Recent work on the role of norms in the use of causal language by ordinary people has led to a consensus among several researchers: The consensus position is that causal attributions are sensitive to both statistical norms and prescriptive norms. But what is a statistical norm? We argue that there are at least two types that should be distinguished-agent-level statistical norms and population-level statistical norms. We then suggest an alternative account of ordinary causal attributions about agents (the responsibility view), noting that this view motivates divergent predictions about the effect of information about each of the two types of statistical norms noted. Further, these predictions run counter to those made by the consensus position. With this set-up in place, we present the results of a series of new experimental studies testing our predictions. The results are in line with the responsibility view, while indicating that the consensus position is seriously mistaken. Consider the following scenario based on a thought experiment by Joshua Knobe (2006):Lauren and Jane both work for a company that uses a mainframe that can be accessed from terminals on different floors of its building. The mainframe has recently become unstable, so that if more than one person is logged in at the same time, the system crashes. Therefore, the company has instituted a temporary policy restricting the use of terminals so that two terminals are not used at the same time until the mainframe is repaired. The policy prohibits logging into the mainframe from the terminal on any floor except the ground floor.One day, Lauren logged into the mainframe on the authorized terminal on the ground floor at the exact same time that Jane logged into the mainframe on the unauthorized terminal on the second floor. Lauren and Jane were both unaware that the other was logging in. Sure enough, the system crashed.When this scenario is given to people without training in philosophy, they are significantly more likely to say that Jane caused the system to crash than Lauren (Livengood, Sytsma, and Rose, Not surprisingly, some philosophers have sought to explain such results away, aiming to preserve the assumption that the ordinary concept of causation is purely descriptive in character.For example, Julia Driver (2008a) suggests that ordinary causal attributions might still be best explained in terms of a type of descriptive consideration-statistical norms. Thus, it might be that Jane is identified as the cause of the system crashing, in the above example, not because she violated a prescriptive norm per se, but because she did something that is statistically atypical in doing so-it presumably being unusual to violate company policy. Subsequent empirical work has suggested against this, however. In fact, a consensus position has emerged that focuses on norms more generally: Ordinary causal attributions are thought to be directly sensitive both to behaviors being out of the prescriptive norm (impermissible) and to behaviors being out of the s...

Abstract:In two fascinating articles, Machery, Mallon, Nichols, and Stich [2004, forthcoming] use experimental methods to raise a specter of doubt about reliance on intuitions in developing theories of reference which are then deployed in philosophical arguments outside the philosophy of language. Machery et al. ran a cross-cultural survey asking Western and East Asian participants about a famous case from the philosophical literature on reference (Kripke's Gödel example). They interpret their results as indicating that there is significant variation in participants' intuitions about semantic reference for that case. We argue that this interpretation is mistaken. We detail a type of ambiguity found in Machery et al.'s probe but not yet noted in the response literature. We argue that this epistemic ambiguity could have affected their results. We do not stop there, however: Rather than rest content with a possibility claim, we ran four studies to test the impact of this ambiguity on participants' responses. We found that this accounts for much of the variation in Machery et al.'s original experiment. We conclude that in the light of our new data, their argument is no longer convincing. Keywords:Reference; Semantic Intuitions; Experimental Philosophy; Machery, Mallon, Nichols, and Stich; Kripke; Gödel In two provocative articles, Edouard Machery, Ron Mallon, Shaun Nichols, and Stephen Stich [2004, forthcoming] have argued that there is a problem with the standard methodology for work on reference in modern analytic philosophy. They argue that this methodology attempts to construct theories consistent with our intuitions about prominent fictional and non-fictional cases [2004: B3]. Philosophical method presupposes that intuitions for such cases are sufficiently uniform across the relevant populations of people.2 Call this presupposition the uniformity conjecture. The uniformity conjecture is a testable, empirical claim (once sufficiently specified).However, philosophers have offered no empirical evidence that the conjecture is true.

Identifying causal relations from correlational data is a fundamental challenge in personality psychology. In most cases, random assignment is not feasible, leaving observational studies as the primary methodological tool. Here, we document several techniques from behaviour genetics that attempt to demonstrate causality. Although no one method is conclusive at ruling out all possible confounds, combining techniques can triangulate on causal relations. Behaviour genetic tools leverage information gained by sampling pairs of individuals with assumed genetic and environmental relatedness or by measuring genetic variants in unrelated individuals. These designs can find evidence consistent with causality, while simultaneously providing strong controls against common confounds. We conclude by discussing several potential problems that may limit the utility of these techniques when applied to personality. Ultimately, genetically informative designs can aid in drawing causal conclusions from correlational studies. Copyright

Many philosophers have worried about what philosophy is. Often they have looked for answers by considering what it is that philosophers do. Given the diversity of topics and methods found in philosophy, however, we propose a different approach. In this article we consider the philosophical temperament, asking an alternative question: What are philosophers like? Our answer is that one important aspect of the philosophical temperament is that philosophers are especially reflective: They are less likely than their peers to embrace what seems obvious without questioning it. This claim is supported by a study of more than 4,000 philosophers and non-philosophers, the results of which indicate that even when we control for overall education level, philosophers tend to be significantly more reflective than their peers. We then illustrate this tendency by considering what we know about the philosophizing of a few prominent philosophers. Recognizing this aspect of the philosophical temperament, it is natural to wonder how philosophers came to be this way: Does philosophical training teach reflectivity or do more reflective people tend to gravitate to philosophy? We consider the limitations of our data with respect to this question and suggest that a longitudinal study be conducted.

Behavior genetic findings figure in debates ranging from urgent public policy matters to perennial questions about the nature of human agency. Despite a common set of methodological tools, behavior genetic studies approach scientific questions with potentially divergent goals. Some studies may be interested in identifying a complete model of how individual differences come to be (e.g., identifying causal pathways among genotypes, environments, and phenotypes across development). Other studies place primary importance on developing models with predictive utility, in which case understanding of underlying causal processes is not necessarily required. Although certainly not mutually exclusive, these two goals often represent tradeoffs in terms of costs and benefits associated with various methodological approaches. In particular, given that most empirical behavior genetic research assumes that variance can be neatly decomposed into independent genetic and environmental components, violations of model assumptions have different consequences for interpretation, depending on the particular goals. Developmental behavior genetic theories postulate complex transactions between genetic variation and environmental experiences over time, meaning assumptions are routinely violated. Here, we consider two primary questions: (1) How might the simultaneous operation of several mechanisms of gene-environment interplay affect behavioral genetic model estimates? (2) At what level of gene-environment interplay does the 'gloomy prospect' of unsystematic and non-replicable genetic associations with a phenotype become an unavoidable certainty?