The mathematical theory of communication.

We propose a causal model theory to explain asymmetries in judgments of the intentionality of a foreseen side-effect that is either negative or positive (Knobe, 2003). The theory is implemented as a Bayesian network relating types of mental states, actions, and consequences that integrates previous hypotheses. It appeals to two inferential routes to judgment about the intentionality of someone else's action: bottom-up from action to desire and top-down from character and disposition. Support for the theory comes from three experiments that (...) test the prediction that bottom-up inference should occur only when the actor's primary objective is known. The model fits intentionality judgments reasonably well with no free parameters. (shrink)

Good reasoning can lead to success; bad reasoning can lead to catastrophe. Yet, it's not obvious how we reason, and why we make mistakes. This new book by one of the pioneers of the field, Philip Johnson-Laird, looks at the mental processes that underlie our reasoning. It provides the most accessible account yet of the science of reasoning.

Through the use of such "folk" concepts as belief, desire, intention, and expectation, Daniel Dennett asserts in this first full scale presentation of...

Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses (...) on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints. (shrink)

People’s concept of free will is often assumed to be incompatible with the deterministic, scientific model of the universe. Indeed, many scholars treat the folk concept of free will as assuming a special form of nondeterministic causation, possibly the notion of uncaused causes. However, little work to date has directly probed individuals’ beliefs about what it means to have free will. The present studies sought to reconstruct this folk concept of free will by asking people to define the concept (Study (...) 1) and by confronting them with a neuroscientific claim that free will is an illusion (Study 2), which invited them to either reconcile or contrast free will with determinism. The results suggest that the core of people’s concept of free will is a choice that fulfills one’s desires and is free from internal or external constraints. No evidence was found for metaphysical assumptions about dualism or indeterminism. (shrink)

When perceiving, explaining, or criticizing human behavior, people distinguish between intentional and unintentional actions. To do so, they rely on a shared folk concept of intentionality. In contrast to past speculative models, this article provides an empirically-based model of this concept. Study 1 demonstrates that people agree substantially in their judgments of intentionality, suggesting a shared underlying concept. Study 2 reveals that when asked to directly define the term intentional, people mention four components of intentionality: desire, belief, intention, and awareness. (...) Study 3 confirms the importance of a fifth component, namely, skill. In light of these findings, the authors propose a model of the folk concept of intentionality and provide a further test in Study 4. The discussion compares the proposed model to past ones and examines its implications for social perception, attribution, and cognitive development. (shrink)

In this provocative monograph, Bertram Malle describes behavior explanations as having a dual nature -- as being both cognitive and social acts -- and proposes...

Stephen Stich and Shaun Nichols advance the debate over folk psychology with their vivid depiction of the contest between the simulation theory and the theory-theory (Stich & Nichols, this issue). At least two aspects of their presentation I find highly congenial. First, they give a generally fair characterization of the simulation theory, in some respects even improving its formulation. Though I have a few minor quarrels with their formulation, it is mostly quite faithful to the version which I have found (...) attractive (Goldman, 1989).Second, I concur with Stich and Nichols in their assertion that the theory-theory and the simulation theory are the only two games in town. Where I disagree, of course, is in Stich and Nichols’s contention that their arguments so dim the prospects of the simulation theory that it should no longer be taken seriously. On the contrary, I shall maintain, the simulation theory remains a very plausible contender, and should receive careful and respectful consideration in the continuing investigation. (shrink)

The psychology of reasoning is increasingly considering agents' values and preferences, achieving greater integration with judgment and decision making, social cognition, and moral reasoning. Some of this research investigates utility conditionals, ‘‘if p then q’’ statements where the realization of p or q or both is valued by some agents. Various approaches to utility conditionals share the assumption that reasoners make inferences from utility conditionals based on the comparison between the utility of p and the expected utility of q. This (...) article introduces a new parameter in this analysis, the underlying causal structure of the conditional. Four experiments showed that causal structure moderated utility-informed conditional reasoning. These inferences were strongly invited when the underlying structure of the conditional was causal, and significantly less so when the underlying structure of the conditional was diagnostic. This asymmetry was only observed for conditionals in which the utility of q was clear, and disappeared when the utility of q was unclear. Thus, an adequate account of utility-informed inferences conditional reasoning requires three components: utility, probability, and causal structure. (shrink)

We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...) construct new causal maps and that their learning is consistent with the Bayes net formalism. (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)