在子中模拟分类和项目记忆机制的会话内部动态
Robert M Nosofsky1, Robert G Cook2, Muhammad A J Qadri3
1Psychological and Brain Sciences, Indiana University, 1101 E. Tenth Street, Bloomington, IN, 47405, USA. nosofsky@indiana.edu.
Psychonomic bulletin & review
|December 7, 2023
概括
子利用类别学习和记忆回忆来解决复杂的任务,正如计算模型所示,这些计算模型可以准确地预测他们在会话中期逆转任务中的行为.
科学领域:
- 认知科学 认知科学
- 动物行为 动物行为
- 计算神经科学是一种神经科学.
背景情况:
- 子表现出先进的认知能力,包括复杂的类别学习和对象记忆.
- 以前的研究表明,子可以在任务中动态地结合基于类别和特定项目的信息,例如中期会话逆转 (MSR).
- 这项MSR任务对于理解动物的暂时组织行为至关重要.
研究的目的:
- 在分类MSR任务中以计算方式建模和调查子行为.
- 为了更深入地了解动物时间和顺序行为背后的理论机制.
- 刺激未来对子认知过程的经验研究.
主要方法:
- 开发和研究各种计算模型来解释子行为数据.
- 将多项处理树分类/内存模型与新型对象/时间编码模型进行比较.
- 将模型配合到已发布的培训中,并从分类MSR任务中转移数据.
主要成果:
- 两个计算模型,一个双过程模型和一个新的对象/时间编码模型,提供高质量的数据匹配.
- 对象/时间编码模型将对象特定的记忆与会话内时间代码相结合.
- 这两种模型都支持 pigeon 决策中的分类和记忆的动态相互作用.
结论:
- 计算建模为管理动物行为的认知机制提供了宝贵的见解.
- 在MSR任务中的行为可以通过集成分类,内存和时间编码的模型来解释.
- 这些发现提升了我们对动物序列行为和认知组织的理解.
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