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首页> 美国卫生研究院文献>Proceedings of the National Academy of Sciences of the United States of America >From the CoverPNAS Plus: Simultaneous modeling of visual saliency and value computation improves predictions of economic choice
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From the CoverPNAS Plus: Simultaneous modeling of visual saliency and value computation improves predictions of economic choice

机译：从CoverPNAS Plus：视觉显着性和价值计算的同时建模可改善经济选择的预测

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Many decisions we make require visually identifying and evaluating numerous alternatives quickly. These usually vary in reward, or value, and in low-level visual properties, such as saliency. Both saliency and value influence the final decision. In particular, saliency affects fixation locations and durations, which are predictive of choices. However, it is unknown how saliency propagates to the final decision. Moreover, the relative influence of saliency and value is unclear. Here we address these questions with an integrated model that combines a perceptual decision process about where and when to look with an economic decision process about what to choose. The perceptual decision process is modeled as a drift–diffusion model (DDM) process for each alternative. Using psychophysical data from a multiple-alternative, forced-choice task, in which subjects have to pick one food item from a crowded display via eye movements, we test four models where each DDM process is driven by (i) saliency or (ii) value alone or (iii) an additive or (iv) a multiplicative combination of both. We find that models including both saliency and value weighted in a one-third to two-thirds ratio (saliency-to-value) significantly outperform models based on either quantity alone. These eye fixation patterns modulate an economic decision process, also described as a DDM process driven by value. Our combined model quantitatively explains fixation patterns and choices with similar or better accuracy than previous models, suggesting that visual saliency has a smaller, but significant, influence than value and that saliency affects choices indirectly through perceptual decisions that modulate economic decisions.
机译：我们做出的许多决定都需要在视觉上快速识别和评估多种选择。这些通常在奖励或价值以及低级的视觉属性（例如显着性）方面有所不同。显着性和价值都会影响最终决定。特别是，显着性会影响注视位置和持续时间，这是对选择的预测。但是，显着性如何传播到最终决策尚不清楚。此外，显着性和价值的相对影响尚不清楚。在这里，我们使用集成模型解决这些问题，该模型将关于何时何地查看的感知决策过程与关于选择什么的经济决策过程相结合。对于每个替代方案，将感知决策过程建模为漂移扩散模型（DDM）过程。使用来自多项替代性强迫选择任务的心理物理数据（受试者必须通过眼球运动从拥挤的显示器中挑选一种食物），我们测试了四个模型，其中每个DDM过程均由（i）显着性或（ii）驱动单价或（iii）累加或（iv）两者的乘积组合。我们发现，包括显着性和价值加权（以显着性与价值之比）为三分之一/三分之二的模型，其性能明显优于仅基于任一数量的模型。这些注视模式可调节经济决策过程，也称为价值驱动的DDM过程。我们的组合模型定量地解释了注视方式和选择，其准确性与以前的模型相似或更高，这表明视觉显着度的影响要小于价值，但意义重大，显着度会通过调节经济决策的感知决策间接影响选择。

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