Components of Syllogistic Reasoning.

Abstract

The present research sought to understand the components of syllogistic reasoning that are used in a syllogistic evaluation task. The research had two major goals. The first was to compare one particular model of syllogistic reasoning, the transitive-chain model of Guyote and Sternberg, to plausible alternative models that have been proposed in the past. Recent comparisons of the models using a response-selection task have provided convincing evidence of the superiority of the transitive chain model for this particular task, and the present research seeks to extend these findings to the response-evaluation task. The second goal was to separate experimentally the premise encoding and premise combination stages of syllogistic reasoning, thereby enabling (a) more direct tests of the various models' assumptions about each stage than has been possible in previous research, and (b) more direct inferences regarding the representations of relations between the subject and predicate of the premises as encoded and combined by subjects. This second goal was accomplished by a modified form of componential analysis whereby an information-processing task is decomposed into a series of nested subtasks that permit isolation of the elementary components of task performance.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1978
Accession Number
ADA054824

Entities

People

  • Margaret E. Turner
  • Robert Sternberg

Organizations

  • Yale University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Atmosphere Models
  • Atmospheres
  • Coding
  • Conversion
  • Data Sets
  • Errors
  • Information Processing
  • Information Science
  • Models
  • Numbers
  • Polarity
  • Psychology
  • Reliability
  • Task Performance And Analysis
  • Test And Evaluation
  • Universities

Fields of Study

  • Psychology

Readers

  • Artificial Intelligence
  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference