Dynamic-stochastic decision models for multiple alternatives with multiple attributes

基于顺序抽样概念的动态随机模型通常用于预测心理学许多领域的选择行为和反应时间，从基本的感知任务到多维偏好和决策。通常，这些模型只能解释二元决策和一维刺激。然而，许多应用程序需要将顺序抽样机制扩展到具有多个备选项的多属性选择选项。对更复杂情况的模型预测的正式推导通常是困难的或不可能的，严重限制了这些模型的心理可解释性和实验测试。一个例外是diderich（1997）开发的多属性决策域理论（MDFT），它基于连续过程的矩阵逼近，但到目前为止，它只针对二元选择和预先确定的属性处理序列顺序进行了开发。本课题的目标是在动态随机决策模型中推进顺序抽样方法。为此，(1)我们将MDFT扩展为包括各种属性处理机制（固定和随机处理顺序、切换时间和处理持续时间），这些机制构成了关于刺激处理中注意分布的不同假设。然后(2)发展了一个多选择选项的决策模型（Box模型），它保留了MDFT的重要特征。所有模型的分析解决方案将努力。项目的实证部分将实验性地探讨MDFT的几个广义版本。我们将探讨辨别和选择范式中属性加工的不同方面。第一个系列的实验将检验作为刺激属性的回报对两种不同知觉辨别任务中反应频率的影响的假设。信息的时间分布对任务表现的影响将在第二组实验中进行研究。最后，将在第三个实验系列中研究属性数量对风险偏好的影响。

Dynamic-stochastic models, based on the notion of sequential sampling, are commonly used to predict choice behavior and response times in many areas of psychology, from basic perceptual tasks to multidimensional preference and decision making. Typically, these models can only account for binary decisions and unidimensional stimuli. However, many applications require an extension of the sequential sampling mechanism to multi-attribute choice options with multiple alternatives. Formal derivation of model predictions for more complex situations is often difficult or impossible, severely limiting the psychological interpretability and experimental tests of these models. An exception is Multiattribute Decision Field Theory (MDFT), developed in Diederich (1997), based on a matrix approximation of continuous processes, but up to now it has been developed only for binary choices and a pre-determined serial order of attribute processing.The goal of this project is to advance the sequential sampling approach within the class of dynamic-stochastic decision models. To this end, (1) we extend MDFT to include a variety of mechanisms for attribute handling (fixed and random processing order, switching times, and process durations) that constitute different hypotheses about the distribution of attention in stimulus processing. Then follows (2) the development of a decision model for multiple-choice options (Box model) that retains important features of MDFT. Analytical solutions for all models will be strived for.The empirical part of the project will experimentally probe several generalized versions of MDFT. We will explore different aspects of attribute processing in discrimination and choice paradigms. The first series of experiments will test hypotheses about the effect of payoffs, considered as stimulus attribute, on response frequencies in two different perceptual discrimination tasks. The effect of temporal distribution of information on task performance will be studied in a second series of experiments. Finally, the effect of the number of attributes on preference under risk will be investigated in a third experimental series.