Categorical consistency of parity and magnitude facilitates implicit learning of color-number associations - Talia L. Retter; Christine Schiltz (PLoS ONE, 2025)
Perceiving high-frequency stimulus pairings may lead to implicit associative learning. Interestingly, category-level pairings, such as blue-even, may facilitate implicit learning relative to item-level pairings, such as blue-2 and blue-7. Such an advantage of categorical consistency has been previously demonstrated for associative learning with parity; here, we replicate this finding, and extend it to a second, more-often studied category, magnitude. In a parity experiment, participants reported the parity of single-digit numerals; numerals appeared in either blue or yellow, but throughout, participants were not given any information about color. In the novel magnitude experiment, the same participants reported the magnitude of single-digit numerals appearing in either purple or green. Associative learning was assessed through the comparison of response performance to congruent (high-frequency color-number parings; p = .9) vs. incongruent (low-frequency; p = .1) trials. A robust congruency effect was found at the category-level for both parity (accuracy: 8%; response time (RT): 54 ms) and magnitude (accuracy: 4%; RT: 37 ms), but not at the item-level. A third, novel parity-mix experiment, with purplish-blue and greenish-yellow, was also tested with these participants, in order to probe for potential interactions of colors associated across parity and magnitude dimensions. There was a congruency-effect advantage for parity-magnitude matching numerals vs. mismatching in terms of accuracy (4%), suggesting that color associations with conceptual categories may relate to each other. An explicit association report task revealed above-chance accuracy for the color of numerals for both parity and magnitude at the category-level, and for parity at the item-level. These results suggest that categorical consistency of multiple numerical concepts may facilitate implicit learning of both specific and multidimensional color-number associations.