Gotta love cognitive modeling. I’ve been looking at the parameter sensitivity on both egocentric and jrd pointing. The point is to fit the set size 4 and then apply it to set size 8 (I ditched 6 in the analysis, so I’ll ignore them again here).
I had a really nice fit (relatively) with common parameter values. RMSE combined (ego/jrd) of 0.9s, which is much smaller than the standard deviation of either ego or jrd pointing responses (error is in abeyance for now).
So I ran some bulk iterations. Damnit. The performance is dependent upon the activation balance between visually attended and spatially updated representations. Well, the random assignment of configurations and trials within a configuration is sufficient to blow that single test out of the water. Bleck. So, I guess I’m going to do this parameter search with bulk runs.
Ha!