative conceptions, viz. those that suppose that language plays no role in cognition. I focus, in Chapter 2, on three paradigmatic theories adopting this perspective, each offering different views on how language contributes to or changes cognition. -/- In Chapter 3, I criticize current views held by dual-process theorists, and (...) I develop a picture of the complex interaction between language and cognition that I deem more plausible by using resources from the literature on the evolution of the faculty of language. Rather than trying to find one general explanation for all cognitive processes, I take seriously the idea that our mind is composed of many subsystems, and that language can interact and modify each in different ways. There is no reason offered in the empirical literature—besides maybe parsimony—that suggest that language has to interact in the same ways with all cognitive processes. Yet, this is seemingly taken for granted, especially within dual-process approaches. -/- On my view, it is a central requirement for a theory of the role of language in cognition to explain how language might have effects, at once, on and within various parts of cognition. In Chapter 4, I explore how this framework can modify how we think about some experiments in psychology, specifically in research on categorization. My idea is that language, once it evolved, changed how some cognitive capacities worked and interacted with each other, but did so in more than one or two ways. Cognitive systems are changed in very different ways—sometimes the transformation is very subtle, such as our way of forming categories by using how similar objects are, while other times it is deep and changes the very way the system works. (shrink)

n this paper, I examine the charge that Gopnik and Meltzoff’s ‘Child as Scientist’ program, outlined and defended in their 1997 book Words, Thoughts and Theories is vitiated by a form of ‘cognitive individualism’ about science. Although this charge has often been leveled at Gopnik and Meltzoff’s work, it has rarely been developed in any detail. -/- I suggest that we should distinguish between two forms of cognitive individualism which I refer to as ‘ontic’ and ‘epistemic’ cognitive individualism (OCI and (...) ECI respectively). I then argue - contra Ronald Giere – that Gopnik and Meltzoff’s commitment to OCI is relatively unproblematic, since it is an easily detachable part of their view. By contrast, and despite their explicit discussion of the issue, their commitment to ECI is much more problematic. (shrink)

Making decisions can be hard, but it can also be facilitated. Simple heuristics are fast and frugal but nevertheless fairly accurate decision rules that people can use to compensate for their limitations in computational capacity, time, and knowledge when they make decisions [Gigerenzer, G., Todd, P. M., & the ABC Research Group (1999). Simple Heuristics That Make Us Smart. New York: Oxford University Press.]. These heuristics are effective to the extent that they can exploit the structure of information in the (...) environment in which they operate. Specifically, they require knowledge about the predictive value of probabilistic cues. However, it is often difficult to keep track of all the available cues in the environment and how they relate to any relevant criterion. This problem becomes even more critical if compound cues are considered. We submit that knowledge about the causal structure of the environment helps decision makers focus on a manageable subset of cues, thus effectively reducing the potential computational complexity inherent in even relatively simple decision-making tasks. We review experimental evidence that tested this hypothesis and report the results of a simulation study. We conclude that causal knowledge can act as a meta-cue for identifying highly valid cues, either individual or compound, and helps in the estimation of their validities. (shrink)

We are in vehement agreement with most of Vaesen's key claims. But Vaesen fails to consider or rebut the possibility that there are deep causal dependencies among the various cognitive traits he identifies as uniquely human. We argue that is one such linchpin trait in the evolution of human tool use, social intelligence, language, and culture.

In our critical review of Doing without Concepts, we argue that although the heterogeneity hypothesis (according to which exemplars, prototypes and theories are natural kinds that should replace ‘concept’) may end fruitless debates in the psychology of concepts, Edouard Machery did not anticipate one consequence of his suggestion: Definitions now acquire a new status as another one of the bodies of information replacing ‘concept’. In order to support our hypothesis, we invoke dual-process models to suggest that prototypes, exemplars and theories (...) are ‘Type 1’ concepts (automatic, implicit) and definitions ‘Type 2’ concepts (controlled, explicit). In the context of this argument, we suggest that one must better distinguish between Type 1 theories (e.g., Bayesian causal nets) and Type 2 theories (e.g., scientific) that explicitly control definitions. (shrink)

Part I of this paper introduced a range of mechanistic theories of causality, including process theories and the complex-systems theories, and some of the problems they face. Part II argues that while there is a decisive case against a purely mechanistic analysis, a viable theory of causality must incorporate mechanisms as an ingredient, and describes one way of providing an analysis of causality which reaps the rewards of the mechanistic approach without succumbing to its pitfalls.

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)

To introduce the issue of the tracking and identification of human agents, I examine the ability of an agent to track a human person and distinguish this target from other individuals: The ability to perform person identification. First, I discuss influential mechanistic models of the perceptual recognition of human faces and people. Such models propose detailed hypotheses about the parts and activities of the mental mechanisms that control the perceptual recognition of persons. However, models based on perceptual recognition are incomplete (...) theories of person identification because they do not explain several identification behaviors that are fundamental to human social interactions. Furthermore, recognition-based models tend to appeal to the controversial concept of the “identity” of a person without explaining what determines personal identity and persistence. To overcome these limitations, I propose to integrate the face-recognition program into a broader causal-historical theory of identification. The causal-historical theory of identification complements models focused on perceptual recognition because it can account for the types of non-perceptual identification overlooked by the face-recognition program. Moreover, it can decompose the identification behaviors into tracking processes that succeed or fail to be sensitive to causal characteristics of a target. I illustrate these advantages with a discussion of the difference between the tracking of a person understood as either a causally continuous biological organism or a psychologically continuous mind. Finally, I argue that the causal-historical theory provides a theoretical framework for investigating the tracking of relations between a target and its contextual and historical attributes, such as a target's possessions. (shrink)

Recently, there has been a large amount of support for the idea that causal claims can be sensitive to normative considerations. Previous work has focused on the concept of actual causation, defending the claim that whether or not some token event c is a cause of another token event e is influenced by both statistical and prescriptive norms. I focus on the policy debate surrounding alternative energies, and use the causal modelling framework to show that in this context, people’s normative (...) commitments don’t just affect the causal claims they are willing to endorse, but also their understanding of the causal structure. In the context of the alternative energy debate, normative considerations affect our understanding of the causal structure by influencing our judgements about which variables should be held fixed, and therefore which variables should be relegated to the background of a causal model. In cases of extreme disagreement, normative commitments can also affect which causal structure we think should be instantiated. Thus, focusing on a new context has revealed a previously unexplored sense in which normative factors are incorporated into causal reasoning. (shrink)

Externalist theories in natural language semantics have become the orthodoxy since Kripke is widely thought to have refuted descriptive theories involving internal cognitive representation of meaning. This shift may be seen in developments in philosophy of language of the 1970s – the direct reference “revolution against Frege”. I consider Fodor’s heretical thought that something has gone “awfully wrong” in this philosophical consensus, perhaps confirming Chomsky’s view that the whole field of philosophical semantics is “utterly wrongheaded” and “crazy” by virtue of (...) its non-naturalist assumptions and “methodological dualism.” Kripke acknowledges that he was led to his views by his “natural intuition”. Accordingly, I take up the aetiological question Chomsky poses, suggesting that we may go beyond the usual analysis of those doctrines that have been assumed too uncritically “to ask why they seem so compelling”. I suggest that the externalist orthodoxy is a kind of cognitive illusion seen elsewhere in philosophy and cognitive science. (shrink)