Individual strategy ratings improve the control for task difficulty effects in arithmetic problem solving paradigms.

Area of Science:

• Cognitive Neuroscience
• Mathematical Cognition

Background:

• Mental arithmetic is a key paradigm for studying problem-solving in neuroimaging.
• Current neuroimaging studies inconsistently evaluate task complexity, often relying on objective features like number size.
• Subjective strategy ratings offer a potentially more accurate measure of task complexity.

Purpose of the Study:

• To compare the predictive value of self-reported strategies versus objective features for arithmetic task performance.
• To validate the use of subjective strategy ratings in neuroimaging research on mental arithmetic.

Main Methods:

• Utilized a paradigm designed for neuroimaging research involving addition and multiplication tasks.
• Employed Principal Component Analysis (PCA) and multiple regression analyses on reaction times.
• Conducted Receiver Operating Characteristic (ROC) analysis to assess classification accuracy.

Main Results:

• Self-reported strategies significantly outperformed objective features in predicting performance.
• PCA revealed that factor loadings aligned well with self-reported strategy percentages.
• ROC analysis demonstrated that individual strategy ratings provided better classification of task complexity.

Conclusions:

• Participant strategy ratings are superior predictors of arithmetic task complexity compared to objective features.
• Subjective strategy reports should be integrated into neuroimaging research to accurately control for task complexity.
• This approach enhances the reliability and validity of findings in cognitive neuroscience studies of mental arithmetic.