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Countering Disinformation Propaganda: Reverse Engineering the Experimental Implicit Learning Paradigm

机译:反击不忠实宣传:逆向工程实验隐式学习范式

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A conflict may develop when people ascribe different meanings to shared phenomena that entail action. In simple situations, resolution can be by persuading the other actors so that actions converge. In complex situations, explicit persuasion attempts are all too often ineffective. Effective persuasion might entail subtly attempting to alter the ascribed meaning of words and concepts. In modern warfare, adversaries can attempt to exploit the quirks of human psychology to influence, usurp, and manipulate everyday decisions by using disinformation. We introduce a conceptual model of information operations, drawing on the paradigm of implicit learning with Artificial Grammar Learning (AGL). The structure, grammar, and patterns of a Twitter data sample are examined. The modeling approach for designing, understanding, and formalizing the complex competitions of the information era is discussed, as is the possibility of using the model for reverse engineering as a possible disinformation stream. An argument is made for an alternative model for negating the effects of disinformation in the cognitive domain.
机译:当人们将不同的含义归于行动的共享现象时,冲突可能会发生。在简单的情况下,分辨率可以通过说服其他演员,以便采取行动会聚。在复杂的情况下,明确的劝说尝试往往是毫不逊色的。有效的劝说可能需要巧妙地尝试改变单词和概念的归因。在现代战争中,对手可以试图利用人类心理学的怪癖来影响篡夺和操纵日常决定,通过使用缺点。我们介绍了一个信息操作的概念模型,用人工语法学习(AGL)绘制了隐式学习的范式。检查了Twitter数据样本的结构,语法和模式。讨论了设计,理解和形式化信息时代复杂竞争的建模方法,这是利用逆向工程模型作为可能的缺陷流的可能性。对替代模型进行替代模型,用于否定认知域中的虚假效果。

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