学习分离精度和反应时间,以便在过渡性推理任务中快速做出决定
Fabian Munoz1,2, Greg Jensen3, Maxwell Shinn4
1Columbia University Medical Center, New York, NY.
Journal of cognitive neuroscience
|December 30, 2025
概括
这项研究表明,漂移扩散模型 (DDM) 可以解释过渡推理 (TI) 任务中的决策. 该模型成功地捕获了子如何学习抽象关系,将DDM应用扩展到认知推理.
科学领域:
- 认知神经科学 认知神经科学
- 计算神经科学是一种神经科学.
- 动物行为 动物行为
背景情况:
- 过渡性推理 (TI) 涉及使用抽象关系的内部表示来进行决策.
- 虽然已知过渡式学习机制,但学习和推理过程中的决策动态需要进一步了解.
- 漂移扩散模型 (DDM) 是一种感知决策的框架.
研究的目的:
- 调查DDM是否可以在IT转移任务中建模决策.
- 分析偏离标准准确度和响应时间 (RT) 模型的快速决策模式.
- 探索DDM对符号推理和串行关系学习的适用性.
主要方法:
- 在一个涉及七张图像列表的TI传输任务中训练了六只子子.
- 记录的决策使用眼动或触及的运动.
- 应用了通用的DDM实现 (PyDDM) 来适应准确性和RT数据.
主要成果:
- 子在200-300次试验中实现了对列表结构的一致学习.
- 行为表现出象征性距离效应,准确度随着顺序项距离的增加而增加.
- 尽管准确度有所提高,但RT在学习过程中保持稳定;DDM与越来越多的证据积累和崩的决策边界相匹配,捕获的RT分布.
- 学习和转移是通过变化的漂移率来建模的,其他DDM参数的变化最小.
- 眼睛和伸手运动表现出类似的动态,非决策时间占RT差异.
结论:
- DDM框架可以成功地考虑过渡性推理任务中的决策动态.
- 该研究确定了一种独特的DDM动态模式,适用于符号推理和串行关系学习.
- 研究结果将DDM的实用性扩展到感知任务以外的复杂的认知过程.
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