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

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:
$0.0万
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
项目状态:
已结题
起止时间:
2014-12-31 至 2023-12-31

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中文摘要
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英文摘要
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.
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Value-driven Crossmodal Attention
  • 批准号:
    411721317
  • 项目类别:
    Research Grants
  • 资助金额:
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  • 财政年份:
    2019
  • 负责人:
    Professor Dr. Hermann J. Müller
  • 依托单位:
Coordination Funds
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    242809327
  • 项目类别:
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  • 资助金额:
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  • 财政年份:
    2014
  • 负责人:
    Professor Dr. Hermann J. Müller
  • 依托单位:
Aufmerksamkeits-Diagnostik auf der Basis der Theorie der visuellen Aufmerksamkeit (TVA): Untersuchungen an Normalpersonen und Patienten
国内基金
海外基金
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  • 批准号:
    60872101
  • 项目类别:
    面上项目
  • 资助金额:
    31.0万元
  • 批准年份:
    2008
  • 负责人:
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