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主要的定量框架，描述视觉搜索中的动态加权过程。特别是，我们计划对不同类型的审判效果进行建模，这是最重要的：重复的效果与目标定义（功能和）尺寸的singleton-target搜索场景中的效果。在过去的几十年中，这些效果在定性上得到了很好的特征，并导致了“维度加权”的概念（现在已成为基于显着性搜索的显着性帐户的公认组成部分，包括J. M. Wolfe的指导搜索模型）。 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, expectedly purely stimulus-driven singleton feature 'pop-out' search (e.g., Found & Müller, 1996), and an even larger portion in singleton combination search (e.g., Weidner et al., 2002) - and is arguably more影响比自上而下的基于模板的影响）对搜索性能的影响（例如，Kristjánsson等，2002）。但是，虽然显然很重要且表征良好，但我们仍然缺乏对加权动态的主要计算说明。在这里，我们建议通过将新的贝叶斯型知觉决策和权重更新模型与RT分布的通用模型相结合来开发这样的帐户。更详细地说，由于Singleton特征搜索主要是由目标分子驱动的，因此我们建议使用以后的（“用厄戈迪克速率的线性阈值”模型）模型来对RT分布进行建模。最重要的是，我们将开发一个感知模型，以说明先验知识对目标检测的影响（基于特征和维度的）审判效果以及维度加权机制。此外，我们将重新审视冗余弹出信号的共同处理的概念，并检查其与审判效果的关系。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不限于弹出搜索的单例搜索范式，并寻找加权动态的大脑相关性。