A model for learning based on the joint estimation of stochasticity and volatility.
A model for learning based on the joint estimation of stochasticity and volatility.
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DOI：
10.1038/s41467-021-26731-9
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发表时间：
2021-11-15
影响因子：
16.6
通讯作者：
Daw ND
中科院分区：
文献类型：
--
作者：
Piray P;Daw ND
以前的研究强调了不确定性对控制学习速度的重要性，以及这种控制如何依赖于学习者推断环境的噪声特性，特别是波动性：变化的速度。然而，学习速度是由波动性和第二个因素，即时刻到时刻的随机性之间的比较共同决定的。然而，以前的许多研究都集中在仅与任何一个因素的估计相对应的简化案例上。在这里，我们引入一个学习模型，在该模型中，这两个因素都是从经验中同时学习的，并使用该模型来模拟许多看似不同的神经科学和行为现象中的人类和动物数据。通过考虑联合估计的完整问题，我们强调了一组以前未被认识到的问题，这些问题源于关于波动性和随机性的推断的相互依存。这种相互依赖使先前的结果变得复杂并丰富了解释，例如患有焦虑和杏仁核损伤的个体的病理性学习。人类的学习依赖于两个噪声因素的相反影响：波动性和随机性。在这里，作者提出了一个学习模型，该模型展示了如何以及为什么联合评估这些因素对于理解健康和病理性学习是重要的。
Previous research has stressed the importance of uncertainty for controlling the speed of learning, and how such control depends on the learner inferring the noise properties of the environment, especially volatility: the speed of change. However, learning rates are jointly determined by the comparison between volatility and a second factor, moment-to-moment stochasticity. Yet much previous research has focused on simplified cases corresponding to estimation of either factor alone. Here, we introduce a learning model, in which both factors are learned simultaneously from experience, and use the model to simulate human and animal data across many seemingly disparate neuroscientific and behavioral phenomena. By considering the full problem of joint estimation, we highlight a set of previously unappreciated issues, arising from the mutual interdependence of inference about volatility and stochasticity. This interdependence complicates and enriches the interpretation of previous results, such as pathological learning in individuals with anxiety and following amygdala damage. Human learning depends on opposing effects of two noise factors: volatility and stochasticity. Here the authors present a model of learning that shows how and why joint estimation of these factors is important for understanding healthy and pathological learning.
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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：
16.6
作者：
de Berker AO;Rutledge RB;Mathys C;Marshall L;Cross GF;Dolan RJ;Bestmann S
通讯作者：
Bestmann S
DOI：
10.1016/j.bpsc.2017.04.005
发表时间：
2017-11-01
影响因子：
5.9
作者：
Brazil, Inti A.;Mathys, Christoph D.;Cohn, Moran D.
通讯作者：
Cohn, Moran D.
影响因子：
19.9
作者：
Courville, Aaron C.;Daw, Nathaniel D.;Touretzky, David S.
通讯作者：
Touretzky, David S.
影响因子：
1.5
作者：
Dayan, P;Yu, AJ
通讯作者：
Yu, AJ