Some things look more complex than others. For example, a crenulate and richly organized leaf may seem more complex than a plain stone. What is the nature of this experience—and why do we have it in the first place? Here, we explore how object complexity serves as an efficiently extracted visual signal that the object merits further exploration. We algorithmically generated a library of geometric shapes and determined their complexity by computing the cumulative surprisal of their internal skeletons—essentially quantifying the “amount of information” within each shape—and then used this approach to ask new questions about the perception of complexity. Experiments 1–3 asked what kind of mental process extracts visual complexity: a slow, deliberate, reflective process (as when we decide that an object is expensive or popular) or a fast, effortless, and automatic process (as when we see that an object is big or blue)? We placed simple and complex objects in visual search arrays and discovered that complex objects were easier to find among simple distractors than simple objects are among complex distractors—a classic search asymmetry indicating that complexity is prioritized in visual processing. Next, we explored the function of complexity: Why do we represent object complexity in the first place? Experiments 4–5 asked subjects to study serially presented objects in a self‐paced manner (for a later memory test); subjects dwelled longer on complex objects than simple objects—even when object shape was completely task‐irrelevant—suggesting a connection between visual complexity and exploratory engagement. Finally, Experiment 6 connected these implicit measures of complexity to explicit judgments. Collectively, these findings suggest that visual complexity is extracted efficiently and automatically, and even arouses a kind of “perceptual curiosity” about objects that encourages subsequent attentional engagement.

中文翻译：

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Curious Objects：视觉复杂性如何引导注意力和参与度

有些事情看起来比其他事情更复杂。例如，圆齿状和组织丰富的叶子可能看起来比普通石头更复杂。这种体验的本质是什么——我们为什么要拥有它？在这里，我们探索对象复杂性如何作为一个有效提取的视觉信号，该对象值得进一步探索。我们通过算法生成了一个几何形状库，并通过计算其内部骨架的累积惊喜来确定它们的复杂性——基本上量化每个形状内的“信息量”——然后使用这种方法提出关于复杂性感知的新问题。实验 1-3 询问什么样的心理过程提取视觉复杂性：一个缓慢的、深思熟虑的、反思的过程（当我们决定一个物体是昂贵的或受欢迎的）还是一个快速、轻松、和自动处理（就像我们看到一个物体是大的或蓝色的一样）？我们将简单和复杂的对象放在视觉搜索数组中，发现复杂对象在简单干扰项中比在复杂干扰项中更容易找到复杂对象——经典的搜索不对称性表明复杂性在视觉处理中被优先考虑。接下来，我们探讨了复杂性的作用：为什么我们首先表示对象复杂性？实验 4-5 要求受试者以自定进度的方式研究连续呈现的物体（用于以后的记忆测试）；与简单物体相比，受试者在复杂物体上停留的时间更长——即使物体形状与任务完全无关——这表明视觉复杂性和探索性参与之间存在联系。最后，实验 6 将这些隐含的复杂性测量与明确的判断联系起来。总的来说，这些发现表明视觉复杂性被有效和自动地提取，甚至引起了一种对物体的“感知好奇心”，从而鼓励随后的注意力参与。