However, owing to a lack of mechanistic knowledge, whether current application protocols are optimized for clinical outcomes is unknown.
It is shown how mechanistic knowledge can be built directly within the connectionist structure that results in a semi-empirical network model.
We compared the strategy complexity of participants with high levels of mechanistic knowledge about the robot against participants with low levels of mechanistic knowledge about the robot.
Our findings suggest that mechanistic knowledge may be equally useful in mathematics.
Specifically, high mechanistic knowledge positively related to the frequency and complexity of mathematical problem-solving strategies.
One perceived issue with phenotypic screening is the lack of mechanistic knowledge.
Instead, mechanistic knowledge acts as a unique and consistent predictor of strategy.
However, even if the assessment did not measure mechanistic knowledge per se, it is still measuring how participants think about the robot, suggesting that readily accessing mechanistic knowledge can influence the use of existing math knowledge.
In general, participants with higher mechanistic knowledge created proportional strategies that were more mathematically complex (and consequently more accurate), while participants with lower mechanistic knowledge relied primarily on estimation or limited proportional strategies.
While we show evidence that mechanistic knowledge relates to math outcomes (in the form of mathematical strategy use), we could not fully explore the relationship between physicality and mechanistic knowledge, as our study only used a physical manipulative.
Our findings show that higher levels of mechanistic knowledge are associated with more frequent and complex mathematical strategy use, suggesting that mechanistic knowledge may be one pathway through which adaptive mathematical strategy use can be improved.
