Modeling inter-trial dynamics in visual search: developing a hierarchical predictive-coding framework of response decisions in a variety of search tasks

视觉搜索中的试验间动态建模：开发各种搜索任务中响应决策的分层预测编码框架

• 批准号: 277137374
• 负责人: Professor Dr. Hermann J. Müller
• 金额: --
• 依托单位: Lehrstuhl für Allgemeine und Experimentelle Psychologie
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/277137374?language=en
• 关键词: Modeling; inter; trial; dynamics; visual

The aim of the project is to develop a principled, quantitative framework describing dynamic weighting processes in visual search. In particular, we plan to model different types of inter-trial effects, foremost: the effects of repetitions vs. switches of the target-defining (features and) dimensions in singleton-target search scenarios. These effects have been well characterized qualitatively over the past decades and led to the notion of 'dimension weighting' (which has now become an accepted component of saliency-based accounts of visual search, including J. M. Wolfe's Guided Search model). Although inter-trial dynamics is sometimes still seen as only a 'minor' source of search RT variation, it actually accounts for a large portion of response time (RT) variability even in the most simple, supposedly purely stimulus-driven singleton feature 'pop-out' search (e.g., Found & Müller, 1996), and an even larger portion in singleton conjunction search (e.g., Weidner et al., 2002) - and is arguably more influential than top-down, template-based) influences on search performance (e.g., Kristjánsson et al., 2002). However, while clearly important and well characterized, we are still lacking a principled, computational account of the dynamics of weighting. Here, we propose to develop such an account by combining a new, Bayesian-type perceptual decision and weight updating model with a generative model of RT distributions. In more detail, as singleton feature search is driven largely by target saliency, we propose to use the LATER ('Linear Approach to Threshold with Ergodic Rate') model for modeling the RT distributions. On top of this, we will develop a perceptual model accounting for the influences of prior knowledge on target detection, (feature- and dimension-based) inter-trial effects, as well as dimension-weighting mechanisms. In addition, we will revisit the notion of coactive processing of redundant pop-out signals and examine its relation to inter-trial effects. In a second project phase, we plan to generalize our perceptual-response framework to a singleton search paradigm not limited to the pop-out search, as well as looking for brain correlates of the weighting dynamics.

该项目的目的是开发一个原则性的，定量的框架，描述动态加权过程中的视觉搜索。特别是，我们计划对不同类型的试验间效应进行建模，最重要的是：在单目标搜索场景中重复与目标定义（特征和）维度切换的影响。在过去的几十年里，这些效果已经被定性地描述出来，并导致了“维度加权”的概念（现在已经成为视觉搜索的基于显着性的解释的一个公认的组成部分，包括J. M。Wolfe的Guided Search模型。虽然试验间动态有时仍然被视为搜索RT变化的“次要”来源，但它实际上占了响应时间（RT）变化的很大一部分，即使在最简单的，据称纯粹是刺激驱动的单例特征“弹出”搜索中（例如，Found & Müller，1996），以及单例合取搜索中更大的部分（例如，Weiden等人，2002）-并且可以说比自上而下的、基于模板的更有影响力）对搜索性能的影响（例如，Kristjánsson等人，2002年）。然而，尽管权重的动态性很重要，也很好地描述了权重的动态性，但我们仍然缺乏一个原则性的、计算性的解释。在这里，我们建议开发这样一个帐户相结合的一个新的，贝叶斯型的感知决策和权重更新模型与生成模型的RT分布。更详细地说，由于单例特征搜索在很大程度上是由目标显着性驱动的，因此我们建议使用LATER（“遍历率阈值的线性方法”）模型来建模RT分布。最重要的是，我们将开发一个感知模型，考虑先验知识对目标检测的影响，（基于特征和维度的）试验间效应，以及维度加权机制。此外，我们将重新审视冗余弹出信号的协同处理的概念，并研究其与试验间效应的关系。在第二个项目阶段，我们计划将我们的感知反应框架推广到一个单例搜索范式，而不仅仅限于弹出搜索，以及寻找大脑相关的加权动态。