Decoding the brain's algorithm for categorization from its neural implementation.
Decoding the brain's algorithm for categorization from its neural implementation.
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DOI：
10.1016/j.cub.2013.08.035
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发表时间：
2013-10-21
期刊：
影响因子：
9.2
通讯作者：
Love, Bradley C.
中科院分区：
文献类型：
--
作者：
Mack, Michael L.;Preston, Alison R.;Love, Bradley C.
认知行为可以在不同的分析层面进行描述：什么行为应该表征该行为，该行为背后的算法和表示是什么，以及这些算法如何在神经活动中物理实现。桥接分析层次的理论通过利用每个层次上存在的约束提供了更完整的解释。尽管理论进步具有巨大潜力，但对认知桥水平分析的研究却很少。例如，类别决策的正式认知模型可以准确预测人类决策，但支持类别决策的模型算法和表示是否与底层神经实现一致仍然未知。这种不确定性很大程度上是由于理论与大脑之间建立联系的障碍造成的。在这里，我们通过使用大脑反应来表征心理计算的本质来解决这个关键问题，该心理计算支持类别决策以评估两种主导且相反的分类模型。我们发现，类别决策过程中的大脑状态与样本而不是原型理论的潜在模型表示更加一致。个人经验的表征，而不是经验的抽象，对于类别决策至关重要。让模型对行为和神经实现负责，为推进更完整的认知算法描述提供了一种方法。
Acts of cognition can be described at different levels of analysis: what behavior should characterize the act, what algorithms and representations underlie the behavior, and how the algorithms are physically realized in neural activity. Theories that bridge levels of analysis offer more complete explanations by leveraging the constraints present at each level. Despite the great potential for theoretical advances, few studies of cognition bridge levels of analysis. For example, formal cognitive models of category decisions accurately predict human decision making, but whether model algorithms and representations supporting category decisions are consistent with underlying neural implementation remains unknown. This uncertainty is largely due to the hurdle of forging links between theory and brain. Here, we tackle this critical problem by using brain response to characterize the nature of mental computations that support category decisions to evaluate two dominant, and opposing, models of categorization. We found that brain states during category decisions were significantly more consistent with latent model representations from exemplar rather than prototype theory. Representations of individual experiences, not the abstraction of experiences, are critical for category decision making. Holding models accountable for behavior and neural implementation provides a means for advancing more complete descriptions of the algorithms of cognition.
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DOI：
10.1073/pnas.0711099105
发表时间：
2008-05-06
影响因子：
11.1
作者：
Hampton, Alan N.;Bossaerts, Peter;O'Doherty, John P.
通讯作者：
O'Doherty, John P.
影响因子：
64.8
作者：
Daw, Nathaniel D.;O'Doherty, John P.;Dayan, Peter;Seymour, Ben;Dolan, Raymond J.
通讯作者：
Dolan, Raymond J.
影响因子：
16.2
作者：
Jiang, Xiong;Bradley, Evan;Riesenhuber, Maximilian
通讯作者：
Riesenhuber, Maximilian
影响因子：
16.2
作者：
Grinband, J;Hirsch, J;Ferrera, VP
通讯作者：
Ferrera, VP
DOI：
10.1016/j.cogbrainres.2004.07.011
发表时间：
2004-12-01
期刊：
COGNITIVE BRAIN RESEARCH
影响因子：
--
作者：
Little, DM;Klein, R;Thulborn, KR
通讯作者：
Thulborn, KR