Biomedical subjects

Arthur B Markman

Publications and source records attributed to Arthur B Markman.

4 recordsLinked to original sources

PubMed2003

Classification of exemplars with single- and multiple-feature manifestations: the effects of relevant dimension variation and category structure.

Most classification research focuses on cases in which each abstract feature has the same surface manifestation whenever it is presented. Previous research finds that people have difficulty learning to classify when each abstract feature has multiple surface manifestations. These studies created multiple manifestations by varying aspects of the stimuli irrelevant to the abstract feature dimension. In this article, multiple manifestations were created by varying aspects of the stimuli relevant to the abstract feature dimension. People given categories with the family resemblance category structure often used in psychology experiments had difficulty learning to classify when multiple manifestations were present, even though the variation was relevant. This effect was reversed when a family resemblance structure with nondiagnostic values was used.

Humans↗

PubMed2003

Category use and category learning.

Categorization models based on laboratory research focus on a narrower range of explanatory constructs than appears necessary for explaining the structure of natural categories. This mismatch is caused by the reliance on classification as the basis of laboratory studies. Category representations are formed in the process of interacting with category members. Thus, laboratory studies must explore a range of category uses. The authors review the effects of a variety of category uses on category learning. First, there is an extensive discussion contrasting classification with a predictive inference task that is formally equivalent to classification but leads to a very different pattern of learning. Then, research on the effects of problem solving, communication, and combining inference and classification is reviewed.

Classification↗

PubMed2003

The nonindependence of stimulus properties in human category learning.

Typically, models of category learning are verified through behavioral experiments with stimuli consisting of putatively independent dimensions such as shape, size, and color. The assumption of independence is critical in both the design of behavioral experiments and the development of models and theories of learning. Using the standard classification learning paradigm and a common stimulus set, the present work demonstrates that the assumption of independence is unwarranted. Systematic relations span stimulus dimensions and govern learning performance. For example, shape is not independent of size and color, because humans quantify size and color over shape when shape is relevant to the categorization. This quantification is reflected in natural language use (e.g., "blue triangle" as opposed to "triangle and blue"). In this example, color and size are predicates and shape is the argument. Across four experiments, the difficulty of mastering a classification rule can be predicted by the number of predicates that must be unbound in order to free rule-relevant stimulus dimensions.

Color Perception↗

PubMed2002

Learning nonlinearly separable categories by inference and classification.

Previous research suggests that learning categories by classifying new instances highlights information that is useful for discriminating between categories. In contrast, learning categories by making predictive inferences focuses learners on an abstract summary of each category (e.g., the prototype). To test this characterization of classification and inference learning further, the authors evaluated the two learning procedures with nonlinearly separable categories. In contrast to previous research involving cohesive, linearly separable categories, the authors found that it is more difficult to learn nonlinearly separable categories by making inferences about features than it is to learn them by classifying instances. This finding reflects that the prototype of a nonlinearly separable category does not provide a good summary of the category members. The results from this study suggest that having a cohesive category structure is more important for inference than it is for classification.

Analysis of Variance↗