学习将准确性和反应时间分开,用于在过渡性推理任务中快速做出决策
bioRxiv : the preprint server for biology
|February 24, 2025
概括
这项研究适应了漂移扩散模型 (DDM) 用于子的连续学习. 结果表明"变量崩边界"DDM在学习和转移过程中在过渡性推理任务中表征决策.
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:
- 漂移扩散模型 (DDM) 被广泛用于决策.
- 它应用于具有非典型响应模式的快速串行学习,需要适应.
- 在学习过程中决策中的潜在变量尚未完全理解.
研究的目的:
- 调整漂移扩散模型 (DDM) 适用于串行学习任务.
- 研究潜变量在快速决策中的作用.
- 在一个过渡的推理转移任务中模拟子的行为.
主要方法:
- 使用PyDDM软件将三只的行为数据与它们相匹配.
- 子接受过渡性推断任务的训练,包括学习新图片列表的顺序.
- 斜视眼动被用来表示选择.
主要成果:
- 子在200-300次内学习列表顺序,达到80-90%的准确性.
- 观察到一种象征性距离效应,其准确性根据学习列表中的项目近距离而有所不同.
- 尽管学习和准确性得到了改进,但反应时间仍然不变.
- 一般化漂移-扩散模型通过调整证据积累和崩边界,成功地适应了准确度和反应时间数据.
结论:
- 在学习和转移期间的过渡性推理 (TI) 任务中的决策可以用"变量崩边界"DDM.描述.
- 这表明,在串行学习的DDM框架内,有一个独特的动态模式.
- 这些发现提供了对学习和决策背后的神经机制的见解.
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