Representing the task in Bayesian reasoning: comment on Lovett and Schunn (1999).
Representing the task in Bayesian reasoning: comment on Lovett and Schunn (1999).
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用贝叶斯推理表示任务：对 Lovett 和 Schunn (1999) 的评论。
DOI：
10.1037//0096-3445.129.4.449
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
Fantino,E
中科院分区：
文献类型：
--
作者：
Goodie,AS;Fantino,E
RCCL模型（MC Lovett和CD Schunn，1999;参见记录1999-05245-001）产生的预测不是新颖的，或者不是真正从其原理中产生的。然而，它提供了有价值的见解，学习过程可能会影响选择的代表性和这些代表性的策略，并指出了可能的理论进展的方式内隐和外显控制。作者的帐户的基础率忽视直接经验与RCCL相比，它的结论是，基于学习的模型允许测试，而不是由基于代表性的模型培养。（PsycInfo数据库记录（c）2016阿帕，保留所有权利）
The RCCL model (MC Lovett and CD Schunn, 1999; see record 1999-05245-001) produces predictions that are non-novel or that do not truly spring from its principles. However, it offers the valuable insight that learning processes may affect the selection of both representations and strategies within those representations, and points the way to possible theoretical progress on implicit and explicit control. The authors' account of base-rate neglect under direct experience is compared with RCCL, and it is concluded that learning-based models allow for tests that are not fostered by representation-based models.(PsycINFO Database Record (c) 2016 APA, all rights reserved)