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Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
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信号检测模型作为上下文盗
Thomas N Sherratt1, Erica O'Neill1
1Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, Canada K1S 5B6.
Royal Society open science
|June 23, 2023
概括
决策者学习信号检测的最佳值,通过将其视为一个上下文多武装强盗 (CMAB) 问题来处理. 这种方法与传统的信号检测理论 (SDT) 不同,它更好地解释了人类在不确定性下的学习和决策.
科学领域:
- 认知科学 认知科学
- 决策科学 决策科学 决策科学
- 机器学习 机器学习
背景情况:
- 信号检测理论 (SDT) 传统上假设立即采用最佳决策值.
- 这种假设经常被违反,因为最佳反应经常需要学习.
- 决策中的不确定性需要超越静态值的适应性策略.
研究的目的:
- 在CMAB框架内重新构建经典的信号检测模型.
- 调查决策者如何学习在不确定性下推断暗示-概率关系.
- 为了将CMAB启发式与信号检测任务中的人类行为进行比较.
主要方法:
- 重构正常-正常和功率法信号检测模型作为CMAB.
- 开发和分析用于平衡勘探和开采的CMAB启发式.
- 在连续提示信号检测任务中实证测试CMAB预测与人类志愿者的决定.
主要成果:
- 一个标准的SDT模型未能准确预测人类的行为.
- 来自CMAB的Softmax规则最能解释志愿者的决定,利用预期回报的后勤函数.
- 一个简单的中点算法也在特定条件下显示了预测能力.
结论:
- 在涉及学习时,语境多武器强盗为理解在不确定性下做出决策提供了更现实的框架.
- 在信号检测中,CMAB启发式,特别是软max规则,有效地模拟了人类的学习和适应.
- 这种CMAB方法为经典SDT问题提供了原则性的解决方案,当初始信息不完整时.
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