Mathematical Institute, Department of Mathematics and Computer Science, Physics, Justus Liebig University Gießen, Gießen, 35392, Germany
Center for Mind, Brain and Behavior, Philipps-University Marburg and Justus Liebig University Gießen, Marburg, 35032, Germany.
J Neurosci. 2023 Jul 19;43(29):5406-5413. doi: 10.1523/JNEUROSCI.0286-23.2023. Epub 2023 Jun 27.
Material properties, such as softness or stickiness, determine how an object can be used. Based on our real-life experience, we form strong expectations about how objects should behave under force, given their typical material properties. Such expectations have been shown to modulate perceptual processes, but we currently do not know how expectation influences the temporal dynamics of the cortical visual analysis for objects and their materials. Here, we tracked the neural representations of expected and unexpected material behaviors using time-resolved EEG decoding in a violation-of-expectation paradigm, where objects fell to the ground and deformed in expected or unexpected ways. Participants were 25 men and women. Our study yielded three key results: First, both objects and materials were represented rapidly and in a temporally sustained fashion. Second, objects exhibiting unexpected material behaviors were more successfully decoded than objects exhibiting expected behaviors within 190 ms after the impact, which might indicate additional processing demands when expectations are unmet. Third, general signals of expectation fulfillment that generalize across specific objects and materials were found within the first 150 ms after the impact. Together, our results provide new insights into the temporal neural processing cascade that underlies the analysis of real-world material behaviors. They reveal a sequence of predictions, with cortical signals progressing from a general signature of expectation fulfillment toward increased processing of unexpected material behaviors. In the real world, we can make accurate predictions about how an object's material shapes its behavior: For instance, we know that cups are typically made of porcelain and shatter when we accidentally drop them. Here, we use EEG to experimentally test how expectations about material behaviors impact neural processing. We showed our participants videos of objects that exhibited expected material behaviors (e.g., a glass shattering when falling to the ground) or unexpected material behaviors (e.g., a glass melting on impact). Our results reveal a hierarchy of predictions in cortex: The visual system rapidly generates signals that index whether expectations about material behaviors are met. These signals are followed by increased processing of objects displaying unexpected material behaviors.
材料特性，如柔软度或粘性，决定了物体的使用方式。基于我们的实际生活经验，我们对物体在特定材料特性下受力时的行为形成了强烈的预期。这些预期已被证明会调节感知过程，但我们目前还不知道预期如何影响物体及其材料的皮质视觉分析的时间动态。在这里，我们使用违反预期范式中的时分辨 EEG 解码来跟踪预期和意外材料行为的神经表示，在该范式中，物体落在地上并以预期或意外的方式变形。参与者是 25 名男性和女性。我们的研究产生了三个关键结果：首先，物体和材料都以快速且时间持续的方式进行表示。其次，在撞击后 190 毫秒内，表现出意外材料行为的物体比表现出预期行为的物体更成功地被解码，这可能表明当期望未得到满足时会产生额外的处理需求。第三，在撞击后 150 毫秒内，发现了对期望满足的一般信号，这些信号普遍适用于特定的物体和材料。总的来说，我们的结果为理解构成现实世界材料行为分析的基础的时间神经处理级联提供了新的见解。它们揭示了一系列预测，皮质信号从期望满足的一般特征发展为对意外材料行为的处理增加。在现实世界中，我们可以对物体的材料如何塑造其行为做出准确的预测：例如，我们知道杯子通常是由瓷器制成的，当我们不小心将其掉落时会破碎。在这里，我们使用 EEG 来实验测试对材料行为的期望如何影响神经处理。我们向参与者展示了表现出预期材料行为（例如，玻璃杯掉在地上时破裂）或意外材料行为（例如，玻璃杯在撞击时融化）的物体的视频。我们的结果揭示了皮质中预测的层次结构：视觉系统快速生成信号，指示对材料行为的期望是否得到满足。这些信号之后是对显示意外材料行为的物体的处理增加。
