一个有效的贝叶斯观察者模型的吸引力和排斥时间上下文效应,当感知多稳定点网时
Eline Van Geert1,2,3, Tina Ivancir1,4,5, Johan Wagemans1,6,7
1Laboratory of Experimental Psychology, Department of Brain and Cognition, KU Leuven, Belgium.
Journal of vision
|April 18, 2024
概括
对多稳定点网的感知显示了吸引和排斥的上下文效应. 一个高效的贝叶斯观察者模型通过结合高效的编码和感知先验来解释这些效应.
科学领域:
- 视觉感知 视觉感知 视觉感知
- 计算神经科学是一种计算神经科学.
- 心理物理学的精神物理.
背景情况:
- 多稳定的点网表现出上下文效应,感知受到先前刺激的影响.
- 现有的模型解释了这些效应,但缺乏详细的底层过程.
- 在视觉感知任务中观察到时间上下文效应,吸引力和排斥力.
研究的目的:
- 测试一个有效的贝叶斯观察者模型能否解释同时具有吸引力和排斥性的上下文效应.
- 调查有效编码和感知先验在这些效应中的作用.
- 模拟吸引力和排斥力效应的个体差异之间的相关性.
主要方法:
- 使用高效贝叶斯观察者模型进行的模拟研究.
- 该模型包含基于刺激频率 (高效编码) 的可变编码精度.
- 该模型考虑了刺激和感官空间之间的不相似性,并包括刺激和感知水平.
主要成果:
- 一个高效的贝叶斯观察者模型,具有刺激和感知水平,解释了吸引力和排斥时间上下文效应的同时发生.
- 该模型重现了个体吸引力和排斥力之间的正相关性.
- 在刺激水平上,高效编码和可能性排斥解释了排斥效应,而先前感知吸引解释了吸引力效应.
结论:
- 在刺激水平上,高效的编码和概率排斥解释了多稳定点网中的排斥性上下文效应.
- 感知先前吸引力解释了这些任务中的有吸引力的时间上下文效应.
- 这些发现支持了视觉感知综合模型,该模型考虑了刺激驱动和内部因素.
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