Implicit Learning of Parity and Magnitude Associations with Number Color

Associative learning can occur implicitly for stimuli that occur together probabilistically. It is debated whether probabilistic, implicit learning occurs not only at the item level, but also at the category level. Here, we investigated whether associative learning would occur between color and numerical categories, while participants performed a color task. In category-level experiments for each parity and magnitude, high-probability pairings of four numbers with one color were categorically consistent (e.g., the Arabic numerals 2,4,6, and 8 appeared in blue with a high probability, p = .9). Associative learning was measured as higher performance for high-probability vs. low-probability color/number pairings. For both parity and magnitude, performance was significantly better for high- vs. low-probability trials (parity: 3.1% more accurate; magnitude: 1.3% more accurate; 9 ms faster). Category-level learning was also evident in a subsequent color association report task with novel double-digit numbers (parity: 63% accuracy; magnitude: 55%). In control, item-level experiments, in which high-probability pairings were not categorically consistent (e.g., 2,3,6, and 7 appeared in blue with a high probability, p = .9), no significant differences between high- vs. low-probability trials were present. These results are in line with associative learning occurring at the category level, and, further, suggest automatic semantic processing of symbolic numerals in terms of parity and magnitude.