Zacks J, Tversky B
Stanford University, California, USA.
Mem Cognit. 1999 Nov;27(6):1073-9. doi: 10.3758/bf03201236.
Interpretations of graphs seem to be rooted in principles of cognitive naturalness and information processing rather than arbitrary correspondences. These predict that people should more readily associate bars with discrete comparisons between data points because bars are discrete entities and facilitate point estimates. They should more readily associate lines with trends because lines connect discrete entities and directly represent slope. The predictions were supported in three experiments--two examining comprehension and one production. The correspondence does not seem to depend on explicit knowledge of rules. Instead, it may reflect the influence of the communicative situation as well as the perceptual properties of graphs.
对图表的解读似乎基于认知自然性和信息处理原则,而非任意的对应关系。这些原则预测,人们应该更容易将柱状图与数据点之间的离散比较联系起来,因为柱状图是离散的实体,便于进行点估计。人们应该更容易将折线图与趋势联系起来,因为折线连接离散的实体并直接表示斜率。这一预测在三个实验中得到了支持——两个实验考察理解,一个实验考察生成。这种对应关系似乎并不依赖于对规则的明确了解。相反,它可能反映了交流情境以及图表感知属性的影响。