Bridging Levels of Analysis for Probabilistic Models of Cognition
Bridging Levels of Analysis for Probabilistic Models of Cognition
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DOI：
10.1177/0963721412447619
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发表时间：
2012-08-01
影响因子：
7.2
通讯作者：
Sanborn, Adam N.
中科院分区：
文献类型：
--
作者：
Griffiths, Thomas L.;Vul, Edward;Sanborn, Adam N.
认知的概率模型描述了归纳推理背后的抽象计算问题，并确定了它们的理想解。这种方法不同于传统的研究人类认知的方法，后者侧重于识别行为背后的认知或神经过程，因此涉及不同层次的分析。为了评估概率模型的理论含义并提高其预测能力，我们必须了解这些不同分析层次上的理论之间的关系。连接分析层次的一种策略是探索与概率推理有直接联系的认知过程。最近的研究采用这一策略集中在蒙特卡洛原理的可能性，它涉及从概率分布抽样，以执行计算，提供了一种方法来链接概率模型的认知更具体的认知和神经过程。
Probabilistic models of cognition characterize the abstract computational problems underlying inductive inferences and identify their ideal solutions. This approach differs from traditional methods of investigating human cognition, which focus on identifying the cognitive or neural processes that underlie behavior and therefore concern alternative levels of analysis. To evaluate the theoretical implications of probabilistic models and increase their predictive power, we must understand the relationships between theories at these different levels of analysis. One strategy for bridging levels of analysis is to explore cognitive processes that have a direct link to probabilistic inference. Recent research employing this strategy has focused on the possibility that the Monte Carlo principle-which concerns sampling from probability distributions in order to perform computations-provides a way to link probabilistic models of cognition to more concrete cognitive and neural processes.