将元学习模型与认知过程模型相结合。

IF 19.3 1区材料科学 Q1 CHEMISTRY, PHYSICAL

ACS Energy Letters Pub Date : 2024-09-23 DOI:10.1017/S0140525X24000165

Adam N Sanborn, Haijiang Yan, Christian Tsvetkov

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引用次数: 0

摘要

元学习的认知模型能对呈现给参与者的实际刺激做出最佳预测，但通过约束神经网络来研究判断偏差将非常臃肿。我们建议将它们与认知过程模型结合起来，后者更直观，更能解释偏差。理性过程模型可以从元学习模型产生的后验分布中依次采样，这似乎是一个天然的契合点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Combining meta-learned models with process models of cognition.

Meta-learned models of cognition make optimal predictions for the actual stimuli presented to participants, but investigating judgment biases by constraining neural networks will be unwieldy. We suggest combining them with cognitive process models, which are more intuitive and explain biases. Rational process models, those that can sequentially sample from the posterior distributions produced by meta-learned models, seem a natural fit.

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来源期刊

ACS Energy Letters Energy-Renewable Energy, Sustainability and the Environment

CiteScore

31.20

自引率

5.00%

发文量

469

审稿时长

1 months

期刊介绍： ACS Energy Letters is a monthly journal that publishes papers reporting new scientific advances in energy research. The journal focuses on topics that are of interest to scientists working in the fundamental and applied sciences. Rapid publication is a central criterion for acceptance, and the journal is known for its quick publication times, with an average of 4-6 weeks from submission to web publication in As Soon As Publishable format. ACS Energy Letters is ranked as the number one journal in the Web of Science Electrochemistry category. It also ranks within the top 10 journals for Physical Chemistry, Energy & Fuels, and Nanoscience & Nanotechnology. The journal offers several types of articles, including Letters, Energy Express, Perspectives, Reviews, Editorials, Viewpoints and Energy Focus. Additionally, authors have the option to submit videos that summarize or support the information presented in a Perspective or Review article, which can be highlighted on the journal's website. ACS Energy Letters is abstracted and indexed in Chemical Abstracts Service/SciFinder, EBSCO-summon, PubMed, Web of Science, Scopus and Portico.