School of Psychological Sciences, Tel Aviv University, Ramat Aviv, POB 39040, 69978, Tel Aviv, Israel.
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
Behav Res Methods. 2021 Oct;53(5):1895-1909. doi: 10.3758/s13428-021-01540-6. Epub 2021 Feb 25.
Perception famously involves both bottom-up and top-down processes. The latter are influenced by our previous knowledge and expectations about the world. In recent years, many studies have focused on the role of expectations in perception in general, and in object processing in particular. Yet studying this question is not an easy feat, requiring-among other things-the creation and validation of appropriate stimuli. Here, we introduce the ObjAct stimulus-set of free-to-use, highly controlled real-life scenes, on which critical objects are pasted. All scenes depict human agents performing an action with an object that is either congruent or incongruent with the action. The focus on human actions yields highly constraining contexts, strengthening congruency effects. The stimuli were analyzed for low-level properties, using the SHINE toolbox to control for luminance and contrast, and using a deep convolutional neural network to mimic V1 processing and potentially discover other low-level factors that might differ between congruent and incongruent scenes. Two online validation studies (N = 500) were also conducted to assess the congruency manipulation and collect additional ratings of our images (e.g., arousal, likeability, visual complexity). We also provide full descriptions of the online sources from which all images were taken, as well as verbal descriptions of their content. Taken together, this extensive validation and characterization procedure makes the ObjAct stimulus-set highly informative and easy to use for future researchers in multiple fields, from object and scene processing, through top-down contextual effects, to the study of actions.
感知过程中既有自下而上的加工，也有自上而下的加工。后者受到我们对世界的先验知识和预期的影响。近年来，许多研究都集中在期望在感知中的作用上，特别是在物体处理中的作用。然而，研究这个问题并不容易，需要创建和验证适当的刺激。在这里，我们引入了免费使用的 ObjAct 刺激集，该刺激集包含高度控制的真实场景，其中粘贴有关键物体。所有场景都描绘了人类代理使用与动作一致或不一致的物体执行动作。对人类动作的关注产生了高度约束的上下文，增强了一致性效应。使用 SHINE 工具箱对低水平特性进行了分析，该工具箱用于控制亮度和对比度，并使用深度卷积神经网络模拟 V1 处理，以发现可能在一致和不一致场景之间存在差异的其他低水平因素。还进行了两项在线验证研究（N=500），以评估一致性操作并收集我们图像的其他评分（例如，唤醒度、喜欢度、视觉复杂度）。我们还提供了所有图像来源的完整描述，以及对其内容的文字描述。总的来说，这种广泛的验证和特征描述过程使 ObjAct 刺激集对来自多个领域的未来研究人员非常有用，包括物体和场景处理、自上而下的上下文效应以及动作研究。
