Causal Structure in Conditional Reasoning
Causal Structure in Conditional Reasoning Tevye R. Krynski (tevye@mit.edu) Joshua B. Tenenbaum (jbt@mit.edu) Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology 77 Massachusetts Ave., Cambridge, MA 02139 The Wason selection task presents subjects with a conditional statement of the form “if p then q”, and asks subjects to choose evidence to determine whether the statement is true. Prior accounts of people’s responses on the selection task have emphasized logical reasoning (Wason, 1966; Ahn & Graham, 1999), probabilistic reasoning (Oaksford & Chater, 1994), or social reasoning (Cosmides, 1989), as well as others. In contrast, we argue that the selection task often engages causal reasoning: for conditional statements in which p and q are causally related, people choose cards that will be most useful to determine which of several candidate causal structures is correct for a given situation. We have developed a model that extends Oaksford & Chater’s (1994) probabilistic information gain framework to handle causal hypotheses. The information gain framework of OC Glymour & Cheng, 1998), category learning (Rehder, 2003; Ahn, 1999), and judgment under uncertainty (Krynski & Tenenbaum, 2003). In this paper we analyze the role of causal structure in conditional reasoning (Over & Jessop, 1998), and argue that people’s responses on the Wason selection task reflect sophisticated abilities to induce causal structure. An important open question in causal reasoning is how people’s background knowledge interacts with observations when inferring causal structure. Causal domain knowledge places important constraints on which cause-effect relationships exist and how the effects depend functionally on the causes (Pearl, 2000; Krynski & Tenenbaum, 2003; Ahn, Kalish, Medin, & Gelman, 1995). This effectively specifies a hypothesis space of candidate causal structures, which we model using causal Bayes nets (Pearl, 2000). Observational evidence can then be used to determine which causal structure is most likely. We propose that this interplay of causal domain knowledge and observational evidence underlies people’s judgments on the Wason selection task. Why interpret conditionals causally? In contrast to OC i.e., p causally influences q, q causally influences p, or they have some common cause. The term “causally influences” does not necessarily mean