VECTOR AVERAGING FOR SMOOTH-PURSUIT EYE-MOVEMENTS INITIATED BY 2 MOVING TARGETS IN MONKEYS

The visual input for pursuit eye movements is represented in the cereb ral cortex as the distributed activity of neurons that are tuned for b oth the direction and speed of target motion. To probe how the motor s ystem uses this distributed code to compute a command for smooth eye m ovements, we have recorded the initiation of pursuit for 150 msec pres entations of two spots moving at different speeds and/or in different directions. With equal probability, one of the two spots continued to move at the same speed and in the same direction and became the tracki ng target, whereas the other disappeared and served as a distractor. W e measured eye acceleration in the interval from 110 to 206 msec after the onset of spot motion, within both the open-loop interval for purs uit and the interval during which eye motion was affected by the two s pots. Our results demonstrate that weighted vector averaging is used t o combine the responses to two moving spots. We found only a minute nu mber of responses that were consistent with either vector summation or winner-take-all computations. In addition, our data show that it is d ifficult for the monkey to defeat vector averaging without extended tr aining on the use of an explicit cue about which spot will become the target. We argue that our experiment reveals the computations done by the pursuit system in the absence of attentional bias and that vector averaging is normally used to read the distributed code of image motio n when there is only one target.