inverse-pruning is like cloud only you can't sample any points in the same thetaGrp (i also tried excluding pts where thetas is within 45 deg of current theta). turns out this hypothesis does as good or better than normal pruning. and in fact, inverse-pruning's mean errors by kinematics resemble cloud more closely than they do pruning. this suggests the reason cloud is good has nothing to do with sensory conditions (thetas). moreover, it suggests that pruning is not an improvement over habitual due to it "taking the best of habitual and cloud".
inverse-pruning is like cloud only you can't sample any points in the same thetaGrp (i also tried excluding pts where thetas is within 45 deg of current theta). turns out this hypothesis does as good or better than normal pruning. and in fact, inverse-pruning's mean errors by kinematics resemble cloud more closely than they do pruning. this suggests the reason cloud is good has nothing to do with sensory conditions (thetas). moreover, it suggests that pruning is not an improvement over habitual due to it "taking the best of habitual and cloud".