Discontinuous categories affect information-integration but not rule-based category learning.
Discontinuous categories affect information-integration but not rule-based category learning.
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DOI：
10.1037/0278-7393.31.4.654
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发表时间：
2005-07
期刊：
影响因子：
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通讯作者：
W. Maddox;J. V. Filoteo;J. Lauritzen;Emily L Connally;Kelli D Hejl;G. Ashby;L. A. Alfonso-Reese;A. Turken;E. M. Waldron;Alfonso-Reese Ashby;Turken;Waldron;Les Cohen;B. Love;Matt Jones;A. Markman;Brian Stankiewicz;Todd Maddox
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文献类型：
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作者：
W. Maddox;J. V. Filoteo;J. Lauritzen;Emily L Connally;Kelli D Hejl;G. Ashby;L. A. Alfonso-Reese;A. Turken;E. M. Waldron;Alfonso-Reese Ashby;Turken;Waldron;Les Cohen;B. Love;Matt Jones;A. Markman;Brian Stankiewicz;Todd Maddox
本研究通过三个实验，直接考察了F·G·阿什比、L·A·阿方索·里斯、A·U·图尔肯和E·M·沃尔德龙(1998)提出的言语系统与内隐系统竞争模型中关于内隐、程序性学习和外显假设检验系统的类内间断操作。不连续的类别对信息整合有不利影响，但不影响基于规则的类别学习。增加不连续的大小并不会导致性能显著下降。到边界的距离提供了与假设检验系统相关联的泛化轮廓的合理描述，而到边界的距离加上到训练的响应区域的距离提供了与基于过程的学习系统相关联的泛化轮廓的合理描述。这些结果表明，类别内的不连续性对信息整合有不同的影响，但对基于规则的类别学习没有影响，并提供了关于每个类别学习系统的详细加工特征的信息。
Three experiments were conducted that provide a direct examination of within-category discontinuity manipulations on the implicit, procedural-based learning and the explicit, hypothesis-testing systems proposed in F. G. Ashby, L. A. Alfonso-Reese, A. U. Turken, and E. M. Waldron's (1998) competition between verbal and implicit systems model. Discontinuous categories adversely affected information-integration but not rule-based category learning. Increasing the magnitude of the discontinuity did not lead to a significant decline in performance. The distance to the bound provides a reasonable description of the generalization profile associated with the hypothesis-testing system, whereas the distance to the bound plus the distance to the trained response region provides a reasonable description of the generalization profile associated with the procedural-based learning system. These results suggest that within-category discontinuity differentially impacts information-integration but not rule-based category learning and provides information regarding the detailed processing characteristics of each category learning system.