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

Schwarz和Miller（2016）提出了一个全面的定量过程模型，称为GSDT，用于解释视觉搜索标准范式中表现的所有与速度和准确性相关的方面。GSDT将先前研究提出的四种不同的部分处理机制连贯地整合在一个统一的概念框架内：视觉引导(G)、序列(S)对象检查、单个对象检查的扩散(D)建模和策略搜索终止(T)规则。拟议项目的目的是在四个单独的子项目中探索GSDT的有效性和更广泛的适用性，以及它的局限性和边界条件：(1)利用多次注视搜索的眼动数据来评估GSDT关于序列性和战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了眼动）；(2)利用GSDT连贯地解释视觉搜索各方面的语义、分类和典型效应；最后（IV）应用GSDT来准确确定哪些加工机制导致了视觉搜索中与年龄相关的性能下降。除了这些新颖的方面，我们还将通过在所有四个子项目中使用现实目标和干扰物（真实物体的高分辨率图像），扩展关于GSDT充分性和有效性的有限和不确定的现有证据。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应用和拟合GSDT，以标准搜索范式获得单观察者数据。最后，我们将定量和系统地将GSDT与相关的视觉搜索综合模型（如GS、CGS、并行扩散模型或TVA）进行比较。

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.