Semantic Interference as a Function of Arousal.

Abstract

One of curious aspects of the Stroop phenomena is that performance in the conflicting color-word task, which brings into play the opposite processes of language decoding and encoding at the same time, tends to improve in noise. A frequent explanation has been that noise-induced arousal enhances attentional selectivity, thus reducing interference caused by the automatic decoding of meaning. The present study based intensity of this interference as an independent variable. Bilingual subjects with different degree of their second language automaticity performed the encoding task (naming colors in which words that meant incongruent colors were printed) in their dominant language while the automatic decoding (interference) came from their weaker languages. The task was performed in quiet and in white noise. In general, noise improved performance but there was an interaction with the interfering language strength , the benefical effect of noise being a function of the automaticity of the interfering language relative to the automaticity of the encoding language. The position is taken here that the effect may be due to a less effective semantic processing brought about by increased arousal. In an additional experiment, a reversed task was used in which interference comes from the physical aspect (color) of words. Noise tended to increase interference, but only when the subjects' weaker language was used for reading. On the whole, the results seem to be equally well explained by noise affecting processing levels as by increased attentional selectivity. (Author)

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1982
Accession Number
ADA138987

Entities

People

  • S. Dornic

Organizations

  • Stockholm University

Tags

DTIC Thesaurus Topics

  • Automatic
  • Coding
  • Decoding
  • Language
  • Message Decoding
  • Message Processing
  • Noise
  • White Noise

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Computational Linguistics
  • Theoretical Analysis.