School of Management, Yale University, 135 Prospect Street, New Haven, CT 06511, USA.
Psychon Bull Rev. 2012 Aug;19(4):601-7. doi: 10.3758/s13423-012-0247-5.
Perhaps the most common method of depicting data, in both scientific communication and popular media, is the bar graph. Bar graphs often depict measures of central tendency, but they do so asymmetrically: A mean, for example, is depicted not by a point, but by the edge of a bar that originates from a single axis. Here we show that this graphical asymmetry gives rise to a corresponding cognitive asymmetry. When viewers are shown a bar depicting a mean value and are then asked to judge the likelihood of a particular data point being part of its underlying distribution, viewers judge points that fall within the bar as being more likely than points equidistant from the mean, but outside the bar--as if the bar somehow "contained" the relevant data. This "within-the-bar bias" occurred (a) for graphs with and without error bars, (b) for bars that originated from both lower and upper axes, (c) for test points with equally extreme numeric labels, (d) both from memory (when the bar was no longer visible) and in online perception (while the bar was visible during the judgment), (e) both within and between subjects, and (f) in populations including college students, adults from the broader community, and online samples. We posit that this bias may arise due to principles of object perception, and we show how it has downstream implications for decision making.
也许最常见的表示数据的方法，无论是在科学交流还是大众媒体中，都是柱状图。柱状图通常描绘集中趋势的度量，但它们的表示方式是不对称的：例如，平均值不是通过一个点，而是通过从单个轴开始的条形的边缘来表示。在这里，我们表明这种图形不对称性产生了相应的认知不对称性。当向观看者展示一个表示平均值的条形图，然后要求他们判断特定数据点是否属于其基础分布的可能性时，观看者会判断落在条形内的点比离平均值等距但在条形外的点更有可能——就好像条形以某种方式“包含”了相关数据。这种“条形内偏差”出现在以下情况下：(a) 带有和不带有误差条的图表；(b) 起源于上下轴的条形；(c) 具有相同极端数字标签的测试点；(d) 无论是从记忆中（当条形不再可见时）还是在线感知中（当条形在判断期间可见时）；(e) 无论是在内部还是在个体之间；(f) 在包括大学生、更广泛社区的成年人和在线样本在内的人群中。我们假设这种偏差可能是由于对象感知的原则引起的，我们展示了它如何对决策产生下游影响。
