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.

众所周知，感知涉及自下而上和自上而下的过程。后者受到我们以前对世界的了解和期望的影响。近年来，许多研究都集中在期望在一般感知中的作用，特别是在对象处理中的作用。然而，研究这个问题并非易事，除其他事项外，还需要创建和验证适当的刺激。在这里，我们介绍了免费使用、高度控制的现实生活场景的 ObjAct 刺激集，其中粘贴了关键对象。所有场景都描绘了人类代理使用与该动作一致或不一致的对象执行动作。对人类行为的关注产生了高度约束的环境，加强了一致性效应。分析刺激的低级属性，使用 SHINE 工具箱来控制亮度和对比度，并使用深度卷积神经网络来模拟 V1 处理，并可能发现其他可能在一致和不一致场景之间存在差异的低级因素。两项在线验证研究（N  = 500）也用于评估一致性操作并收集我们图像的其他评级（例如，唤醒、可爱性、视觉复杂性）。我们还提供对所有图像的在线来源的完整描述，以及对其内容的口头描述。总而言之，这种广泛的验证和表征过程使 ObjAct 刺激集具有高度的信息性，并且易于未来多个领域的研究人员使用，从对象和场景处理到自上而下的上下文效果，再到动作研究。