Dynamic-stochastic decision models for multiple alternatives with multiple attributes

动态-随机模型基于序贯抽样的概念，在心理学的许多领域中被广泛用于预测选择行为和反应时间，从基本知觉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，许多应用需要将序贯抽样机制扩展到具有多个备选方案的多属性选择选项。对于更复杂的情况，正式推导模型预测往往是困难或不可能的，严重限制了这些模型的心理学可解释性和实验测试。一个例外是Diederich(1997)提出的基于连续过程的矩阵近似的多属性决策场理论(MDFT)，但到目前为止，它只适用于二元选择和预定的属性处理顺序。为此，(1)我们将MDFT扩展到包括各种属性处理机制(固定和随机的加工顺序、转换时间和加工持续时间)，这些机制构成了关于刺激加工中注意力分配的不同假设。接着(2)建立了一个保留MDFT重要特征的多项选择期权决策模型(Box模型)。该项目的实验部分将对MDFT的几个推广版本进行实验探索。我们将在辨别和选择范式中探索属性加工的不同方面。第一系列实验将检验两种不同知觉辨别任务中，作为刺激属性的报酬对反应频率的影响的假设。信息的时间分布对任务绩效的影响将在第二系列实验中进行研究。最后，我们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.