10 Volume 15Number 1 Categorization Models bound rule of Figure 2Bie Place all

10 volume 15number 1 categorization models bound rule

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10 Volume 15—Number 1 Categorization Models
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bound rule of Figure 2B—i.e., ‘‘Place all but moderately large squares in category A.’’ When the range was large, about half of the participants had patterns like that in Figure 3B while a sizable minority had patterns like that in Figure 3C. This later pattern is consistent with exemplar theories. In another experiment, we manipulated stimulus confusability by adding a tick-marked ruler to the display with the stimuli. The stimuli were once again assigned to categories; the probabilities are indicated in Figure 4A. The critical stimuli in this design are sizes 2 and 5. The optimal rule is a one-bound solution that separates the smaller squares from the larger ones (rule depicted with a dotted line). With the ruler, the participant could measure the square size, mitigating perceptual uncertainty. Results are shown in Figure 4B. Participants who were not provided the ruler had a decreasing pattern which in itself does not contradict ei- ther exemplar- or rule-based processing. Participants who were provided the ruler had a more complex pattern with moderated proportions for the critical stimuli. This pattern is inconsistent with rule-based categorization. The reduction of stimulus con- fusion with a ruler resulted in a pattern exclusively consistent with exemplar-based processing. More recently, Nosofsky and Stanton (2005) employed a variant of this approach with stimuli that were varied on two dimensions. They found exemplar processing, and their results are consistent with ours. The trend in theory building is toward models with both exemplar and rule processes. Erickson and Kruschke (1998) advocate a neural-network model that, under appropriate cir- cumstances, allows behavior to transition from rule-based to exemplar-based processing. Nosofsky, Palmeri, and McKinley (1994) advocate a similar dual-process model: Participants use rules, but may augment them with exemplars if they deem their performance on certain stimuli is too poor. Neither the Erickson and Kruschke nor the Nosofsky et al. model explicitly accounts for stimulus confusion. Yet, because both rely on both exemplar- and rule-based processing, they may be easily adapted to ac- count for our results. In sum, our results provide support for the dual-processing approach as well as give an indication of how these two processes interact. (A) Proportion of Category A Responses .8 .2 .8 .2 .2 .8 70 105 62 90 90 111 Square Size (pixels) Square Size (pixels) Square Size (pixels) 90 30 135 Large Range Intermediate Range Small Range (C) (B) Fig. 3. Selected participants’ categorization behavior for three ranges of square sizes. Circles with error bars denote category-A response proportions and standard errors, respectively. Solid and dashed lines denote rule and exemplar predictions, respectively. Panel A shows characteristic results when the square sizes ranged from 70 to 105 pixels. The pattern of responses is consistent with a single decision bound (squares below a criterion are classified in category A; otherwise they are classified in category B). This pattern is not optimal—extremely large squares are systemat-
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  • Fall '17
  • Ruth Weintraub
  • Categorization, concept learning, Jeffrey N. Rouder

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