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学习过程中协调多种心理能力。

Coordinating multiple mental faculties during learning.

作者信息

Luo Xiaoliang, Mok Robert M, Roads Brett D, Love Bradley C

机构信息

Department of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP, UK.

MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Rd, Cambridge, CB2 7EF, UK.

出版信息

Sci Rep. 2025 Feb 13;15(1):5319. doi: 10.1038/s41598-025-89732-4.

DOI:10.1038/s41598-025-89732-4
PMID:39939457
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11822098/
Abstract

Complex behavior is supported by the coordination of multiple brain regions. How do brain regions coordinate absent a homunculus? We propose coordination is achieved by a controller-peripheral architecture in which peripherals (e.g., the ventral visual stream) aim to supply needed inputs to their controllers (e.g., the hippocampus and prefrontal cortex) while expending minimal resources. We developed a formal model within this framework to address how multiple brain regions coordinate to support rapid learning from a few example images. The model captured how higher-level activity in the controller shaped lower-level visual representations, affecting their precision and sparsity in a manner that paralleled brain measures. In particular, the peripheral encoded visual information to the extent needed to support the smooth operation of the controller. Alternative models optimized by gradient descent irrespective of architectural constraints could not account for human behavior or brain responses, and, typical of standard deep learning approaches, were unstable trial-by-trial learners. While previous work offered accounts of specific faculties, such as perception, attention, and learning, the controller-peripheral approach is a step toward addressing next generation questions concerning how multiple faculties coordinate.

摘要

复杂行为由多个脑区的协调来支持。在没有小人脑模型的情况下,脑区是如何进行协调的呢?我们提出,协调是通过一种控制器-外周架构实现的,在外周架构中,外周(例如腹侧视觉通路)旨在以最少的资源消耗,为其控制器(例如海马体和前额叶皮层)提供所需的输入。我们在这个框架内开发了一个形式模型,以解决多个脑区如何协调以支持从少量示例图像中快速学习的问题。该模型捕捉了控制器中的高级活动如何塑造低级视觉表征,以一种与大脑测量结果相似的方式影响其精度和稀疏性。特别是,外周将视觉信息编码到支持控制器平稳运行所需的程度。通过梯度下降进行优化而不考虑架构约束的替代模型,无法解释人类行为或大脑反应,并且像标准深度学习方法一样,是不稳定的逐次试验学习者。虽然之前的工作对诸如感知、注意力和学习等特定能力进行了阐述,但控制器-外周方法是朝着解决关于多种能力如何协调的下一代问题迈出的一步。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c8f/11822098/9b5743d119ec/41598_2025_89732_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c8f/11822098/abab0b035dd6/41598_2025_89732_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c8f/11822098/48165d4bf3f0/41598_2025_89732_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c8f/11822098/9d01de079420/41598_2025_89732_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c8f/11822098/9b5743d119ec/41598_2025_89732_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c8f/11822098/abab0b035dd6/41598_2025_89732_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c8f/11822098/48165d4bf3f0/41598_2025_89732_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c8f/11822098/9d01de079420/41598_2025_89732_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c8f/11822098/9b5743d119ec/41598_2025_89732_Fig4_HTML.jpg

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