Primate vision is characterized by constant, sequential processing and selection of visual targets to fixate. Although expected reward is known to influence both processing and selection of visual targets, similarities and differences between these effects remain unclear mainly because they have been measured in separate tasks. Using a novel paradigm, we simultaneously measured the effects of reward outcomes and expected reward on target selection and sensitivity to visual motion in monkeys. Monkeys freely chose between two visual targets and received a juice reward with varying probability for eye movements made to either of them. Targets were stationary apertures of drifting gratings, causing the end points of eye movements to these targets to be systematically biased in the direction of motion. We used this motion-induced bias as a measure of sensitivity to visual motion on each trial. We then performed different analyses to explore effects of objective and subjective reward values on choice and sensitivity to visual motion to find similarities and differences between reward effects on these two processes. Specifically, we used different reinforcement learning models to fit choice behavior and estimate subjective reward values based on the integration of reward outcomes over multiple trials. Moreover, to compare the effects of subjective reward value on choice and sensitivity to motion directly, we considered correlations between each of these variables and integrated reward outcomes on a wide range of timescales. We found that, in addition to choice, sensitivity to visual motion was also influenced by subjective reward value, although the motion was irrelevant for receiving reward. Unlike choice, however, sensitivity to visual motion was not affected by objective measures of reward value. Moreover, choice was determined by the difference in subjective reward values of the two options, whereas sensitivity to motion was influenced by the sum of values. Finally, models that best predicted visual processing and choice used sets of estimated reward values based on different types of reward integration and timescales. Together, our results demonstrate separable influences of reward on visual processing and choice and point to the presence of multiple brain circuits for the integration of reward outcomes.

灵长类视觉的特点是持续、顺序处理和选择要注视的视觉目标。尽管已知预期奖励会影响视觉目标的处理和选择，但这些影响之间的异同仍不清楚，主要是因为它们已在不同的任务中进行测量。使用一种新的范式，我们同时测量了奖励结果和预期奖励对猴子的目标选择和视觉运动敏感性的影响。猴子在两个视觉目标之间自由选择，并以不同概率对其中任何一个进行眼球运动获得果汁奖励。目标是漂移光栅的固定孔径，导致这些目标的眼球运动终点系统地偏向运动方向。我们使用这种运动引起的偏差来衡量每次试验中对视觉运动的敏感性。然后，我们进行了不同的分析，以探索客观和主观奖励值对选择和视觉运动敏感性的影响，以发现奖励对这两个过程的影响之间的异同。具体来说，我们使用不同的强化学习模型来拟合选择行为，并基于多次试验的奖励结果的整合来估计主观奖励值。此外，为了直接比较主观奖励值对选择和运动敏感性的影响，我们考虑了这些变量中的每一个与广泛时间尺度上综合奖励结果之间的相关性。我们发现，除了选择之外，对视觉运动的敏感性还受到主观奖励价值的影响，尽管该动议与获得奖励无关。然而，与选择不同的是，对视觉运动的敏感性不受奖励价值的客观衡量标准的影响。此外，选择是由两个选项的主观奖励值的差异决定的，而对运动的敏感性受值总和的影响。最后，最佳预测视觉处理和选择的模型使用了基于不同类型的奖励集成和时间尺度的估计奖励值集。总之，我们的结果证明了奖励对视觉处理和选择的可分离影响，并指出存在多个用于整合奖励结果的大脑回路。选择是由两个选项的主观奖励值的差异决定的，而对运动的敏感性受值总和的影响。最后，最佳预测视觉处理和选择的模型使用了基于不同类型的奖励集成和时间尺度的估计奖励值集。总之，我们的结果证明了奖励对视觉处理和选择的可分离影响，并指出存在多个用于整合奖励结果的大脑回路。选择是由两个选项的主观奖励值的差异决定的，而对运动的敏感性受值总和的影响。最后，最佳预测视觉处理和选择的模型使用了基于不同类型的奖励集成和时间尺度的估计奖励值集。总之，我们的结果证明了奖励对视觉处理和选择的可分离影响，并指出存在多个用于整合奖励结果的大脑回路。