Can Invalid Information Be Ignored When It Is Detected?
通过815名美国成年人的在线实验，研究发现人们即使检测到数值序列中的异常值，仍会受其影响而产生估计偏差，视觉警告和不同任务场景也无法完全消除这种偏差。
= 815 adults, recruited through Amazon Mechanical Turk in the United States), we investigated whether people could ignore quantitative information when they judged for themselves that it was misreported. Participants recruited online viewed sets of values sampled from Gaussian distributions to estimate the underlying means. They attempted to ignore invalid information, which were outlier values inserted into the value sequences. Results indicated participants were able to detect outliers. Nevertheless, participants' estimates were still biased in the direction of the outlier, even when they were most certain that they detected invalid information. The addition of visual warning cues and different task scenarios did not fully eliminate systematic over- and underestimation. These findings suggest that individuals may incorporate invalid information they meant to ignore when forming beliefs.