Calder-Travis Joshua, Ma Wei Ji
Department of Experimental Psychology, University of Oxford, Oxford, UK.
Department of Psychology, New York University, New York, NY, USA.
J Vis. 2020 Dec 2;20(13):11. doi: 10.1167/jov.20.13.11.
Visual search, the task of detecting or locating target items among distractor items in a visual scene, is an important function for animals and humans. Different theoretical accounts make differing predictions for the effects of distractor statistics. Here we use a task in which we parametrically vary distractor items, allowing for a simultaneously fine-grained and comprehensive study of distractor statistics. We found effects of target-distractor similarity, distractor variability, and an interaction between the two, although the effect of the interaction on performance differed from the one expected. To explain these findings, we constructed computational process models that make trial-by-trial predictions for behavior based on the stimulus presented. These models, including a Bayesian observer model, provided excellent accounts of both the qualitative and quantitative effects of distractor statistics, as well as of the effect of changing the statistics of the environment (in the form of distractors being drawn from a different distribution). We conclude with a broader discussion of the role of computational process models in the understanding of visual search.
视觉搜索，即在视觉场景中的干扰项中检测或定位目标项的任务，对动物和人类而言都是一项重要功能。不同的理论解释对干扰项统计数据的影响做出了不同预测。在此，我们采用了一项任务，其中我们对干扰项进行参数化变化，从而能够对干扰项统计数据进行同时精细且全面的研究。我们发现了目标 - 干扰项相似度、干扰项变异性以及二者之间的相互作用所产生的影响，尽管这种相互作用对表现的影响与预期不同。为了解释这些发现，我们构建了计算过程模型，这些模型基于所呈现的刺激对行为进行逐次试验预测。这些模型，包括贝叶斯观察者模型，对干扰项统计数据的定性和定量影响以及改变环境统计数据（以从不同分布中抽取干扰项的形式）的影响都提供了出色的解释。我们最后对计算过程模型在理解视觉搜索中的作用进行了更广泛的讨论。
