Many decisions, from crossing a busy street to choosing a profession, require integration of discrete sensory events. Previous studies have shown that integrative decision-making favors more reliable stimuli, mimicking statistically optimal integration. It remains unclear, however, whether reliability biases operate even when they lead to suboptimal performance. To address this issue, we asked human observers to reproduce the average motion direction of two suprathreshold coherent motion signals presented successively and with varying levels of reliability, while simultaneously recording whole-brain activity using electroencephalography. By definition, the averaging task should engender equal weighting of the two component motion signals, but instead we found robust behavioral biases in participants' average decisions that favored the more reliable stimulus. Using population-tuning modeling of brain activity we characterized tuning to the average motion direction. In keeping with the behavioral biases, the neural tuning profiles also exhibited reliability biases. A control experiment revealed that observers were able to reproduce motion directions of low and high reliability with equal precision, suggesting that unbiased integration in this task was not only theoretically optimal but demonstrably possible. Our findings reveal that temporal integration of discrete sensory events in the brain is automatically and suboptimally weighted according to stimulus reliability.

SIGNIFICANCE STATEMENT Many everyday decisions require integration of several sources of information. To safely cross a busy road, for example, one must consider the movement of vehicles with different speeds and trajectories. Previous research has shown that individual stimuli are weighted according to their reliability. Whereas reliability biases typically yield performance that closely mimics statistically optimal integration, it remains unknown whether such biases arise even when they lead to suboptimal performance. Here we combined a novel integrative decision-making task with concurrent brain recording and modeling to address this question. While unbiased decisions were optimal in the task, observers nevertheless exhibited robust reliability biases in both behavior and brain activity, suggesting that reliability-weighted integration is automatic and dissociable from statistically optimal integration.

许多决定，从穿过繁忙的街道到选择职业，都需要整合离散的感官事件。以前的研究表明，综合决策有利于更可靠的刺激，模仿统计上的最佳整合。然而，目前尚不清楚可靠性偏差是否会导致性能欠佳。为了解决这个问题，我们要求人类观察者重现连续呈现的两个超阈值相干运动信号的平均运动方向，并具有不同的可靠性水平，同时使用脑电图记录全脑活动。根据定义，平均任务应该对两个分量运动信号产生相等的权重，但相反，我们发现参与者的行为存在稳健的行为偏差。有利于更可靠刺激的平均决策。使用大脑活动的群体调整模型，我们描述了调整到平均运动方向的特征。为了与行为偏差保持一致，神经调整配置文件也表现出可靠性偏差。一项控制实验表明，观察者能够以相同的精度再现低可靠性和高可靠性的运动方向，这表明该任务中的无偏集成不仅在理论上是最佳的，而且已经证明是可能的。我们的研究结果表明，大脑中离散感觉事件的时间整合会根据刺激可靠性自动且次优地加权。神经调谐配置文件也表现出可靠性偏差。一项控制实验表明，观察者能够以相同的精度再现低可靠性和高可靠性的运动方向，这表明该任务中的无偏集成不仅在理论上是最佳的，而且已经证明是可能的。我们的研究结果表明，大脑中离散感觉事件的时间整合会根据刺激可靠性自动且次优地加权。神经调谐配置文件也表现出可靠性偏差。一项控制实验表明，观察者能够以相同的精度再现低可靠性和高可靠性的运动方向，这表明该任务中的无偏集成不仅在理论上是最佳的，而且已经证明是可能的。我们的研究结果表明，大脑中离散感觉事件的时间整合会根据刺激可靠性自动且次优地加权。

意义声明许多日常决策需要整合多个信息源。例如，要安全地穿过繁忙的道路，必须考虑具有不同速度和轨迹的车辆的运动。先前的研究表明，个人刺激根据其可靠性进行加权。尽管可靠性偏差通常会产生与统计最优集成非常相似的性能，但即使这些偏差导致次优性能，这些偏差是否会出现仍然未知。在这里，我们将一个新颖的综合决策任务与并发大脑记录和建模相结合来解决这个问题。虽然在任务中无偏见的决定是最佳的，但观察者在行为和大脑活动方面表现出强大的可靠性偏差，