Biomedical subjects

T L Spalding

Publications and source records attributed to T L Spalding.

4 recordsLinked to original sources

PubMed2001

Preferences for ascending and descending hierarchical organization in spatial communication.

People prefer to order spatial information in a hierarchy of decreasing size of spatial unit when giving directions for finding objects and in a hierarchy of increasing size of spatial unit when providing descriptions of object locations (Plumert, Carswell, DeVet, & Ihrig, 1995). In five experiments, we examined whether people have a preference for ascending or descending organization when the task does not involve conveying spatial information to others. In Experiments 1-3, people learned the locations of objects in a model house and then verified statements describing those locations. People verified statements faster when spatial units were organized in an ascending (i.e., small to large) than in a descending (i.e., large to small) or random order. In Experiment 4, people first performed a sentence verification task and afterward wrote down directions for finding the objects. People again exhibited a preference for ascending organization in the verification task but exhibited a preference for descending organization when giving directions for finding the same objects. Experiment 5 demonstrated that the ascending advantage was not due to the link between the object and small landmark. Discussion focuses on the role of pragmatics and memory retrieval in preferences for ascending versus descending hierarchical organization.

Choice Behavior↗

PubMed2000

Concept learning and feature interpretation.

Models of categorization often assume that people classify new instances directly on the basis of the presented, observable features. Recent research, however, has suggested that the coherence of a category may depend in part on more abstract features that can link together observable features that might otherwise seem to have little similarity. Thus, category learning may also involve the determination of the appropriate abstract features that underlie a category and link together the observable features. We show in four experiments that observable features of a category member are often interpreted as congruent with abstract features that are suggested by observable features of other highly available category members. Our discussion focuses on the implications of these findings for future research.

Adult↗

PubMed1999

What is learned in knowledge-related categories? Evidence from typicality and feature frequency judgments.

When a category's features are tied together by integrative knowledge, subjects learn the category faster than when the features are not directly related. What do subjects learn about the category in such circumstances? Some research has suggested that the subjects can use the knowledge itself in performing the category learning task and, thus, do not learn the details of the category's features. Two experiments investigated this hypothesis by collecting feature frequency estimates after category learning. The results showed that integrative knowledge about a category did not decrease subjects' sensitivity to feature frequency--if anything, knowledge improved it. A third experiment found that integrative knowledge did reduce sensitivity to feature frequency in typicality ratings. The results suggest that knowledge does not inhibit the learning of detailed category information, though it may replace its use in some tasks.

Adult↗

PubMed1994

Comparison-based learning: effects of comparing instances during category learning.

When learning about a category, people often compare new instances with similar old instances and notice features common to the compared instances. Five experiments demonstrate that such comparisons cause features common to compared instances to be considered more important for the category than equally frequent features that are not common to compared instances. Experiment 1 shows that what is learned depends on which instances are compared. Experiment 2 investigates the conditions under which comparison-based learning occurs. The next experiments find that these comparisons affect subjective feature frequency (Experiment 3) and sensitivity to feature correlations (Experiment 4). Experiment 5 shows that comparisons during early learning affect what is learned from later instances. The discussion focuses on the implications for models of category representation.

Color Perception↗