发表机构
The Pennsylvania State University（宾夕法尼亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对社会文化结构对决策的表征问题，在ACT-R认知架构中提出带向量符号自编码器的陈述性记忆系统，结合HRR编码，通过IAT测试验证其有效性。
AI 中文摘要
如何在计算认知模型中更好地表征社会文化结构对决策的影响？建模该影响需要遍历多个语义表征层级，但建模者并不清楚哪些层级对特定情境最为显著。尽管大语言模型和基于认知的语料库模型可通过共现表征广泛的语义关联，但需考虑记忆中自我表征的作用，以确定文化关联如何塑造决策。我们提出一种用于ACT-R认知架构的陈述性记忆系统，该系统通过向量符号自编码器在多个层级表征语义关联。我们采用简单的HRR操作将情景记忆与从文本提取的语义记忆向量进行不同编码，从而生成记忆请求的最终块激活。我们使用针对种族语境内隐联想测验（IAT）的ACT-R认知模型来测试该新陈述性记忆系统。
英文摘要
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.
