Department of Psychological & Brain Sciences, Johns Hopkins University.
Cogn Sci. 2021 Apr;45(4):e12933. doi: 10.1111/cogs.12933.
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.
有些事物看起来比其他事物更复杂。例如，一个有齿状和组织丰富的叶子可能看起来比一块普通的石头更复杂。这种体验的本质是什么，为什么我们首先会有这种体验呢？在这里，我们探讨了物体复杂性如何作为一种高效提取的视觉信号，表明该物体值得进一步探索。我们通过计算内部骨架的累积惊讶度，从算法上生成了一个几何形状库，并确定了它们的复杂性——本质上是量化了每个形状内部的“信息量”，然后用这种方法来探讨对复杂性感知的新问题。实验 1-3 探讨了提取视觉复杂性的是哪种心理过程：是一个缓慢、深思熟虑、反思的过程（就像我们决定一个物体是昂贵还是受欢迎时那样），还是一个快速、轻松和自动的过程（就像我们看到一个物体很大或很蓝时那样）？我们在视觉搜索数组中放置了简单和复杂的物体，发现复杂的物体比简单的物体在简单的干扰物中更容易被找到，而简单的物体在复杂的干扰物中则更难被找到——这是一种经典的搜索不对称性，表明复杂性在视觉处理中是被优先处理的。接下来，我们探讨了复杂性的功能：为什么我们首先要表示物体的复杂性呢？实验 4-5 要求被试以自我调节的方式依次呈现物体（用于之后的记忆测试）；被试在复杂的物体上停留的时间比简单的物体长——即使物体的形状完全与任务无关——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 将这些复杂的隐性测量与显性判断联系起来。总的来说，这些发现表明，视觉复杂性是高效和自动提取的，甚至会引起一种对物体的“感知好奇心”，从而鼓励后续的注意力参与。
