Department of Psychology, Arizona State University, Tempe, AZ 85287, USA.
Mem Cognit. 2013 Apr;41(3):339-53. doi: 10.3758/s13421-012-0271-8.
Two experiments investigated category inference when categories were composed of correlated or uncorrelated dimensions and the categories overlapped minimally or moderately. When the categories minimally overlapped, the dimensions were strongly correlated with the category label. Following a classification learning phase, subsequent transfer required the selection of either a category label or a feature when one, two, or three features were missing. Experiments 1 and 2 differed primarily in the number of learning blocks prior to transfer. In each experiment, the inference of the category label or category feature was influenced by both dimensional and category correlations, as well as their interaction. The number of cues available at test impacted performance more when the dimensional correlations were zero and category overlap was high. However, a minimal number of cues were sufficient to produce high levels of inference when the dimensions were highly correlated; additional cues had a positive but reduced impact, even when overlap was high. Subjects were generally more accurate in inferring the category label than a category feature regardless of dimensional correlation, category overlap, or number of cues available at test. Whether the category label functioned as a special feature or not was critically dependent upon these embedded correlations, with feature inference driven more strongly by dimensional correlations.
两个实验研究了当类别由相关或不相关的维度组成，且类别最小或中度重叠时的类别推理。当类别最小重叠时，维度与类别标签强烈相关。在分类学习阶段之后，在一个、两个或三个特征缺失的情况下，后续的转移需要选择类别标签或特征。实验 1 和 2 主要在转移前的学习块数量上有所不同。在每个实验中，类别标签或类别特征的推断都受到维度和类别相关性以及它们的相互作用的影响。当维度相关性为零时且类别重叠较高时，测试时可用线索的数量对性能的影响更大。然而，当维度高度相关时，只需少量线索就足以产生高水平的推断；即使重叠很高，额外的线索也有积极但降低的影响。无论维度相关性、类别重叠或测试时可用线索的数量如何，受试者在推断类别标签时通常比推断类别特征更准确。类别标签是否作为特殊特征起作用取决于这些嵌入的相关性，特征推断更多地受维度相关性的驱动。
