论文标题
树推理：二进制多项式处理树中的响应时间，参数的代表性和唯一性
Tree Inference: Response Time in a Binary Multinomial Processing Tree, Representation and Uniqueness of Parameters
论文作者
论文摘要
多项式加工树（MPT）是一个有向树，具有与每个弧相关的概率。在这里，我们考虑了与每个弧关联的附加参数，例如选择弧所需的时间。 MPT通常用作任务模型。每个顶点代表一个过程，并且从顶点降下的弧降代表过程的选择。源顶点表示刺激时开始的处理，并且末端顶点表示做出响应。响应分为课堂。实验因素选择性影响顶点，如果改变因子的水平会改变从该顶点降下的弧线和其他弧线上的参数值。较早的工作表明，如果两个实验因素中的每一个选择性地影响任意MPT中的不同顶点，则这些因素与两个相对简单的MPT之一是等效的。这两个应用中的哪一个取决于两个选择性影响的顶点是否由因素排序。一个特殊情况，即用于有序过程的标准二进制树，如果顶点被订购，并且选择性影响第一个顶点的因子仅在该顶点下降的两个弧线上更改参数值。在这里，我们得出了与特定案例有关的特定响应类别相关的概率和衡量标准的必要条件和衡量标准。参数值不是唯一的，我们给出了可接受的转换，以将一组参数值转换为另一个参数值。当进行两个因素的实验时，要估计的观测值和参数的数量取决于每个因素的水平的数量。我们提供自由度。
A Multinomial Processing Tree (MPT) is a directed tree with a probability associated with each arc. Here we consider an additional parameter associated with each arc, a measure such as the time required to select the arc. MPTs are often used as models of tasks. Each vertex represents a process and an arc descending from a vertex represents selection of an outcome of the process. A source vertex represents processing that begins when a stimulus is presented and a terminal vertex represents making a response. Responses are partitioned into classes. An experimental factor selectively influences a vertex if changing the level of the factor changes parameter values on arcs descending from that vertex and on no others. Earlier work shows that if each of two experimental factors selectively influences a different vertex in an arbitrary MPT it is equivalent for the factors to one of two relatively simple MPTs. Which of the two applies depends on whether the two selectively influenced vertices are ordered by the factors or not. A special case, the Standard Binary Tree for Ordered Processes, arises if the vertices are so ordered and the factor selectively influencing the first vertex changes parameter values on only two arcs descending from that vertex. Here we derive necessary and sufficient conditions for the probability and measure associated with a particular response class to be accounted for by this special case. Parameter values are not unique and we give admissible transformations for transforming one set of parameter values to another. When an experiment with two factors is conducted, the number of observations and parameters to be estimated depend on the number of levels of each factor; we provide degrees of freedom.
