An integrative effort: Bridging motivational intensity theory and recent neurocomputational and neuronal models of effort and control allocation.
An integrative effort: Bridging motivational intensity theory and recent neurocomputational and neuronal models of effort and control allocation.
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综合努力：将动机强度理论与最新的努力和控制分配的神经计算和神经元模型联系起来。
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发表时间：
2021
期刊：
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通讯作者：
E. Vassena
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作者：
N. Silvestrini;Sebastian Musslick;A. Berry;E. Vassena
在过去的十年里，越来越多的认知、神经生物学和计算模型被提出，试图解释人类如何分配身体或认知努力。大多数模型与动机强度理论（MIT）有着概念上的相似之处，MIT是一个有影响力的经典动机心理学理论。然而，很少有人努力整合这些模型，这些模型仍然局限于它们被开发的解释水平，即心理学，计算，神经生物学和神经元。在这篇评论中，我们得到了新的分析最近的三个计算和神经元模型的努力分配控制理论的期望值，强化元学习者（RML）模型，和神经元模型的注意力的努力，并建立这些模型和MIT之间的正式关系。我们的分析揭示了这些模型所做的预测之间惊人的相似之处，有一个共同的关键原则：感知任务难度和努力之间的非单调关系，遵循一个或倒U形。此外，该模型收敛的命题，背侧前扣带皮层可能负责确定分配的努力和认知控制。最后，我们讨论了不同的贡献和优势，每个理论对理解神经计算过程的努力分配。最后，我们通过在所提出的模型所描述的自适应努力分配的不同理论之间建立新的联系，强调了统一理解努力分配的必要性。（PsycInfo数据库记录（c）2022阿帕，保留所有权利）。
An increasing number of cognitive, neurobiological, and computational models have been proposed in the last decade, seeking to explain how humans allocate physical or cognitive effort. Most models share conceptual similarities with motivational intensity theory (MIT), an influential classic psychological theory of motivation. Yet, little effort has been made to integrate such models, which remain confined within the explanatory level for which they were developed, that is, psychological, computational, neurobiological, and neuronal. In this critical review, we derive novel analyses of three recent computational and neuronal models of effort allocation-the expected value of control theory, the reinforcement meta-learner (RML) model, and the neuronal model of attentional effort-and establish a formal relationship between these models and MIT. Our analyses reveal striking similarities between predictions made by these models, with a shared key tenet: a nonmonotonic relationship between perceived task difficulty and effort, following a sawtooth or inverted U shape. In addition, the models converge on the proposition that the dorsal anterior cingulate cortex may be responsible for determining the allocation of effort and cognitive control. We conclude by discussing the distinct contributions and strengths of each theory toward understanding neurocomputational processes of effort allocation. Finally, we highlight the necessity for a unified understanding of effort allocation, by drawing novel connections between different theorizing of adaptive effort allocation as described by the presented models. (PsycInfo Database Record (c) 2022 APA, all rights reserved).