Simple heuristic or knowledge-based inference? Model comparison of binary choice inference

Simple heuristic or knowledge-based inference? Model comparison of binary choice inference Hidehito Honda (hito@muscat.L.chiba-u.ac.jp) Toshihiko Matsuka (matsukat@muscat.L.chiba-u.ac.jp) Department of Cognitive and Information Science, Chiba University 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan Abstract We investigated the processes of making inference in binary choice tasks. We proposed statistical models for two simple heuristics and two knowledge-based inferences, and com- pared how well these models could explain the observed pat- terns of inferences. It was found that the best model for ex- plaining choice patterns varied depending on the inference problem. In particular, results suggested that participants used a simple heuristic when they had difficulty in retrieving clues pertaining to the inference problem. In contrast, when partici- pants could retrieve enough clues pertaining to inference problems, they made inferences based on these clues. Keywords: simple heuristics; knowledge-based inferences; binary choice inferences; model comparison Introduction In the last decade, a highly controversial topic in research on judgment and decision-making has been the recognition heuristic (Goldstein & Gigerenzer, 2002). When the recog- nition heuristic is applied to a binary choice problem, the inference rule is described as follows: “If one of two objects is recognized and the other is not, then infer that the recognized object has the higher value with respect to the criterion. ” (Goldstein and Gigerenzer, 2002, p.76) For example, consider the following question, “Which city has the larger population, Tokyo or Chiba? ” In this problem, the recognition heuristic predicts that when a person recog- nizes Tokyo but not Chiba, the person will infer that Tokyo has the larger population. Many researchers have discussed the recognition heuris- tic from theoretical, empirical, and rational perspectives. For example, in the journal Judgment and Decision Making, a special issue on the recognition heuristic was published in 2010 and 2011, which included 24 papers in three volumes. In particular, researchers have been interested in whether the recognition heuristic is actually used in making infer- ences. Some researchers have claimed that people often make inferences based on the recognition heuristic in binary choice situations, in which people recognize one of the ob- jects in a pair 1 (Goldstein & Gigerenzer, 2002; Pachur, Bro- der, & Marewski, 2008; Pachur & Hertwig, 2006; Reimer & Hereafter, depending on the recognition of objects in a pair, we use following abbreviations: R-U (Recognized-Unrecognized), R- R (Recognized-Recognized), and U-U (Unrecognized- Unrecognized). katsikopoulos, 2004; Snook & Cullen, 2006; Volz et al., 2006). In contrast, others have argued that although people make inferences using the recognition heuristic in some R-U pairs, people often make inferences using knowledge per- taining to the inference problem when such knowledge is available (Broder & Eichler 2006; Hilbig, Pohl, & Broder, 2009; Glockner & Broder, 2011; Newell & Fernandez, 2006; Newell & Shanks, 2004; Oeusoonthornwattana & Shanks, 2010; Oppenheimer, 2003; Pohl, 2006; Richter & Spath, 2006). The recognition heuristic can be applied only to R-U pairs. Thus, although many studies have accumulated evi- dence for both the arguments, they have focused on the pro- cessing of inference in R-U pairs. Then, how do people make inferences when they recognize both objects of the pair? Previous studies have assumed that in the case of R-R pairs, inferences are made by using knowledge (e.g., Gold- stein & Gigerenzer, 2002; Hilbig, 2010). As described above, many studies have suggested that when people have knowledge in addition to mere recognition, inferences are affected by the knowledge. However, specific models of knowledge-based inferences have not been proposed to date. Furthermore, people may make inferences using a sim- ple heuristic such as the recognition heuristic, even in the case of R-R pairs. For example, Hertwig, Herzog, Schooler, and Reimer (2008) proposed a fluency heuristic, which is an inference strategy using the retrieval fluency of objects, and Honda, Abe, Matsuka, and Yamagishi (2011) proposed a familiarity heuristic, which is the inference strategy using the familiarity of objects. The goal of the present study was to examine binary choice inferences in not only R-U pairs, but also in R-R pairs. Brighton and Gigerenzer (2009) argued that when examining the validity of the recognition heuristic, alterna- tive models should be proposed and compared. Accordingly, we approached the above issue by comparing models. In particular, we examined binary choice of population infer- ences, by comparing the validity of models representing a simple heuristic and knowledge-based inference. Models of binary choice inference For each pair of cities, the two cities can be ordered by their actual populations, and we name them accordingly. For ex- ample, if cities X and Y are presented, and the city X has a larger population than city Y, we call city X the “larger city,” and city Y the “smaller city. ”