Meta-learning in active inference.

IF 16.6 1区 心理学 Q1 BEHAVIORAL SCIENCES Behavioral and Brain Sciences Pub Date : 2024-09-23 DOI:10.1017/S0140525X24000074
O Penacchio, A Clemente
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引用次数: 0

Abstract

Binz et al. propose meta-learning as a promising avenue for modelling human cognition. They provide an in-depth reflection on the advantages of meta-learning over other computational models of cognition, including a sound discussion on how their proposal can accommodate neuroscientific insights. We argue that active inference presents similar computational advantages while offering greater mechanistic explanatory power and biological plausibility.

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主动推理中的元学习
Binz等人提出元学习(meta-learning)是建立人类认知模型的一条大有可为的途径。他们深入反思了元学习相对于其他认知计算模型的优势,包括对他们的建议如何适应神经科学洞察力的合理讨论。我们认为,主动推理具有类似的计算优势,同时具有更强的机理解释能力和生物学合理性。
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来源期刊
Behavioral and Brain Sciences
Behavioral and Brain Sciences 医学-行为科学
CiteScore
1.40
自引率
1.70%
发文量
353
期刊介绍: Behavioral and Brain Sciences (BBS) is a highly respected journal that employs an innovative approach called Open Peer Commentary. This format allows for the publication of noteworthy and contentious research from various fields including psychology, neuroscience, behavioral biology, and cognitive science. Each article is accompanied by 20-40 commentaries from experts across these disciplines, as well as a response from the author themselves. This unique setup creates a captivating forum for the exchange of ideas, critical analysis, and the integration of research within the behavioral and brain sciences, spanning topics from molecular neurobiology and artificial intelligence to the philosophy of the mind.
期刊最新文献
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