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首页> 外文期刊>Cognitive computation >Modelling Visual Search with the Selective Attention for Identification Model (VS-SAIM): A Novel Explanation for Visual Search Asymmetries
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Modelling Visual Search with the Selective Attention for Identification Model (VS-SAIM): A Novel Explanation for Visual Search Asymmetries

机译：使用选择性注意识别模型（VS-SAIM）对视觉搜索进行建模：视觉搜索不对称的新颖解释

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In earlier work, we developed the Selective Attention for Identification Model (SAIM [16]). SAIM models the human ability to perform translation-invariant object identification in multiple object scenes. SAIM suggests that central for this ability is an interaction between parallel competitive processes in a selection stage and a object identification stage. In this paper, we applied the model to visual search experiments involving simple lines and letters. We presented successful simulation results for asymmetric and symmetric searches and for the influence of background line orientations. Search asymmetry refers to changes in search performance when the roles of target item and non-target item (distractor) are swapped. In line with other models of visual search, the results suggest that a large part of the empirical evidence can be explained by competitive processes in the brain, which are modulated by the similarity between target and distractor. The simulations also suggest that another important factor is the feature properties of distractors. Finally, the simulations indicate that search asymmetries can be the outcome of interactions between top-down (knowledge about search items) and bottom-up (feature of search items) processing. This interaction in VS-SAIM is dominated by a novel mechanism, the knowledge-based on-centre-off-surround receptive field. This receptive field is reminiscent of the classical receptive fields but the exact shape is modulated by both, top-down and bottom-up processes. The paper discusses supporting evidence for the existence of this novel concept.
机译：在早期的工作中，我们开发了识别模型的选择性注意（SAIM [16]）。 SAIM模拟了人类在多个对象场景中执行平移不变对象识别的能力。 SAIM建议，此功能的核心是选择阶段和对象识别阶段的并行竞争过程之间的相互作用。在本文中，我们将该模型应用于涉及简单线条和字母的视觉搜索实验。我们为非对称和对称搜索以及背景线方向的影响提供了成功的仿真结果。搜索不对称是指当目标项目和非目标项目（干扰因素）的角色互换时，搜索性能的变化。与其他视觉搜索模型一致，结果表明，大部分经验证据可以通过大脑中的竞争过程来解释，而竞争过程是由目标和干扰物之间的相似性来调节的。模拟还表明，另一个重要因素是干扰因素的特征。最后，模拟表明搜索不对称性可能是自上而下（有关搜索项的知识）和自下而上（有关搜索项的特征）处理之间相互作用的结果。 VS-SAIM中的这种交互以一种新颖的机制为主导，该机制是基于知识的，围绕中心的，围绕周围的接收场。该接收场使人联想到经典的接收场，但其精确形状受自顶向下和自底向上过程的调节。本文讨论了这一新颖概念的存在的支持证据。

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