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

该项目的目的是开发一个原则性的、量化的框架，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试验间效应进行建模，最重要的是：在单一目标搜索场景中，目标定义(特征和)维度的重复与切换的影响。在过去的几十年里，这些效应得到了很好的定性描述，并导致了“维度权重”的概念(它现在已经成为视觉搜索中基于显着性的描述的一个公认组成部分，包括J·M·沃尔夫的引导式搜索模型)。尽管试验间动态有时仍被视为搜索反应时差异的一小部分来源，但它实际上解释了响应时间(RT)变化的很大一部分，即使在最简单的、被认为是纯粹刺激驱动的单一特征“弹出”搜索中(例如，Found&Müler，1996)，以及在单一联合搜索中更大的一部分(例如，Weidner等人，2002)-并且可以说比自上而下、基于模板的影响更大(例如，Kristjánsson等人，2002)。然而，尽管我们显然很重要，也很有特点，但我们仍然缺乏对权重动态的原则性的、计算性的解释。在这里，我们建议通过将新的贝叶斯类型的感知决策和权重更新模型与RT分布的生成模型相结合来开发这样的帐户。更详细地说，由于单一特征搜索在很大程度上是由目标显著度驱动的，因此我们建议使用后者(具有遍历率的线性阈值方法)模型来建模RT分布。在此基础上，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，我们将重温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，我们计划将我们的知觉反应框架推广到不限于弹出搜索的单一搜索范式，以及寻找权重动态的大脑关联。

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.