Rutgers University, New Brunswick.
University of California, Berkeley.
Cognition. 2024 Nov;252:105918. doi: 10.1016/j.cognition.2024.105918. Epub 2024 Aug 16.
Despite proportional information being ubiquitous, there is not a standard account of proportional reasoning. Part of the difficulty is that there are several apparent contradictions: in some contexts, proportion is easy and privileged, while in others it is difficult and ignored. One possibility is that although we see similarities across tasks requiring proportional reasoning, people approach them with different strategies. We test this hypothesis by implementing strategies computationally and quantitatively comparing them with Bayesian tools, using data from continuous (e.g., pie chart) and discrete (e.g., dots) stimuli and preschoolers, 2nd and 5th graders, and adults. Overall, people's comparisons of highly regular and continuous proportion are better fit by proportion strategy models, but comparisons of discrete proportion are better fit by a numerator comparison model. These systematic differences in strategies suggest that there is not a single, simple explanation for behavior in terms of success or failure, but rather a variety of possible strategies that may be chosen in different contexts.
尽管比例信息无处不在，但对于比例推理并没有一个标准的解释。部分困难在于存在几个明显的矛盾：在某些情况下，比例是容易且优先的，而在其他情况下则是困难且被忽视的。一种可能性是，尽管我们在需要进行比例推理的任务中看到了相似之处，但人们会采用不同的策略来处理它们。我们通过计算实现这些策略，并使用来自连续（例如，饼图）和离散（例如，点）刺激以及学龄前儿童、二年级和五年级学生以及成年人的数据，将其与贝叶斯工具进行定量比较来检验这一假设。总的来说，人们对高度规则且连续的比例的比较更符合比例策略模型，而对离散比例的比较则更符合分子比较模型。这些策略上的系统差异表明，不能仅用成功或失败来简单地解释行为，而是在不同的情境下可能会选择各种可能的策略。
