A New and Unique Prediction for Cue-Search in a Parallel-Constraint Satisfaction Network Model: The Attraction Search Effect
A New and Unique Prediction for Cue-Search in a Parallel-Constraint Satisfaction Network Model: The Attraction Search Effect
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DOI：
10.1037/rev0000107
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发表时间：
2018-10-01
影响因子：
5.4
通讯作者：
Broder, Arndt
中科院分区：
文献类型：
--
作者：
Jekel, Marc;Gloeckner, Andreas;Broder, Arndt
在许多已建立的决策模型中，一个共同的假设是信息是按照某种预先指定的搜索规则进行搜索的。而信息的内容则影响着搜索的终止。通常被指定为停止规则的搜索方向被视为独立于所检索信息的价位。我们提出了一种扩展的并行约束满足网络模型(iCodes：集成一致性决策和搜索)，与预先指定的搜索规则不同，该模型假设可用信息的价位影响对隐藏信息的搜索。具体地说，该模型预测了吸引力搜索效应，因为在给定可用信息的情况下，信息搜索指向更有吸引力的备选方案。在三项研究中，参与者根据部分揭示的概率信息在两个选项之间进行选择，在信息搜索成本不同的环境中一致地观察到吸引搜索效应，尽管影响的程度随着货币搜索成本的降低而减小。我们还在对5项已发表研究的重新分析中发现了这一效果。通过iCodes，我们提出了一个完全指定的正式模型，并讨论了在竞争建模框架内对理论发展的影响。
A common assumption of many established models for decision making is that information is searched according to some prespecified search rule. While the content of the information influences the termination of search. usually specified as a stopping rule, the direction of search is viewed as being independent of the valence of the retrieved information. We propose an extension to the parallel constraint satisfaction network model (iCodes: integrated coherence-based decision and search), which assumes-in contrast to prespecified search rules-that the valence of available information influences search of concealed information. Specifically, the model predicts an attraction search effect in that information search is directed toward the more attractive alternative given the available information. In 3 studies with participants choosing between two options based on partially revealed probabilistic information, the attraction search effect was consistently observed for environments with varying costs for information search although the magnitude of the effect decreased with decreasing monetary search costs. We also find the effect in reanalyses of 5 published studies. With iCodes, we propose a fully specified formal model and discuss implications for theory development within competing modeling frameworks.