Bayesian multi-level modelling for predicting single and double feature visual search
Bayesian multi-level modelling for predicting single and double feature visual search
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DOI：
10.1016/j.cortex.2023.10.014
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发表时间：
2023-11-25
期刊：
影响因子：
3.6
通讯作者：
Clarke，Alasdair D. F.
中科院分区：
文献类型：
--
作者：
Hughes，Anna E.;Nowakowska，Anna;Clarke，Alasdair D. F.
视觉搜索任务的表现经常被总结为“搜索斜率”-每增加一个干扰物的反应时间的额外成本。虽然具有浅搜索斜率的搜索任务被称为高效（弹出，并行，特征），但在高效和低效（串行，合取）搜索之间没有明确的二分法。事实上，在经验数据中观察到一系列搜索斜率。目标对比信号（TCS）理论是一个罕见的定量模型，试图预测搜索斜率有效的视觉搜索的例子。一项使用TCS框架的研究表明，双特征搜索（目标在颜色和形状上与干扰物不同）中的搜索斜率可以从相关的单特征搜索的斜率中估计出来。使用对比度组合模型进行该估计，并且共线对比度积分模型被示出优于其他选项。在我们的工作中，我们将TCS扩展到贝叶斯多层框架。我们调查使用正态分布和移位对数正态分布建模，并表明后者允许更好地拟合先前公布的数据。我们运行了一个新的完全受试者内实验，试图复制关键的原始发现，并表明总体而言，TCS在预测数据方面做得很好。然而，我们没有重复共线组合模型优于其他对比组合模型的发现，而是发现可能难以最终区分它们。
Performance in visual search tasks is frequently summarised by “search slopes” - the additional cost in reaction time for each additional distractor. While search tasks with a shallow search slopes are termed efficient (pop-out, parallel, feature), there is no clear dichotomy between efficient and inefficient (serial, conjunction) search. Indeed, a range of search slopes are observed in empirical data. The Target Contrast Signal (TCS) Theory is a rare example of quantitative model that attempts to predict search slopes for efficient visual search. One study using the TCS framework has shown that the search slope in a double-feature search (where the target differs in both colour and shape from the distractors) can be estimated from the slopes of the associated single-feature searches. This estimation is done using a contrast combination model, and a collinear contrast integration model was shown to outperform other options. In our work, we extend TCS to a Bayesian multi-level framework. We investigate modelling using normal and shifted-lognormal distributions, and show that the latter allows for a better fit to previously published data. We run a new fully within-subjects experiment to attempt to replicate the key original findings, and show that overall, TCS does a good job of predicting the data. However, we do not replicate the finding that the collinear combination model outperforms the other contrast combination models, instead finding that it may be difficult to conclusively distinguish between them.