Duch Włodzisław
Department of Informatics, Faculty of Physics, Astronomy and Informatics, and Neurocognitive Laboratory, Center for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland.
Patterns (N Y). 2021 Sep 25;2(11):100353. doi: 10.1016/j.patter.2021.100353. eCollection 2021 Nov 12.
Memetics has so far been developing in social sciences, but to fully understand memetic processes it should be linked to neuroscience models of learning, encoding, and retrieval of memories in the brain. Attractor neural networks show how incoming information is encoded in memory patterns, how it may become distorted, and how chunks of information may form patterns that are activated by many cues, forming the foundation of conspiracy theories. The rapid freezing of high neuroplasticity (RFHN) model is offered as one plausible mechanism of such processes. Illustrations of distorted memory formation based on simulations of competitive learning neural networks are presented as an example. Linking memes to attractors of neurodynamics should help to give memetics solid foundations, show why some information is easily encoded and propagated, and draw attention to the need to analyze neural mechanisms of learning and memory that lead to conspiracies.
到目前为止，模因学一直在社会科学领域发展，但要全面理解模因过程，它应与大脑中学习、编码和记忆检索的神经科学模型相联系。吸引子神经网络展示了传入信息是如何在记忆模式中编码的，它是如何可能被扭曲的，以及信息块是如何形成由许多线索激活的模式的，这构成了阴谋论的基础。高神经可塑性快速冻结（RFHN）模型被认为是此类过程的一种合理机制。作为示例，给出了基于竞争性学习神经网络模拟的扭曲记忆形成的例证。将模因与神经动力学的吸引子联系起来，应有助于为模因学奠定坚实基础，揭示为何某些信息易于编码和传播，并提请人们注意分析导致阴谋的学习和记忆的神经机制的必要性。
