Categories and Feature Inference: Category Membership and a Reasoning Bias

Categories and Feature Inference: Category Membership and a Reasoning Bias Takashi Yamauchi (tya@psyc.tamu.edu) Na-Yung Yu (dbskdud40@tamu.edu) Department of Psychology, Mail Stop 4235 Texas AM Osherson, et al., 1990; Sloman, 1993, 1998; Sloutsky, 2003). The information about category membership may draw more attention; yet, people interpret category membership as they do for other perceptual and conceptual attributes. In this paper, we propose that category membership plays a special role in feature inferences, and molds people’s inferential behavior in a way other regular attributes cannot. Specifically, we propose that the awareness of category membership generally creates a reasoning strategy and biases people’s inferential behavior. Consider a simple prediction task in which one infers the value of an unknown feature on the basis of another stimulus (Figure 1) (Murphy & Ross, 1994; Yamauchi & Markman, 2000). In one case, two stimuli have the same arbitrary label “monek” (Figure 1a); in the other case, two stimuli have different labels “moneke” and “plaple. ” In this circumstance, we think that people generally apply the following reasoning rules proportional to the extent to which the two labels carry the information about category membership: Rule 1 – if two items belong to the same category, then the two items have characteristics in common; Rule 2 – if two items belong to different categories, then the two items have different characteristics. Undoubtedly, this reasoning strategy is erroneous because the members of natural categories are organized probabilistically, and the shared label does not necessarily guarantee shared features (and vice versa) (Rosch & Mervis, 1975). Moreover, psychological responses that we can observe in empirical studies are characteristically probabilistic. Therefore, such extreme “category-based” responses would rarely happen. However, we think that there is a cognitive bias to apply this “reasoning habit” in feature inferences when category information is transparent. This reasoning strategy reflects the mutual-exclusivity constraint suggested by E. Markman (1989) and the psychological essentialism assumption suggested by Medin and Otorny (1989), and Gelman (2003). For example, the reason why shared labels lead to shared features is because a category is bound by some unknown or unknowable essential features, and these essential features generate other features. Likewise, two categories are viewed as mutually exclusive because they are bound by two sets of essentially different features. We hypothesize that the mere presence of category labels promotes this rule-based reasoning strategy and generates polarity and uniformity in feature inference (e.g., Goldstone, 1994; Tajfel, 1963). For example, by applying this induction strategy, people accentuate the difference between two groups (i.e., polarity hypothesis), and discount perceptual variability of individual stimuli (i.e., uniformity hypothesis). We tested whether or not such reasoning biases would appear when the arbitrary labels carry category membership information. Experiment In our experiment, participants received pairs of a sample stimulus and a test stimulus one pair at a time (Figure 1), and predicted the feature value of a test stimulus on the basis of the sample stimulus. The stimuli were schematic illustrations of cartoon bugs, which were composed of 5 feature dimensions with binary values (Table 1). Twenty test stimuli were presented twice. In one case, a test stimulus was paired with a sample stimulus that had the same label, and in the other case, the same test stimulus was paired with a sample stimulus that had a different label (Figures 1a & 1b).