Saccade-vergence interactions in macaques. II. Vergence enhancement as the product of a local feedback vergence motor error and a weighted saccadic burst

Saccade-vergence interactions in macaques. II. Vergence enhancement as the product of a local feedback vergence motor error and a weighted saccadic burst
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DOI:
10.1152/jn.01337.2004
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发表时间:
2005-10-01
影响因子:
2.5
通讯作者:
Mays, LE
Mays, LE
中科院分区:
医学3区
文献类型:
--
作者:
Busettini, C;Mays, LE

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In the accompanying paper we reported that intrasaccadic vergence enhancement during combined saccade vergence eye movements reflects saccadic dynamics, which implies the involvement of saccadic burst signals. This involvement was not predicted by the Multiply Model of Zee et al. We propose a model wherein vergence enhancement is the result of a multiplicative interaction between a weighted saccadic burst signal and a nonvisual short- latency estimate of the vergence motor error at the time of the saccade. The enhancement of vergence velocity by saccades causes the vergence goal to be approached more rapidly than if no saccade had occurred. The adjustment of the postsaccadic vergence velocity to this faster reduction in vergence motor error occurred with a time course too fast for visual feedback. This implies the presence of an internal estimate of the progress of the movement and indicates that vergence responses are under the control of a local feedback mechanism. It also implies that the vergence enhancement signal is included in the vergence feedback loop and is an integral part of the vergence velocity command. Our multiplicative model is able to predict the peak velocity of the vergence enhancement as a function of cyclopean saccadic dynamics, smooth vergence dynamics, and saccade - vergence timing with remarkable precision. It performed equally well for both horizontal and vertical saccades with very similar parameters, suggesting a common mechanism for all saccadic directions. A saccade - vergence additive model is also presented, although it would require external switching elements. Possible neural implementations are discussed.