Release from response interference in color-word contingency learning

• Published in: Acta psychologica Vol. 244; p. 104187
• Main Authors: Roberts, Brady R.T., Forrin, Noah D., McLean, David, MacLeod, Colin M.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-04-2024; Elsevier
• Subjects: Contingency learning; Learning; PEP 2.0 model; Response interference; Learning; Contingency learning; Response interference; PEP 2.0 model
• ISSN: 0001-6918; 1873-6297
• DOI: 10.1016/j.actpsy.2024.104187

In identifying the print colors of words when some combinations of color and word occur more frequently than others, people quickly show evidence of learning these associations. This contingency learning effect is evident in faster and more accurate responses to high-contingency combinations than to low-contingency combinations. Across four experiments, we systematically varied the number of response-irrelevant word stimuli connected to response-relevant colors. In each experiment, one group experienced the typical contingency learning paradigm with three colors linked to three words; other groups saw more words (six or twelve) linked to the same three colors. All four experiments disconfirmed a central prediction derived from the Parallel Episodic Processing (PEP 2.0) model (Schmidt et al., 2016)—that the magnitude of the contingency learning effect should remain stable as more words are added to the response-irrelevant dimension, as long as the color-word contingency ratios are maintained. Responses to high-contingency items did slow down numerically as the number of words increased between groups, consistent with the prediction from PEP 2.0, but these changes were unreliable. Inconsistent with PEP 2.0, however, overall response time did not slow down and responses to low-contingency items actually sped up as the number of words increased across groups. These findings suggest that the PEP 2.0 model should be modified to incorporate response interference caused by high-probability associations when responding to low-probability combinations.
•One can quickly acquire an association between highly correlated stimuli attributes (e.g., a word and its font color).•The contingency learning effect refers to faster and more accurate responding to high-probability association.•This study tested the predictions of a computational model (PEP 2.0) of how we learn these simple associations.•Adding more response-irrelevant words led to faster responses for low-probability associations, counter to model predictions.•Our findings suggest that high-probability associations may cause interference when learning low-probability associations.