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Release from response interference in color-word contingency learning
Brady R T Roberts1, Noah D Forrin1, David McLean1
1Department of Psychology, University of Waterloo, Waterloo, Canada.
Acta Psychologica
|February 17, 2024
Summary
People learn associations between words and colors, but adding more words unexpectedly speeds up responses to less frequent combinations. This challenges the Parallel Episodic Processing model, suggesting interference effects need consideration.
Area of Science:
- Cognitive Psychology
- Human Learning and Memory
Background:
- Contingency learning describes how people learn associations between stimuli, impacting response speed and accuracy.
- The Parallel Episodic Processing (PEP 2.0) model predicts stable learning effects regardless of irrelevant stimulus complexity.
Purpose of the Study:
- To test the Parallel Episodic Processing (PEP 2.0) model's prediction regarding contingency learning with increased irrelevant stimuli.
- To investigate how varying the number of response-irrelevant words affects the contingency learning effect.
Main Methods:
- Four experiments were conducted, manipulating the number of response-irrelevant words (3, 6, or 12) linked to three response-relevant colors.
- Participants identified the print colors of words, with varying color-word contingency ratios.
- Response times and accuracy were measured to assess the contingency learning effect.
Main Results:
- Contrary to PEP 2.0 predictions, the magnitude of the contingency learning effect did not remain stable as the number of words increased.
- While high-contingency item response times showed a non-significant numerical slowdown, low-contingency item response times unexpectedly sped up.
- Overall response times did not increase with more words, contradicting the model's stability prediction.
Conclusions:
- The findings disconfirm a key prediction of the PEP 2.0 model regarding contingency learning.
- The results suggest that response interference from high-probability associations, particularly with low-probability combinations, should be incorporated into the PEP 2.0 model.
- The study highlights the need for model refinement to account for complex interactions in associative learning paradigms.
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