I have a cognitive architecture solving a set of tasks. Also I have data of human subjects solving the same set of tasks.
Now I want to see whether I can find a relationship between the performance of the architecture and that of human subjects (e.g: if its hard for the architecture its also hard for human subjects).
I have 3 summary statistics describing the performance of the architecture, call them a,b,c. They all measure slightly different things. Think about: uncertainty, structure of the solution given etc. For the human data I only have access to one statistic (data taken from a published experiment ), which is related but not identical to a,b,c.
If I compute the correlation coefficient, I see that b shows a high correlation to the performance of human subjects. However a,b,c have correlation (~0.2-0.7) in themselves.
I read that one can compute the partial correlation coefficient which measures the relationship of two variables excluding the influence of the other. If I do this, c has the highest partial correlation to human data, although its correlation to the human data was the lowest
I have trouble interpreting these results, and I am not sure whether I should do my interpretation based on correlation or partial correlation and if the use of partial correlation makes sense in this context. Also note: this is purely explorative, I don't aim to do hypothesis testing, I merely search for relationships.