视觉工作记忆的空间连续扩散模型
Alex Fennell1, Roger Ratcliff1
1The Ohio State University, United States.
Cognitive psychology
|September 2, 2023
概括
本研究将视觉工作记忆 (VWM) 的离散和资源模型与扩散模型集成在一起,以解释响应时间数据. 结果显示,一个统一的模型包含一个猜测组件和依赖项目的记忆强度,最适合实验结果.
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.
- 心理物理学的精神物理.
背景情况:
- 视觉工作记忆 (VWM) 模型传统上专注于准确性,经常忽视响应时间 (RT) 数据.
- 现有的VWM离散和资源模型本身并不能解释内存检索的时间动态.
研究的目的:
- 通过将它们与空间连续扩散模型 (SCDM) 集成来评估离散和资源VWM模型.
- 调查RT数据如何提炼和区分不同的VWM模型.
主要方法:
- 进行了5次VWM实验,展示了2-6个彩色方块,然后在连续的色轮上进行了cued回忆.
- 收集的响应比例和RT数据作为选择角度的函数.
- 在SCDM框架内实施和评估VWM模型,该模型采用连续分布和高斯噪声来建模证据积累.
主要成果:
- 从离散类和资源类中最合适的模型汇聚到一个共同的结构中.
- 这种统一模型包括一个"猜测组件" (建模为零漂移过程),并允许内存强度 (分布高度) 与项目数量不同.
- 整合选择和RT数据使得模型之间的歧视成为可能,这些模型不能仅仅从选择数据中确定.
结论:
- 混合VWM模型包含猜测过程和项目依赖的内存强度,提供了精度和RT数据的卓越计算.
- 该SCDM框架是有效的评估和比较VWM模型使用全面的行为数据.
- 响应时间数据对于完全了解视觉工作记忆动态至关重要.
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