Event Observation in the Acquisition of Acoustic Transient Patterns.

Abstract

Three experiments investigated how individuals learn to classify sequentially-structured patterns of complex environmental sounds. In Experiment 1 listeners classified either auditory patterns or their visually-presented symbolic analogues as targets or nontargets. Some individuals received 'observation' trials on which they simply heard (saw) examples of the target patterns prior to classification. The observation trials were shown to be effective for target acquisition, and positive transfer occurred between symbolic observation and subsequent auditory pattern classification. The results of Experiments 2 and 3 suggested further that a relatively direct structural similarity must exist between the observed and classified target patterns for the observations to be effective. When this relation was subtle or abstract, positive transfer was limited. Post experimental tests in Experiments 1 and 3 also suggested that individual had learned something about the composition rules used to produce the target patterns rather than simple paired-associate responses. These findings have implications for pattern classification theory and for the design of performance aids for sonar technicians. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1981
Accession Number
ADA110522

Entities

People

  • James A. Ballas
  • James H. Howard Jr.

Organizations

  • The Catholic University of America

Tags

Communities of Interest

  • Biomedical
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Air Force
  • Applied Psychology
  • Behavioral Sciences
  • Biological Sciences
  • Control Systems
  • Engineering
  • Human Factors Engineering
  • Information Science
  • Military Research
  • Navy
  • Passive Sonar
  • Pattern Recognition
  • Psychology
  • Students
  • Systems Engineering
  • Target Acquisition

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Instructional Design and Training Evaluation.
  • Neural Network Machine Learning.