A lack of robust cross-domain structural priming effects

Area of Science:

• Cognitive Psychology
• Linguistics
• Cognitive Neuroscience

Background:

• Structural priming, observed in language and across domains like math-to-language, suggests abstract cognitive representations.
• Previous research indicated potential for mathematical structures to prime language, implying abstract processing.

Purpose of the Study:

• To investigate math-to-language structural priming, particularly with mathematical primes lacking overt operators (exponents).
• To replicate and test the robustness of previously reported math-to-language priming effects.
• To assess the impact of different experimental settings (online vs. in-person) on these effects.

Main Methods:

• Experiment 1 used mathematical primes without overt operators (exponents) to test priming.
• Experiments 2 and 3 attempted to replicate prior math-to-language priming findings using online and in-person methodologies.
• Statistical analyses and Bayes factor estimation were employed to evaluate the evidence for priming effects.

Main Results:

• Experiment 1 showed a non-significant numerical trend for math-to-language priming.
• Experiments 2 and 3, both individually and combined, failed to produce statistically significant math-to-language priming effects.
• Bayes factor analysis indicated that a null effect was more probable than a priming effect in the combined data.

Conclusions:

• The current findings challenge the robustness of math-to-language structural priming.
• Cross-domain structural priming remains understudied and underspecified, hindering research on abstract cognitive representations.
• Future research should increase item numbers and utilize diverse methodologies to better understand cross-domain priming.