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Theory-based categorization under speeded conditions.

Christian C. Luhmann Woo-kyoung Ahn Thomas J. Palmeri Vanderbilt University

Corresponding Author: Christian C. Luhmann Department of Psychology, Vanderbilt University Wilson Hall, 111 21st Avenue South, Nashville, TN 37203 (615) 322-2835 [email protected]

Running head: speeded categorization

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Theory-based categorization 1

Abstract

A largely accepted view in the categorization literature is that similarity-based reasoning is faster than theory-based reasoning. In the current study, we explored whether theory-based categorization behavior would continue to be observed when people are forced to make category decisions under time pressure. As a specific test of the theory-based view of category representation, we examined the causal status hypothesis, which states that properties acting as causes are more important than properties acting as effects when categorizing an item (Ahn, Kim, Lassaline, & Dennis, 2000). Subjects learned four categories of items composed of three features and learned causal relations between those features. In two experiments we found that participants gave more weight to cause features than to effect features even under rapid response conditions. categorization.

We discuss implications of these findings for

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When categorizing in everyday life people recruit information from various sources. Previous work on categorization has generally focused on two types of information. On one view, concept learning and use involves computing the similarity between novel objects and a stored representation of a category (e.g., Kruschke, 1992; Nosofsky, 1986; Smith & Medin, 1981; Rosch & Mervis, 1975). An alternative view assumes that people have theory-like background knowledge that includes relations between properties and influences categorization (Carey, 1985; Keil, 1989; Murphy & Medin, 1985; Rips, 1989). For example, adults categorize animals with the appearance and behavior of a horse but the cow insides and lineage as cows (Keil, 1989). This behavior suggests that lineage has a special status beyond perceptual features, presumably due to our lay theories of biology. While these two views have been traditionally portrayed in opposition (e.g. Sloman, 1996; Murphy & Medin, 1985), recently many advocate the use of both kinds of information (e.g. Smith & Sloman, 1994). However, these proposals typically put the two views on unequal footing. A persistent bias is that similarity-based categorization proceeds more rapidly than theory-based information. This assumption may be motivated by the belief that novices (e.g., children) use similarity-based reasoning and is thus a simpler mode of reasoning (cf. Keil, Smith, Simons, & Levin, 1998). Smith and Sloman (1994) state this explicitly by arguing that theory-based reasoning is, “more analytic and reflective than similarity-based categorization” (pp. 377-378).

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Smith and Sloman (1994) examined the effect of time constraints on theory-based categorization. Following Rips (1989), subjects received a forced-choice task where each item consisted of a description of an object (e.g., circular object with a 4 inch diameter) and two possible responses: one signifying a theory-based decision (e.g., calling it a pizza rather than a quarter because of the constraints imposed by the minting process) and the other signifying a similarity-based decision (e.g., calling it a quarter because the object is more similar to quarters; but see Nosofsky & Johansen, 2000). While Rips’ (1989) subjects preferred the theory-based response, when Smith and Sloman asked their subjects to respond as quickly as possible, they failed to reproduce these results. Only when instructed to talk aloud while categorizing did subjects prefer the theory-based response. Smith and Sloman concluded that a “…possible constraint…is that the situation encourage people to articulate and explain their reasons for categorization, rather than encourage rapid judgments” (p. 383). This study constitutes the main evidence that theory-based categorization is a slow process. One problem with this Smith and Sloman’s (1994) interpretation is that any judgment resulted in subjects’ accepting bizarre objects as category members. For the pizza/quarter question, one must entertain the idea of both a non-standard quarter (violating ontological beliefs) and an unusual pizza (violating pragmatic beliefs). Thus, the task may become a judgment between a “lesser of two evils, ” which does not represent a naturalistic test of the theory use.1

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It is important to point out that Smith and Sloman (1994) never obtained a preference for similarity-based choices in their speeded condition. When speeded, subjects simply showed no preference when offered both similarity and theory-based responses.

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More recent studies have suggested that theory-based categorization may be faster than previously thought. Lin and Murphy (1997) taught subjects about novel objects and their intended functions. Some object parts were central to this function while others played minor functional roles. The results showed that functionally central parts influenced categorization judgments more than functionally irrelevant parts even when the picture of the object to be categorized was presented for only 50ms, demonstrating rapid influence of domain knowledge. Palmeri and Blalock (2000) reported a similar pattern of results. Following Wisnewski and Medin (1994), they had subjects categorize drawings supposedly drawn by ”creative children” or ”non-creative children.” Subjects were able to categorize using this background knowledge (e.g., the amount of emotional expression) even when the drawings were shown for only 200ms. Their results suggest that theory-use does not require lengthy periods of reflection. Although this recent evidence is compelling, the effect of background knowledge demonstrated in these two studies is limited to perceptual categorization. The current studies utilize verbal stimuli as in Smith and Sloman (1994) to examine speeded theorybased effects in conceptual categorization. More importantly, both studies fail to provide a principled account of the theory-based mechanisms at work. Palmeri and Blalock (2000) allowed the subjects to generate categorization rules given their background knowledge (i.e. category labels), but did not attempt to characterize this process. Lin and Murphy (1997) assume that theory-based categorizing of artifacts relies on functional features, but it is not clear why some functional features (e.g., used to hang a tool on the

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wall) are more central than others (e.g., used to grab an animal’s neck) (but see Ahn, 1998). In the current study, we test a well-articulated theory-based mechanism under speeded conditions. As a specific test of the theory-based view, we have chosen to investigate the causal status hypothesis (Ahn, 1998; Ahn, et al., 2000), which states that features of an object that act as causes in one’s domain theory are more important than features that act as effects. To test this hypothesis, Ahn, et al. (2000) presented subjects with novel categories with a list of features (e.g., X, Y, Z). Subjects were also told that feature X caused feature Y, which in turn caused feature Z. When subjects received descriptions of possible category members, those items missing feature Z were rated as the best category members, items missing feature X were rated as the worst category members, and items missing feature Y fell midway. These findings strongly support the idea that causes are more important than effects when categorizing. The purpose of the current studies is to examine whether the use of theories requires extended deliberation. To do so, we employed a methodology similar to Ahn, et al. (2000); we created artificial categories with causally related features and had subjects judge the category membership of items. In addition, we manipulated the amount of time pressure on making such judgments.

Experiment 1

Method Participants.

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Twenty-nine Vanderbilt University undergraduates participated in partial fulfillment of course requirements. Materials. The stimuli consisted of four fictional animals each with three features (e.g. X, Y, and Z). The features were described as forming a causal chain such that feature X causes feature Y and feature Y causes feature Z. An included summary diagram contained arrows depicting the presence and direction of a causal relationship. The features were chosen to facilitate intuitive causal connections between adjacent features (e.g., a small heart causes a low body temperature presumably due to weak circulation) in order to approximate the effect of entrenched naïve theories. To ensure that the three features in each category did not vary in importance in the absence of causal information, a separate set of 30 subjects were presented with features without causal information and rated the category membership likelihood of items missing a single feature. The pre-test results showed no significant differences between the ratings of items missing the feature X (M = 3.90), items missing feature Y (M = 4.38), and items missing the feature Z (M = 4.25; all p’s>.4). Thus, we concluded that the features were equated for a priori saliency. Procedure. The experiment was run on Apple iMacs using RSVP (Williams & Tarr, No date). During the learning phase, subjects were first given the opportunity to study the description of each animal one at a time. For each animal they were given the list of features, the causal relationships between them, and the summary diagram. While each description remained on the computer screen, subjects were instructed to “write about

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how you think each feature causes the next.” This was done to encourage subjects to think causally about the features instead of as a simple ordered list. After viewing and writing about all animals, subjects were presented with 6 blocks of trials during which they were prompted with the name of an animal and required to select (using a mouse-click) that animal’s features from a table containing all 4 animals’ features. On each trial one of four tables was randomly chosen, each with a unique arrangement of the features so that subjects could not simply memorize locations in the table. Subjects were additionally required to select the correct features for each animal in the appropriate causal order (see below). After each selection, subjects were presented with feedback about their selection as well as a summary of that animal’s features and the causal relationships. Successfully responding to the entire set of animals twice consecutively, with one allowed error, permitted subjects to move on to the next block. In the first two blocks responses were unspeeded. In the last four blocks subjects were told to respond as quickly as they could, and any responses that took longer than 5 seconds were counted as incorrect. This speeded element was added to automatize the use of the novel causal background knowledge thereby approximating real-life lay theories. In addition, on half of the blocks subjects were asked for the features in the forward (e.g. X, Y, Z) order, while in the other half in the backward order (e.g. Z, Y, X) to prevent features from being rated as central simply because it was always presented first in the list. The order manipulation alternated across blocks always beginning with a forward block. Once completing the learning phase, subjects proceeded to the transfer task. They were presented with items missing a single feature and asked to rate the likelihood that

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the item belonged to its target category on an 8-point scale (with 1 labeled as “Definitely Unlikely” and 8 labeled as “Definitely Likely”). Subjects were told that since every item would be missing exactly one feature there would be no perfectly good category members and no perfectly bad category members and were encouraged to use the entire scale. Trials began with the name of one of the animals (the target category) appearing on the screen for one second and then replaced by a triad of features. The three features belonged to the target category with one feature negated by appending the phrase “does not.” The features were presented in a triangular arrangement with the location of each feature in the triangle randomly determined for each trial to discourage reliance on spatial information. Additionally, the negated feature was displayed in red to facilitate reading of stimuli. This should not have qualitatively altered performance since the other two features are logically inferable from the negated feature given that each item would contain the target category’s features with exactly one feature negated. There were 4 blocks of these trials each consisting of each of the 12 items (4 categories with 3 features each) presented 4 times for a total of 48 trials presented in random order. In two of the blocks (Speeded condition), subjects were instructed to answer “as quickly as possible while still remaining accurate” and given an example of how medical professionals often had to make decisions that were both accurate and rapid. In the other two blocks (Unspeeded condition), they were told to “take as much time as needed”. The two conditions alternated across blocks and were counterbalanced across subjects. The RTs in the speeded blocks (M=1560ms) were significantly faster than the

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RTs in the unspeeded blocks (M=3202ms), t(170)=21.09, p