论文标题
通过神经网络的预测事件细分和表示：通过心理实验评估的自我监督模型
Predictive Event Segmentation and Representation with Neural Networks: A Self-Supervised Model Assessed by Psychological Experiments
论文作者
论文摘要
人们将复杂，不断变化和连续的经验分为基本，稳定和离散的时空体验单元，称为事件。事件细分文献研究了允许人们提取事件的机制。事件细分理论指出，人们预测正在进行的活动并观察预测错误信号以找到使事件与众不同的事件边界。在这项研究中，我们研究了通过计算模型和随附的心理实验提高这种能力的机制。受到事件分割理论和预测处理的启发，我们引入了事件分割的自我监督模型。该模型由神经网络组成，这些神经网络在下一个时间阶段预测感觉信号以表示不同事件，以及根据其预测错误来调节这些网络的认知模型。为了验证我们的模型在分割事件中的能力，在被动观察过程中学习它们，并在其内部代表空间中代表它们，我们准备了一个视频，该视频描绘了以点光显示器表示的人类行为。我们将参与者和模型的事件细分行为与该视频中的两个分层事件分割级别进行了比较。通过使用点 - 生物相关技术，我们证明了模型的事件分割决策与参与者的响应相关。此外，通过通过基于相似性的技术近似参与者的表示空间，我们表明我们的模型与参与者的模型形成了相似的表示空间。结果表明，跟踪预测误差信号的模型可以产生类似人类的事件边界和事件表示。最后，我们讨论了我们对事件认知文献的贡献，以及我们对大脑中事件细分的理解。
People segment complex, ever-changing and continuous experience into basic, stable and discrete spatio-temporal experience units, called events. Event segmentation literature investigates the mechanisms that allow people to extract events. Event segmentation theory points out that people predict ongoing activities and observe prediction error signals to find event boundaries that keep events apart. In this study, we investigated the mechanism giving rise to this ability by a computational model and accompanying psychological experiments. Inspired from event segmentation theory and predictive processing, we introduced a self-supervised model of event segmentation. This model consists of neural networks that predict the sensory signal in the next time-step to represent different events, and a cognitive model that regulates these networks on the basis of their prediction errors. In order to verify the ability of our model in segmenting events, learning them during passive observation, and representing them in its internal representational space, we prepared a video that depicts human behaviors represented by point-light displays. We compared event segmentation behaviors of participants and our model with this video in two hierarchical event segmentation levels. By using point-biserial correlation technique, we demonstrated that event segmentation decisions of our model correlated with the responses of participants. Moreover, by approximating representation space of participants by a similarity-based technique, we showed that our model formed a similar representation space with those of participants. The result suggests that our model that tracks the prediction error signals can produce human-like event boundaries and event representations. Finally, we discussed our contribution to the literature of event cognition and our understanding of how event segmentation is implemented in the brain.
