High-Level Cognitive Processes in Causal Judgments: An Integrated Model

High-Level Cognitive Processes in Causal Judgments: An Integrated Model Andrea Stocco (stocco@units.it) Danilo Fum (fum@units.it) Stefano Drioli (drioli@psico.units.it) Dipartimento di Psicologia, Universit`a di Trieste via S. Anastasio 12, I-34134, Trieste, Italy Investigating the role of compound cues in causal judgments, however, they obtained experimental findings that could not be explained, in their entirety, by either group of theories. In the paper we review the power PC theory and illustrate some results obtained in Fum & Stocco (2003) that seem to falsify it. We present a new model that, while being compat- ible with previous data, is able to explain those puzzling re- sults. We describe an experiment in which our model and the power PC make contrasting predictions, and we present find- ings that corroborate our hypothesis. We discuss some further data that, while implied by our model, are out of the scope of the power PC theory. We conclude the paper by summarizing the features of our account of human causal cognition and by outlining some possible developments. Abstract The problem of whether human causal judgments could be better explained by associationistic or probabilistic accounts is dealt with in the paper that reviews the basic tenets of the power PC theory (Cheng, 1997), the most famous of the prob- abilistic explanations, and discusses some results obtained by Fum & Stocco (2003) that are at odds with power PC predic- tions. An integrated model is described that is capable of ex- plaining those findings, and a new experiment is presented in which the predictions of the model and of the power PC the- ory are contrasted in the case in which the causal power of a compound cue is equal to one of its components. The results clearly corroborate the model that provides, moreover, an ex- planation for some data that lie outside the scope of the power PC theory. A Probabilistic Account Introduction Recent research on adult causal cognition has been focusing on two main kinds of theoretical explanations that capture many of the central findings in the field. Associative accounts (Shanks, 1995) consider the causal reasoning performed by humans as similar to the classi- cal conditioning happening in animals and claim that, be- cause both processes involve the detection of the same pre- dictive relations, they may use a common mechanism. The most famous model in this class is that of Rescorla & Wag- ner (1972)—henceforth RW if it approaches 1, C is perceived as producing O; if it approaches −1, the cue is seen as preventing the outcome. Relying on this idea, Cheng & Novick (1990) developed their probabilistic contrast model assuming that, in presence of a set of possible causes for an effect, the ∆P for each cause is computed on the so-called focal set, defined as “a contex- tually determined set of events that the reasoner uses as input to the covariation process” (Cheng, 1997, p. 371). When a putative cause is taken into account, all other causal factors are kept constant within the focal set, and ∆P is computed on a background of constant alternative causes. The transition from the probabilistic contrast model to the power PC theory was motivated by a series of problems that could not be adequately explained by the former nor by al- ternative associative accounts like the R&W. The power PC theory essentially computes how much a ∆P judgment should be discounted for providing an estimate of the causal power of a cue. It also detects special conditions in which the causal power cannot be deduced from ∆P. One of the tenets of the theory is that, whenever the possi- ble alternatives to a candidate cause C are kept under control and ∆P is non negative, C (i.e., the causal power of C to gen- erate the outcome O) is given by: C = ∆P c 1 − P(O|¬C)