感官不确定性的任务优化模型重现了人类的信心判断
Lakshmi Narasimhan Govindarajan1,2,3, Sagarika Alavilli1,2,4, Josh H McDermott1,2,3,4
1Department of Brain and Cognitive Sciences, MIT.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
人类准确地代表了自然环境中的感知不确定性. 我们的研究表明,人类的信心判断与模型生成的不确定性估计一致,验证了感官模两可的内部表示.
科学领域:
- 认知心理学 认知心理学
- 神经科学是一个神经科学.
- 计算建模计算建模
背景情况:
- 感官信息往往含糊不清,导致对外部世界的不确定的解释.
- 估计感知不确定性对于适应性行为至关重要,但在自然主义环境中对这种不确定性的人类表现的明确表现仍然不太了解.
- 缺乏可计算的刺激模型阻碍了量化不确定性表示的进展.
研究的目的:
- 调查人类是否在自然环境中明确表示感知不确定性.
- 为了确定这些人类不确定性表示是否在规范上准确.
- 开发和利用任务优化的计算模型来估计不确定性.
主要方法:
- 开发了任务优化的计算模型,在感知估计上生成概率分布.
- 将人类的信心判断 (作为不确定性的间接措施) 与模型衍生的信心进行比较.
- 利用声音本地化和音调感知任务来评估不确定性表示.
主要成果:
- 人类的信心判断系统地随着试验中的刺激变化而变化.
- 人类的信心在不同的条件下密切跟踪模型衍生的信心.
- 研究结果表明,人类不确定性表示准确地反映了感知估计中的实际不确定性.
结论:
- 人类的信心判断是明确不确定性表示的可靠指标.
- 开发的建模框架为评估感知不确定性的规范性准确性提供了一种方法.
- 这种方法可以扩展到其他各种感知领域,为研究不确定性提供了一个统一的框架.
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