Jonathan Livengood

Two Types of Typicality: Rethinking the Role of Statistical Typicality in Ordinary Causal Attributions

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.

Deep trouble for the deep self

Chandra Sripadas (2010) Deep Self Concordance Account aims to explain various asymmetries in peoples judgments of intentional action. On this account, people distinguish between an agents active and deep self; attitude attributions to the agents deep self are then presumed to play a causal role in peoples intentionality ascriptions. Two judgments are supposed to play a role in these attributions—a judgment that specifies the attitude at issue and one that indicates that the attitude is robust (Sripada & Konrath, 2011). In this article, we show that the Deep Self Concordance Account, as it is currently articulated, is unacceptable.

Behaviour Genetic Frameworks of Causal Reasoning for Personality Psychology: Behaviour genetics and causal reasoning

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

Ordering effects, updating effects, and the specter of global skepticism

One widely-endorsed argument in the experimental philosophy literature maintains that intuitive judgments are unreliable because they are influenced by the order in which thought experiments prompting those judgments are presented. Here, we explicitly state this argument from ordering effects and show that any plausible understanding of the argument leads to an untenable conclusion. First, we show that the normative principle is ambiguous. On one reading of the principle, the empirical observation is well-supported, but the normative principle is false. On the other reading, the empirical observation has only weak support, and the normative principle, if correct, would impugn the reliability of deliberative reasoning, testimony, memory, and perception, since judgments in all these areas are sensitive to ordering in the relevant sense. We then reflect on what goes wrong with the argument.