Indiana University, Bloomington, IN, USA.
Psychon Bull Rev. 2011 Aug;18(4):659-81. doi: 10.3758/s13423-011-0106-9.
Increasing the number of available sources of information may impair or facilitate performance, depending on the capacity of the processing system. Tests performed on response time distributions are proving to be useful tools in determining the workload capacity (as well as other properties) of cognitive systems. In this article, we develop a framework and relevant mathematical formulae that represent different capacity assays (Miller's race model bound, Grice's bound, and Townsend's capacity coefficient) in the same space. The new space allows a direct comparison between the distinct bounds and the capacity coefficient values and helps explicate the relationships among the different measures. An analogous common space is proposed for the AND paradigm, relating the capacity index to the Colonius-Vorberg bounds. We illustrate the effectiveness of the unified spaces by presenting data from two simulated models (standard parallel, coactive) and a prototypical visual detection experiment. A conversion table for the unified spaces is provided.
增加信息的来源数量可能会对表现产生不利或有利的影响，这取决于处理系统的容量。对响应时间分布进行的测试被证明是确定认知系统工作负载容量（以及其他属性）的有用工具。在本文中，我们开发了一个框架和相关的数学公式，在同一个空间中表示不同的容量测定（米勒的种族模型限制、格赖斯的限制和汤森的容量系数）。新的空间允许对不同的限制和容量系数值进行直接比较，并有助于阐明不同度量之间的关系。还为 AND 范式提出了一个类似的通用空间，将容量指数与科洛尼乌斯-沃尔伯格限制联系起来。我们通过呈现来自两个模拟模型（标准并行、协同激活）和一个典型的视觉检测实验的数据来说明统一空间的有效性。还提供了一个统一空间的转换表。
