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赫布学习在人类感知中的作用:对人类视觉长期增强范式的方法学与理论综述

The role of Hebbian learning in human perception: a methodological and theoretical review of the human Visual Long-Term Potentiation paradigm.

作者信息

Sumner Rachael L, Spriggs Meg J, Muthukumaraswamy Suresh D, Kirk Ian J

机构信息

School of Pharmacy, University of Auckland, New Zealand.

Centre for Psychedelic Research, Division of Brain Sciences, Centre for Psychiatry, Imperial College London, UK.

出版信息

Neurosci Biobehav Rev. 2020 Aug;115:220-237. doi: 10.1016/j.neubiorev.2020.03.013. Epub 2020 Jun 18.

DOI:10.1016/j.neubiorev.2020.03.013
PMID:32562886
Abstract

Long-term potentiation (LTP) is one of the most widely studied forms of neural plasticity, and is thought to be the principle mechanism underlying long-term memory and learning in the brain. Sensory paradigms utilising electroencephalography (EEG) and sensory stimulation to induce LTP have allowed translation from rodent and primate invasive research to non-invasive human investigations. This review focusses on visual sensory LTP induced using repetitive visual stimulation, resulting in changes in the visually evoked response recorded at the scalp with EEG. Across 15 years of use and replication in humans several major paradigm variants for eliciting visual LTP have emerged. The application of different paradigms, and the broad implementation of visual LTP across different populations combines to provide a rich and sensitive account of Hebbian LTP, and potentially non-Hebbian plasticity mechanisms. This review will conclude with a discussion of how these findings have advanced existing theories of perceptual learning by positioning Hebbian learning both alongside and within other major theories such as Predictive Coding and The Free Energy Principle.

摘要

长时程增强(LTP)是研究最为广泛的神经可塑性形式之一,被认为是大脑中长时记忆和学习的主要机制。利用脑电图(EEG)和感觉刺激来诱导LTP的感觉范式,使得研究从啮齿动物和灵长类动物的侵入性研究转化为非侵入性的人体研究。本综述聚焦于使用重复性视觉刺激诱导的视觉感觉LTP,其会导致通过EEG在头皮记录的视觉诱发电位发生变化。在人类中经过15年的应用和重复研究,出现了几种用于诱发视觉LTP的主要范式变体。不同范式的应用以及视觉LTP在不同人群中的广泛实施,共同为赫布型LTP以及潜在的非赫布型可塑性机制提供了丰富且敏感的描述。本综述将通过讨论这些发现如何通过将赫布型学习与预测编码和自由能原理等其他主要理论并置以及纳入其中,推进现有的知觉学习理论来进行总结。

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