费克纳所不能做的:通过差异缩放来分离感知编码和解码
Joris Vincent1,2, Marianne Maertens1,3, Guillermo Aguilar1,4
1Computational Psychology, Technische Universität, Berlin, Germany.
Journal of vision
|May 9, 2024
概括
感知编码功能不能完全由单独匹配任务数据来确定. 最大概率联合测量有效地从模拟和现实世界的感知数据中恢复真实的编码函数形状.
科学领域:
- 感知科学是一种感知科学.
- 心理物理学的精神物理.
- 认知科学是一种认知科学.
背景情况:
- 了解刺激变化如何与感知量有关,在感知研究中至关重要.
- 感知编码过程通常通过心理物理实验间接推断,而不是直接测量.
- 匹配任务是一种常见的心理物理技术,涉及观察者调整探头刺激以匹配目标的外观.
研究的目的:
- 以分析和计算方式证明匹配任务数据不足以独特确定感知编码函数.
- 评估最大概率联合测量在从匹配数据中恢复真实编码函数的有效性.
- 测量白色效应的感知尺度和匹配数据,并验证估计编码函数的预测能力.
主要方法:
- 从匹配数据中显示编码函数不可识别的分析导出.
- 从已知的编码函数和测试恢复方法生成数据的模拟.
- 将最大概率联合测量 (MLCM) 应用于模拟和实证数据.
- 对感知尺度的实证测量和对白色效应的匹配数据.
主要成果:
- 匹配任务数据不足以限制感知编码函数;无限的函数对可以产生相同的匹配数据.
- 最大概率联合测量 (MLCM) 在从模拟数据中恢复地面真实编码函数的形状方面表现出色.
- 估计编码函数准确预测了对白色效应的匹配数据,并考虑了个体差异.
结论:
- 匹配任务本身只能提供有限的关于底层感知编码过程的信息.
- 最大概率联合测量是一种可靠的方法,用于从心理物理数据中估计感知编码函数.
- 开发的方法成功地模拟了感知缩放和匹配行为,为像怀特效应这样的现象提供了洞察力.
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