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This is because the critical item is in between the two categories and it is always larger than a preceding item from Category 1 and smaller than a preceding item from Category 2. Thus, the cases of C1+Down and C2+Up are nearly impossible to happen for the critical item. Table 1 Model performance (AIC) on fit to transfer performance. Figure 4 The observed and predicted group difference on the critical item. Group 1 strongly classifies the critical item as Category 2, mean p(1) = 0.26, in either the C1+Up or C2+Down case. The performance of Group 1 in these two cases is not significantly different [t(10) = 0.10, p = 0.92]. This result is better accommodated by the decision bound model. See the triangle in Figure ?Figure4,4, which represents the prediction of the winning model. For Group 1, the winning model is the decision bound model. See [http://www.selleckchem.com/products/Elesclomol.html HSP activator] Table ?Table22 for the parameter values, which provide best [https://en.wikipedia.org/wiki/Tuclazepam tuclazepam] fits. The mean best-fit boundary b is 0.137, which equals 543 mel, locating in between the highest edge of Category 1 (520 mel) and the critical item (595 mel). Consequently, Group 1 shows CVE with no doubt. Table 2 Best-fitting parameter values. Group 2 clearly shows sequence effect on classifying the critical item. On classifying the critical item, when following a Category 1 item [p(1) = 0.71 for C1+Up], Group 2 tends to make a response of Category 1, whereas when following a Category 2 item, Group 2 tends to make a response of Category 2 [p(1) = 0.28 for C2+Down]. The difference on probability of Category 1 between these two cases is significant [t(17) = 3.89, p [http://www.selleckchem.com/products/Semagacestat(LY450139).html LY450139 in vitro] C1+Up case [p(1) = 0.81] and the C2+Down case [p(1) = 0.75]. For Group 3, the tendency to make classification for the critical item is not different in different categorization conditions [t(11) = 0.91, p = 0.38]. The performance of Group 3 is better fit by the decision bound model. The mean best-fit boundary b is 0.244, which equals 599.56 mel. This boundary is larger than the critical item, hence predicting the critical item as Category 1. The decision bound model's prediction for Group 3 can be seen in Figure ?Figure4.4. However, this result presumably can also be accommodated by GCM. Since GCM would always predict the critical item as the low-variability category (i.e., Category 1), it is hard to say that Group 3 relies on rule or exemplars for categorization. One thing for sure is that Group 3 does not show CVE and does not rely on some short-term representation for categorization. To sum up, a number of interesting findings in this experiment are listed as follow. First, CVE does occur in perceptual category learning (i.e., Group 1).