Automatic and Controlled Components of Judgment under Uncertainty

Automatic and Controlled Components of Judgment under Uncertainty Mario B. Ferreira (mabf@fpce.ul.pt), Leonel Garcia-Marques (garcia_marques@sapo.pt) Faculty of psychology and Education, Alameda da Universidade, 1649-013 Lisboa, Portugal Steven J. Sherman (sherman@indiana.edu) Department of Psychology, Indiana University Bloomington, IN 47405 Margarida Garrido (margarida.garrido@iscte.pt) Department of Social Psychology, ISCTE , Av. das Forcas Armadas 1649-026 Lisboa, Portugal Abstract The categorization of inductive reasoning into largely automatic processes (heuristic reasoning) and controlled analytical processes (rule-based reasoning) put forward by dual-process approaches of judgment under uncertainty (e.g., Stanovich & West, 2000) has been primarily a matter of assumption with a scarcity of direct empirical findings supporting it. We used the process dissociation procedure (Jacoby, 1991) to provide convergent evidence validating a dual-process perspective to judgment under uncertainty based on the independent contributions of heuristic and rule-based reasoning. Process dissociations based on experimental manipulation of variables were derived from relevant theoretical properties typically used to contrast the two forms of reasoning. These included processing goals (Experiment 1) and priming (Experiment 2). Results consistently supported the present perspective. We conclude that judgment under uncertainty is not either an automatic or controlled process, but that it reflects both processes, with each making independent contributions. Keywords: Heuristics; dual process models; automatic processes; controlled processes; judgment under uncertainty. Introduction From our perspective, the greatest contribution of more than 30 years of research concerning the use of heuristics and biases is not so much the realization that intuitive judgments are often governed by heuristics that do not follow probability rules, but the revelation of a gap, within our own heads, between “natural assessments” such as availability or representativeness and the deliberate application of a justifiable set of inductive rules. In recent years, dual-process approaches of judgment under uncertainty (e.g.,Kahneman & Frederick, 2002; Stanovich & West, 2000) have categorized the cognitive processes underlying inductive reasoning into two basic forms of reasoning: largely automatic associative processes, here referred to as heuristic reasoning (H) and controlled analytical processes, rule-based reasoning (RB). But, what are these two forms of reasoning; How do they work; and When do they become active? The what question: H refers to inferences based on simplifying principles such as similarity and contiguity, whereas RB refers to symbolically represented inferential rules structured by logic. The how question: H operates intuitively in the sense that once triggered it gives rise to an autonomous process without further control until an end response pops out into consciousness. RB’s operation involves the deliberate application of rules that are put to work strategically according to the person’s goals. The when question: H’s activation depends only on appropriate triggering cues (e.g., similarity matching involved in the representativeness heuristic), whereas RB’s activation depends on recognizing the applicability of an abstract rule (based on the verification of formal conditions), as well as on the availability of cognitive resources and motivation. Research on judgment under uncertainty has traditionally employed errors and biases in answers to inferential problems to characterize the underlying heuristic principles and their consequences (e.g., Tversky & Kahneman, 1974). In such research, RB is typically gauged in terms of correct responses (defined by applicable probability or statistical rules) or calibrated responses (defined by ecological considerations or objective criteria) to inferential problems, while H is usually estimated by incorrect or badly calibrated responses to the same kind of inferential tasks. This approach contrasts with our own both conceptually and methodologically. At the conceptual level, the above approach implies a zero-sum or hydraulic relation between the RB and the H process. As correct responses increase, incorrect responses necessarily decrease. Our dual-process approach conceives of the two processing modes as contributing independently to the judgment. At the methodological level, the above approach assumes that inferential problems or tasks are pure measures of underlying processes. However, such a process-pure assumption may be troublesome to maintain because tasks differ in a number of ways beyond the extent to which they tap H and RB. Rather, most, if not all, judgments under uncertainty will be influenced by simultaneously occurring heuristic and rule-based processes. Therefore, it is important to employ uncontaminated measures of processes through procedures that do not require or assume a one-to-one