Bayesian analyses strongly support sign priming for small addition problems in older adults but only anecdotal support for +1 problems¶

Claim · All claims
Evidence · 1 study · causal r2 · q3
Currency · newest study 2018 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? In older adults, Bayes factors showed substantial support for priming of large additions (BF10 = 3.41), very strong support for small additions (BF10 = 86.12), but only anecdotal support for the null for +1 problems (BF01 = 1.81). → Thevenot 2018

Evidence¶

Thevenot 2018¶

Thevenot, C., Dewi, J., Bagnoud, J., Wolfer, P., Fayol, M., & Castel, C. (2018). The use of automated procedures by older adults with high arithmetic skills during addition problem solving. https://eric.ed.gov/?id=ED

q3 · i? · causal · r2

Bayesian paired t-tests with Cauchy prior 0.707 on contrasts with significant SOA effects in older adults. "very strong for small addition problems (BF10 = 86.12)" but BF01 = 1.81 for +1 problems, weaker than in young adults.

"In older adults, the support for alternative hypothesis was substantial for large addition problems (BF10 = 3.41) and very strong for small addition problems (BF10 = 86.12). However, contrary to the frequentist approach repported above, the estimated Bayes factor (null/hypothesis) of BF01 = 1.81 for 1-digit addition problems suggested an anecdotal support for null hypothesis of a model without an effect of SOA."

Discussion¶

Related Claims¶

• Sign priming effects in addition occur for large, small, and +1 problems but not for tie or +0 problems in older adults — related
• Presenting the '+' sign 150 ms before operands speeds up addition problem solving in expert older adults, replicating the effect found in young adults — a broader claim this one bears on
• Older adults' slowdown relative to young adults is far smaller for problems interpreted as procedurally solved than for retrieval-solved problems — related
• Problem type modulated solution times, with large problems slowest and +1 problems fastest in both age groups — related
• Older adults were slower than young adults overall on solution times, with no age difference in accuracy — related