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Nevin Manimala Statistics

A lack of robust cross-domain structural priming effects

Mem Cognit. 2026 Jun 3. doi: 10.3758/s13421-026-01901-6. Online ahead of print.

ABSTRACT

Structural priming effects within language (e.g., Bock, 1986) have guided theory and research on structural representation for several decades. Structural priming has also been observed across domains, such as from mathematics to language (e.g., Scheepers et al., 2011), suggesting highly abstract structural representation within the global cognitive system. Experiment 1 investigated how this effect is impacted by a mathematical structural prime that lacks an overt operator, as is the case with exponents. A weak numerical trend toward a math-to-language priming effect was not found to be statistically significant. Experiments 2-3 sought to replicate Scheepers et al.’s (2011) original math to language priming effects in online and in-person settings, respectively. Separately and combined, these experiments failed to yield significant math to language priming effects, despite robust sample sizes. Bayes factor estimates suggest a null effect was more likely than a priming effect in the combined dataset. These results highlight the fact that cross-domain structural priming is understudied and underspecified, leading to difficulty planning and implementing the types of studies needed to establish when and how abstract structural representations persist across cognitive domains. Recommendations for future research include increasing item numbers and exploring methodologies that measure processing as well as behavioral responses.

PMID:42237051 | DOI:10.3758/s13421-026-01901-6

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