Testing, validating and applying a general process model (GSDT) of visual search.

Schwarz和Miller(2016)提出了一个全面的量化过程模型，称为GSDT，以解释视觉搜索标准范式中表征性能的所有与速度和精度相关的方面。GSDT在一个统一的概念框架内连贯地整合了先前研究提出的四种不同的部分加工机制：视觉引导(G)、连续(S)物体检测、个体物体检测的扩散(D)建模和策略性搜索终止(T)规则。拟议项目的目的是探讨GSDT的有效性和更广泛的适用性，以及它在四个不同的分项目中的限制和边界条件：(一)使用多重注视搜索的眼动数据来评估GSDT关于序列性和策略性搜索终止的假设(在子项目II和III的所有实验中也记录和分析眼动)，(Ii)使用GSDT连贯地解释语义、分类和典型性对视觉搜索各个方面的影响，(iii.)使用一种新的、原创的视觉搜索范式来测试GSDT关于视觉指导和目标-分心物辨别能力的中心假设，最后(IV)。应用GSDT来准确识别哪些加工机制对视觉搜索中年龄相关的性能下降有贡献并结合在一起。除了这些新的方面，我们还将通过在所有四个子项目中使用现实的目标和干扰物(真实对象的高分辨率图像)来扩展关于GSDT充分性和有效性的有限和非决定性的现有证据。我们还将开发和提供(基于Python的)软件，允许研究人员以常规方式将GSDT应用于通过标准搜索范式获得的单一观察者数据。最后，我们将定量和系统地将GSDT与相关的综合视觉搜索模型进行比较，如GS、CGS、基于平行扩散的模型或TVA。

Schwarz and Miller (2016) presented a comprehensive quantitative process model, called GSDT, to account for all speed- and accuracy-related aspects characterizing performance in the standard paradigm of visual search. GSDT coherently incorporates and integrates four distinct partial processing mechanisms suggested by previous research within a single unified conceptual frame: visual guidance (G), serial (S) object inspection, diffusion (D) modeling of individual object inspections, and a strategic search termination (T) rule. The aim of the proposed project is to explore the validity and wider applicability of GSDT as well as its limits and boundary conditions in four separate Subprojects: (I.) using eye movement data from multiple fixation search to evaluate GSDT's assumptions about seriality and strategic search termination (eye movements are also recorded and analyzed in all Experiments of Subprojects II and III), (II.) using GSDT to coherently account for semantic, categorical, and typicality effects on all aspects of visual search, (III.) using a new, original visual search paradigm to test central GSDT assumptions about visual guidance and target-distractor discriminability, and finally (IV.) applying GSDT to identify exactly which processing mechanisms contribute and combine to the age-related performance decline in visual search. Beyond these novel aspects we will also extend the limited and inconclusive extant evidence regarding the adequacy and validity of GSDT by using, in all four Subprojects, realistic targets and distractors (high-resolution images of real objects). We will also develop and provide (Python-based) software allowing researchers to apply and fit GSDT in a routine manner to single-observer data obtained with the standard search paradigm. Finally, we will quantitatively and systematically compare GSDT to related comprehensive models of visual search such as GS, CGS, parallel diffusion-based models, or TVA.