Dartmouth College.
Stanford University School of Medicine.
J Cogn Neurosci. 2021 Feb;33(2):248-262. doi: 10.1162/jocn_a_01647. Epub 2020 Nov 9.
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.
灵长类动物的视觉特征是对视觉目标进行持续的、顺序的处理和选择，以进行注视。虽然已知预期奖励会影响视觉目标的处理和选择，但这些影响之间的相似之处和差异仍不清楚，主要是因为它们是在单独的任务中测量的。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目标选择和视觉运动敏感性的影响。猴子可以自由选择两个视觉目标，并以不同的概率获得果汁奖励，用于对它们中的任何一个进行眼球运动。目标是漂移光栅的静止孔径，导致这些目标的眼球运动端点被系统地偏向运动方向。我们使用这种运动诱导的偏差作为每次试验视觉运动敏感性的度量。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相似之处和差异。具体来说，我们使用不同的强化学习模型来拟合选择行为，并根据多个试验中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的综合奖励结果之间的相关性。我们发现，除了选择之外，视觉运动敏感性也受到主观奖励值的影响，尽管运动与获得奖励无关。然而，与选择不同的是，视觉运动敏感性不受客观奖励值的影响。此外，选择取决于两个选项的主观奖励值的差异，而运动敏感性则受到值的总和的影响。最后，最能预测视觉处理和选择的模型使用了基于不同类型的奖励整合和时间尺度的估计奖励值的集合。总之，我们的研究结果表明，奖励对视觉处理和选择有可分离的影响，并指出了存在多个大脑回路用于整合奖励结果。
