A LEARNING-MODEL FOR FORCED-CHOICE DETECTION EXPERIMENTS
A LEARNING-MODEL FOR FORCED-CHOICE DETECTION EXPERIMENTS
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DOI：
10.1111/j.2044-8317.1965.tb00341.x
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发表时间：
1965-01-01
影响因子：
2.6
通讯作者：
KINCHLA, RA
中科院分区：
文献类型：
--
作者：
ATKINSON, RC;KINCHLA, RA
根据一个包含两个不同过程的模型，分析了几个采用强迫选择程序的信号检测实验：感觉过程和决定过程。感觉过程规定了外部信号事件和被试假设的感觉状态之间的关系。决策过程规定了受试者的感觉状态和可观察到的反应之间的关系。感觉过程被认为在整个实验中是固定的，而决定过程被认为是作为先前事件的特定序列的函数而在不同的试验中变化的。假设决策过程中的变化受一个简单的随机学习模型控制。有几种方法可以建立学习模型，这里报道的实验旨在从这些替代方法中进行选择。经验结果支持线性算子过程，其响应概率具有试至试的变化，这不仅是信号和信息事件的函数，也是被激活的感觉状态的特定序列的函数。
Several signal detection experiments employing a forced‐choice procedure are analysed in terms of a model that incorporates two distinct processes: a sensory process and a decision process. The sensory process specifies the relation between external signal events and hypothesized sensory states of the subject. The decision process specifies the relation between the sensory states and the observable responses of the subject. The sensory process is assumed to be fixed throughout an experiment, whereas the decision process is viewed as varying from trial to trial as a function of the particular sequence of preceding events. The changes in the decision process are assumed to be governed by a simple stochastic learning model. There are several ways of formulating the learning model and the experiments reported here were designed to select among these alternative approaches. The empirical results favour a linear‐operator process with trial‐to‐trial changes in response probabilities that are a function not only of the signal and information events, but also of the particular sequence of sensory states activated.