What is typical about the typicality effect in category-based induction?

Research on category-based induction has documented a consistent typicality effect: Typical exemplars promote stronger inferences about their broader category than atypical exemplars. This work has been largely confined to categories whose central tendencies are also the most typical members of the category. Does the typicality effect apply to the broad set of categories for which the ideal category member is considered most typical? In experiments with natural and artificial categories, typicality and induction-strength ratings were obtained for ideal and central-tendency exemplars. Induction strength was greatest for the central-tendency exemplars, regardless of whether the central tendency or the ideal was rated more typical. These results suggest that the so-called "typicality" effect is a special case of a more universal central-tendency effect in category-based induction.

Determining transformation distance in similarity: Considerations for assessing representational changes a priori.

The Representational Distortion (RD) approach to similarity (e.g., Hahn, Chater, & Richardson, 2003) proposes that similarity is computed using the transformation distance between two entities. We argue that researchers who adopt this approach need to be concerned with how representational transformations can be determined a priori. We discuss several roadblocks to using this approach. Specifically, we demonstrate the difficulties inherent in determining what transformations are psychologically salient and the importance of considering the directionality of transformations.

Assessing the concreteness of relational representation.

Research has shown that people's ability to transfer abstract relational knowledge across situations can be heavily influenced by the concrete objects that fill relational roles. This article provides evidence that the concreteness of the relations themselves also affects performance. In 3 experiments, participants viewed simple relational patterns of visual objects and then identified these same patterns under a variety of physical transformations. Results show that people have difficulty generalizing to novel concrete forms of abstract relations, even when objects are unchanged. This suggests that stimuli are initially represented as concrete relations by default. In the 2nd and 3rd experiments, the number of distinct concrete relations in the training set was increased to promote more abstract representation. Transfer improved for novel concrete relations but not for other transformations such as object substitution. Results indicate that instead of automatically learning abstract relations, people's relational representations preserve all properties that appear consistently in the learning environment, including concrete objects and concrete relations.

The optimal level of fuzz: Case studies in a methodology for psychological research.

Cognitive Science research is hard to conduct, because researchers must take phenomena from the world and turn them into laboratory tasks for which a reasonable level of experimental control can be achieved. Consequently, research necessarily makes tradeoffs between internal validity (experimental control) and external validity (the degree to which a task represents behavior outside of the lab). Researchers are thus seeking the best possible tradeoff between these constraints, which we refer to as the optimal level of fuzz. We present two principles for finding the optimal level of fuzz, in research, and then illustrate these principles using research from motivation, individual differences, and cognitive neuroscience.