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Related Experiment Videos

Automatic comparisons of artificial digits never compared: learning linear ordering relations.

J Tzelgov1, V Yehene, L Kotler

  • 1Department of Behavioral Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel. tzelgov@bgumail.bgu.ac.il

Journal of Experimental Psychology. Learning, Memory, and Cognition
|February 22, 2000
PubMed
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Participants learned to judge symbol magnitudes, developing an automatic size congruity effect. This automatic processing of magnitude relations persisted even for novel symbol pairs, indicating robust learning.

Area of Science:

  • Cognitive Psychology
  • Cognitive Science
  • Human Perception

Background:

  • Magnitude decisions involve understanding relative size.
  • The size congruity effect demonstrates automatic processing of physical size.
  • Automaticity in cognitive tasks implies processes occur without conscious effort.

Purpose of the Study:

  • To investigate the automaticity of magnitude relation processing.
  • To examine the development of the size congruity effect in magnitude decisions.
  • To determine if learned magnitude relations generalize to novel stimuli.

Main Methods:

  • Participants underwent training on magnitude decisions using arbitrary symbols.
  • Symbol pairs were presented, requiring participants to identify the larger magnitude.

Related Experiment Videos

  • A Stroop-like task was used to measure the size congruity effect on physical size judgments.

    • Main Results:

    • Training led to a significant size congruity effect in magnitude decisions.
    • This effect indicated automatic processing of the learned magnitude relations.
    • The size congruity effect was observed even for symbol pairs not encountered during training.

    Conclusions:

    • Magnitude decisions can lead to automatic processing of symbolic magnitude relations.
    • The findings support theories of automaticity, suggesting robust learning of abstract relations.
    • This research has implications for understanding how humans process and represent numerical and quantitative information.