Abstract :
[en] 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.
Funding text :
This work was supported by structural funding allocated by the Faculty of Humanities, Education and Social Sciences of the University of Luxembourg (FHSE/UL; https://www.uni.lu/ fhse-en/; to CS at the CNSlab/EPSYLON) and a postdoc fellowship from the France 2030 program Initiative d\u2019Excellence Lorraine (LUE; https://www.univ-lorraine.fr/lue/; to TLR as part of ANR-15-IDEX-04-LUE). We gratefully offer thanks to undergraduate student assistants Aur\u00E9lie Marochi, Laura Klein, Sarah Niesmann, and Nicole Gerasimova, for their fundamental role in data collection. We also thank the journal editor and two anonymous reviewers for their positive but critical comments, which have led to considerable improvement of an earlier version of this manuscript.
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