实例理论在没有分类结构的情况下预测分类决策:对没有语法的人工语法学习的计算分析
1Booth University College, 447 Webb Place, Winnipeg, MB, R3B 2P2, Canada. evan.curtis@boothuc.ca.
Memory & cognition
|August 11, 2023
概括
这项研究表明,即使在人工语法学习 (AGL) 中,人类的分类也依赖于认知过程,而不仅仅是刺激特征. 一个基于实例的内存模型准确地预测了没有先前存在的结构的AGL决策.
科学领域:
- 认知心理学 认知心理学
- 计算神经科学是一种神经科学.
背景情况:
- 传统的分类理论强调刺激特征.
- 人工语法学习 (AGL) 研究经常争论基于规则的与基于片段的知识.
- 刺激驱动的焦点可能会忽视决策的潜在认知机制.
研究的目的:
- 调查AGL中对分类决策的机械,过程驱动的解释.
- 在一个没有结构的AGL任务中测试基于实例的内存模型的预测能力.
- 倡导在分类理论中转向过程级建模.
主要方法:
- 使用一个伪AGL任务随机生成字符串来消除实验者强加的结构.
- 从基于实例的人类记忆模型中得出的预测.
- 对人类参与者进行了一项标准的AGL实验,使用相同的随机生成字符串.
主要成果:
- 基于实例的模型的预测与人类参与者的分类决定密切匹配.
- 即使在刺激缺乏固有的结构的情况下,模型的表现仍然很强.
- 这些发现不管测试刺激中的偶然结构如何,都是正确的.
结论:
- 包括在AGL中的分类决策更好地通过认知过程而不是仅仅通过刺激特征来解释.
- 基于实例的内存模型为理解分类机制提供了一个可行的框架.
- 未来的分类研究应该优先考虑对认知过程的建模,而不是识别刺激敏感性.
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