Chiba University, Chiba, Japan.
Mem Cognit. 2011 Jul;39(5):851-63. doi: 10.3758/s13421-010-0057-9.
In research on the recognition heuristic (Goldstein & Gigerenzer, Psychological Review, 109, 75-90, 2002), knowledge of recognized objects has been categorized as "recognized" or "unrecognized" without regard to the degree of familiarity of the recognized object. In the present article, we propose a new inference model--familiarity-based inference. We hypothesize that when subjective knowledge levels (familiarity) of recognized objects differ, the degree of familiarity of recognized objects will influence inferences. Specifically, people are predicted to infer that the more familiar object in a pair of two objects has a higher criterion value on the to-be-judged dimension. In two experiments, using a binary choice task, we examined inferences about populations in a pair of two cities. Results support predictions of familiarity-based inference. Participants inferred that the more familiar city in a pair was more populous. Statistical modeling showed that individual differences in familiarity-based inference lie in the sensitivity to differences in familiarity. In addition, we found that familiarity-based inference can be generally regarded as an ecologically rational inference. Furthermore, when cue knowledge about the inference criterion was available, participants made inferences based on the cue knowledge about population instead of familiarity. Implications of the role of familiarity in psychological processes are discussed.
在识别启发式的研究中（Goldstein & Gigerenzer，《心理评论》，109，75-90，2002），被识别物体的知识被归类为“被识别”或“未被识别”，而不考虑被识别物体的熟悉程度。在本文中，我们提出了一个新的推理模型——基于熟悉度的推理。我们假设，当被识别物体的主观知识水平（熟悉度）不同时，被识别物体的熟悉度会影响推理。具体来说，人们会推断出在一对两个物体中更熟悉的那个物体在被判断维度上的标准值更高。在两个实验中，我们使用二择一任务，检验了一对两个城市中关于群体的推理。结果支持基于熟悉度的推理预测。参与者推断出在一对中更熟悉的城市人口更多。统计建模表明，基于熟悉度的推理的个体差异在于对熟悉度差异的敏感性。此外，我们发现基于熟悉度的推理可以被普遍视为一种生态理性推理。此外，当关于推理标准的线索知识可用时，参与者会根据关于人口的线索知识进行推理，而不是基于熟悉度。讨论了熟悉度在心理过程中的作用。
