ABSTRACT Similar estimation biases appear in a wide range of quantitative judgments, across many tasks and domains. Often, these biases (those that occur, for example, when adults or children indicate remembered locations of objects in bounded spaces) are believed to provide evidence of Bayesian or rational cognitive processing, and are explained in terms of relatively complex Bayesian models (e.g., the Category Adjustment Model). Here, we suggest that some of these phenomena may be accounted for instead within a simpler alternative theoretical framework that has previously been found to explain bias in common numerical estimation tasks across development. We report data from university undergraduate students and 7- through 10-year-olds completing a speeded linear position reproduction task. Bias in both adults’ and children’s responses was effectively explained in terms of a relatively simple psychophysical model of proportion estimation. These data clearly show that the proportion estimation framework is a viable alternative to theories that explain biases as the result of a Bayesian cognitive adjustment process. We also discuss our view that these data are not easily reconciled with the requirements of the more complex Category Adjustment Model that assumes estimates should exhibit a central tendency bias.

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关于认知加工，有偏估计告诉我们什么？作为比例估计的空间判断

摘要 类似的估计偏差出现在广泛的定量判断中，跨越许多任务和领域。通常，这些偏差（例如，当成人或儿童指出在有界空间中记忆的物体位置时发生的偏差）被认为提供了贝叶斯或理性认知处理的证据，并根据相对复杂的贝叶斯模型（例如，品类调整模型）。在这里，我们建议可以在更简单的替代理论框架中解释其中一些现象，该框架先前已被发现可以解释整个开发过程中常见数值估计任务中的偏差。我们报告了完成快速线性位置再现任务的大学本科生和 7 到 10 岁儿童的数据。成人和儿童反应中的偏差可以通过相对简单的比例估计心理物理学模型得到有效解释。这些数据清楚地表明，对于将偏差解释为贝叶斯认知调整过程的结果的理论，比例估计框架是一个可行的替代方案。我们还讨论了我们的观点，即这些数据不容易与更复杂的类别调整模型的要求相协调，该模型假设估计值应表现出集中趋势偏差。