用向量符号编码多层级语义，模拟文化背景对决策影响
Learning a Vector-Symbolic Model for Socio-Cultural Tasks
- 通过向量符号自动编码器构建多层级语义表征
- 在ACT-R模型中实现记忆请求的片段激活，准确模拟隐性关联测试
- 适用于研究文化因素如何塑造认知决策的学者
如何更好地在计算认知模型中表示社会文化结构对决策的影响？该影响涉及多层次语义表征，但模型者难以判断哪些层次最相关。尽管大语言模型和基于认知的语料库模型可通过共现关系表示广泛的语义关联，仍需考虑自我表征在记忆中的作用，以确定文化关联如何影响决策。本文提出一种用于ACT-R认知架构的命题记忆系统，通过向量符号自动编码器在多个层次上表示语义关联。利用简单的高阶重叠表示（HRR）操作，将情景记忆与从文本中提取的语义记忆向量区分开，生成针对记忆请求的最终片段激活。我们使用种族情境化的内隐联想测试（IAT）的ACT-R认知模型来验证这一新命题记忆系统。
原文摘要 · Abstract (English)
How can we better represent the impact of sociocultural structures on decision making in computational cognitive models? Modeling this impact requires traversing multiple levels of semantic representation, however it is not immediately clear to a modeler which levels of representation are most salient to a given situation. Though large language models and cognitively grounded corpus models can represent broad semantic associations through co-occurences, the role of self representations in memory should be accounted for to determine how cultural associations shape decision making. We propose a declarative memory system to be used in the ACT-R cognitive architecture that represents semantic associations at multiple levels via a vector-symbolic autoencoder. We use a simple HRR operation to encode episodic memories differently from semantic memory vectors extracted from text to produce a final chunk activation for a memory request. We use ACT-R cognitive models of a racially contextualized implicit association test (IAT) to test this new declarative memory system.
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