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A common presupposition in the concepts literature is that concepts constitute a singular natural kind. If, on the contrary, concepts split into more than one kind, this literature needs to be recast in terms of other kinds of mental representation. We offer two new arguments that concepts, in fact, divide into different kinds: (a) concepts split because different kinds of mental representation, processed independently, must be posited to explain different sets of relevant phenomena; (b) concepts split because different kinds of mental representation, processed independently, must be posited to explain responses to different kinds of category. Whether these arguments are sound remains an open empirical question, to be resolved by future empirical and theoretical work.

Philosophers have long been concerned with intuitions about consciousness, but this interest usually takes a peculiar form. The fundamental goal is typically not to understand the intuitions themselves, with all the psychological intricacies. Instead, what philosophers really want to understand is the true nature of consciousness, and they turn to intuitions as a way of getting indirect evidence about this other topic. This emphasis strikes us as unfortunate. Intuitions about consciousness are fascinating phenomena, amply worthy of study in their own right. The fact that people have the intuitions they do can teach us something valuable about the way people ascribe mental states, the way they think about non-human animals, perhaps even the way they make moral judgments. Our aim here, then, is to conduct a straightforward investigation into people’s intuitions about consciousness. In pursuing this line of inquiry, we truly have no ulterior motives. It is not as though we are trying to present a theory about the true nature of consciousness and have simply chosen to argue for it in a roundabout way. Rather, we are genuinely intrigued by the intuitions themselves, and we want to get a better understanding of the psychological mechanisms that generate them. Our paper therefore draws on a number of different lines of existing research, including research in ‘theory of mind ’ (e.g., Gopnik & Meltzoff; Scholl & Leslie 1999), research in consciousness studies (e.g., Block 1978; 1995), and research about how people determine which sorts of entities are capable of having mental states (Inagaki & Hatano 1991; Johnson 2000). Because our aims are somewhat unusual, we will be making use of a somewhat unusual method. First we introduce hypotheses about the psychological mechanisms underlying people’s intuitions; then we put these hypotheses to the test using systematic experiments. I We begin by setting out two initial hypotheses. These hypotheses will not be concerned directly with the actual patterns of people’s intuitions. Instead, they will be concerned with certain underlying psychological processes. But when the two hypotheses are put together (and combined with a few plausible assumptions), they yield definite testable predictions.

Abstract: In this précis of our recent book, Semantic Cognition: A Parallel Distributed Processing Approach (Rogers & McClelland 2004), we present a parallel distributed processing theory of the acquisition, representation, and use of human semantic knowledge. The theory proposes that semantic abilities arise from the flow of activation among simple, neuron-like processing units, as governed by the strengths of interconnecting weights; and that acquisition of new semantic information involves the gradual adjustment of weights in the system in response to experience. These simple ideas explain a wide range of empirical phenomena from studies of categorization, lexical acquisition, and disordered semantic cognition. In this précis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the “theory-theory” approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form “good ” or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (5) how concepts reorganize between the ages of 4 and 10, and (6) the relationship between causal knowledge and semantic knowledge. The explanations our theory offers for these phenomena are illustrated with reference to a simple feedforward connectionist model. The relationships between this simple model, the broader theory, and more general issues in cognitive science are discussed.

The mechanism by which humans perceive others differs greatly from how humans perceive inanimate objects. Unlike inanimate objects, humans have the distinct property of being “like me ” in the eyes of the observer. This allows us to use the same systems that process knowledge about self-performed actions, self-conceived thoughts, and self-experienced emotions to understand actions, thoughts, and emotions in others. The authors propose that internal simulation mechanisms, such as the mirror neuron system, are necessary for normal development of recognition, imitation, theory of mind, empathy, and language. Additionally, the authors suggest that dysfunctional simulation mechanisms may underlie the social and communicative deficits seen in individuals with autism spectrum disorders.

This study investigates the interaction between preschoolers ’ initial theories and their ability to learn causal relations from patterns of data. Children observed ambiguous evidence in which sets of two candidate causes co-occurred with an effect (e.g. A&B � E, A&C � E, A&D � E, etc). In one condition, all candidate causes were from the appropriate domain (a biological cause for a biological effect); in another condition, the recurring candidate cause, A, crossed domains (a psychological cause for a biological effect). When all causes were domainappropriate, children were able to use the data to identify A as a cause. When the recurring cause crossed domains, children were less likely to endorse A. However, preschoolers were significantly more willing to accept cross-domain causes after seeing the evidence than at baseline. A Bayesian model is proposed to explain this interaction. Very young children have remarkably sophisticated causal knowledge about the world. Children reason about the causes of mental states (e.g., Meltzoff, 1995), physical systems (e.g., Bullock, Gelman, & Baillargeon, 1982; Shultz, 1982), and biological events (e.g., Gelman & Wellman, 1991; Kalish, 1996). Preschoolers can even make predictions about hidden variables and explain events in terms of unobservable causes (Schulz & Sommerville, in press). Many researchers have suggested that children’s causal knowledge can be characterized as intuitive theories: abstract, coherent, defeasible representations of causal

Introduction One of the great mysteries of human cognition is how we learn to discover meaningful and useful categories and concepts about the world based on the data flowing from our sensors. Why do very young children acquire concepts like support and animate (Leslie 1988) rather than between three and six feet wide or blue with red and green dots? One answer to this question is that categories are created, refined and maintained to support accurate prediction. Knowing that an entity is animate is generally much more useful for the purpose of predicting how it will behave than knowing that it is blue with red and green dots. The idea of using predictability, or a lack thereof, as the driving force behind the creation and refinement of knowledge structures has been applied in a variety of contexts. Drescher (1991) and Shen (1993) used uncertainty in action outcomes to trigger refinement of action models, and McCallum (1995) an

Everyday human interaction relies on making inferences about social goals: goals that an intentional agent adopts in relation to another agent, such as “chasing”, “fleeing”, “approaching”, “avoiding”, “helping ” or “hindering”. We present a computational model of social goal inference that takes as input observations of multiple agents moving in some environmental context. The model infers a social goal for each agent that is most likely to have given rise to that agent’s observed actions, under an intuitive theory that expects agents to act approximately rationally. We provide evidence for our theory-based approach over a simpler bottom-up motion cue-based approach in a behavioral experiment designed to distinguish the two accounts.

Teleological explanations (TEs) account for the existence or properties of an entity in terms of a function: we have hearts because they pump blood, and telephones for communication. While many teleological explanations seem appropriate, others are clearly not warranted—for example, that rain exists for plants to grow. Five experiments explore the theoretical commitments that underlie teleological explanations. With the analysis of [Wright, L. (1976). Teleological Explanations. Berkeley, CA: University of California Press] from philosophy as a point of departure, we examine in Experiment 1 whether teleological explanations are interpreted causally, and confirm that TEs are only accepted when the function invoked in the explanation played a causal role in bringing about what is being explained. However, we also find that playing a causal role is not sufficient for all participants to accept TEs. Experiment 2 shows that this is not because participants fail to appreciate the causal structure of the scenarios used as stimuli. In Experiments 3–5 we show that the additional requirement for TE acceptance is that the process by which the function played a causal role must be general in the sense of conforming to a predictable pattern. These findings motivate a proposal, Explanation for Export, which suggests that a psychological function of explanation is to highlight information likely to subserve future prediction and intervention. We relate our proposal to normative accounts of explanation from philosophy of science, as well as to claims from psychology and artificial intelligence.

this paper benefited from support provided by NIH Grant R01-HD23922 to Frank Keil