在分类中解开行为变化的感知和过程相关来源
Florian I Seitz1, Jana B Jarecki1, Jörg Rieskamp1
1Center for Economic Psychology, Department of Psychology, University of Basel.
概括
人类的分类因内部噪音而变化. 连续的模型预测,而不仅仅是类别响应,可以确定噪音是否影响感知,注意力或相似性计算,指导有针对性的培训.
科学领域:
- 认知心理学 认知心理学
- 计算神经科学是一种计算神经科学.
- 人类的感知人类的感知.
背景情况:
- 人类的分类表现出个体内部的变化,使其难以区分偏见.
- 变性可能源于分类过程的不同阶段,包括感知和认知.
- 现有的方法很难分离出这种行为变化的精确来源.
研究的目的:
- 在人类分类中调查和区分行为变化的来源.
- 探索噪音在感知和认知过程中对分类准确性的影响.
- 开发一种方法来识别分类中的特定步骤,其中变异性起源.
主要方法:
- 基于相似性的分类模型被模拟以分析行为变异性.
- 该研究研究了不同处理阶段 (感知,注意,计算) 的噪音如何影响模型预测.
- 连续模型预测与离散类别响应进行了比较,以确定噪声源.
主要成果:
- 感知或认知过程中的噪音导致了对反应决定性的重大误判.
- 不同类别的响应不足以确定变异的来源,因为不同类型的噪音产生了类似的模式.
- 连续模型预测成功地区分了噪音源:噪音特征感知,注意力和相似性计算产生了不同的预测模式.
结论:
- 对类别信念的持续测量,就像概率判断一样,对于解开行为变化的来源至关重要.
- 确定噪声的特定来源 (感知,注意或计算) 可以告知有针对性的干预措施,以提高人类分类技能.
- 这项研究为理解和解决人类决策过程中的变化提供了一个框架.
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