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Cue interaction in human contingency judgment.

G B Chapman1, S J Robbins

  • 1Department of Psychology, University of Pennsylvania, Philadelphia 19104.

Memory & Cognition
|September 1, 1990
PubMed
Summary
This summary is machine-generated.

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Human contingency judgment is influenced by multiple cues, not just isolated frequency information. This study shows how cue interactions, like blocking and conditioned inhibition, affect perceived predictive strength.

Area of Science:

  • Cognitive Psychology
  • Behavioral Science
  • Human Learning

Background:

  • Traditional contingency judgment research often assumes isolated cue evaluation.
  • Emerging evidence indicates that judgments are influenced by interactions between co-present cues.
  • Understanding these interactions is crucial for a comprehensive model of predictive learning.

Purpose of the Study:

  • To investigate how cue interactions affect human contingency judgment.
  • To demonstrate phenomena like blocking and conditioned inhibition in human predictive learning.
  • To evaluate the applicability of animal conditioning models to human learning.

Main Methods:

  • Two experiments were conducted using within-subject designs.
  • Experiment 1 demonstrated blocking by comparing stimuli presented with strong vs. nonpredictive cues.

Related Experiment Videos

  • Experiment 2 examined conditioned inhibition by assessing negative predictive strength after compound presentations.

    • Main Results:

    • A stimulus compounded with a strong predictor was rated less predictive (blocking).
    • A stimulus gained negative predictive strength when presented with a positive predictor without the outcome (conditioned inhibition).
    • These findings indicate that cue interactions significantly alter perceived predictive strength.

    Conclusions:

    • Human contingency judgment is not solely based on isolated frequency information.
    • Cue interaction phenomena, such as blocking and conditioned inhibition, are observable in humans.
    • Animal conditioning models provide a valuable framework for understanding human contingency learning.