Computational modeling of generative episodic memory

生成情景记忆的计算模型

• 批准号: 419039588
• 负责人: Professor Dr. Laurenz Wiskott
• 金额: --
• 依托单位: Institut für Neuroinformatik (INI)
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/419039588?language=en
• 关键词: Computational; modeling; generative; episodic; memory

Despite the large number of experimental and conceptual studies that have suggested that episodic memory is generative, computational models almost exclusively adopt the storage view. In this project, we develop a generative model for the encoding and retrieval of personally experienced episodes, which describes the interplay between hippocampus and neocortex.The model consists of (a) a perceptual-semantic network that is hierarchically structured and gradually transforms perceived images into a more semantic representation, and (b) a semantic network that is able to complement incomplete semantic representations in a plausible way in a recurrent process. The former is realized by a 'vector quantized variational autoencoder (VQ-VAE)', the latter by a 'pixel convolutional neural network (PixelCNN)'. When an episode is encoded, the VQ-VAE first converts it into a semantic representation, part of which is then stored. When attention is high, a large part is stored; when attention is low, only a small part is stored. During recall, this part is read out again and plausibly completed by the PixelCNN. The VQ-VAE can then be applied backwards and reconstruct a concrete episode from the complete semantic representation.So far, we use single images of handwritten digits on different backgrounds as episodes; the digits represent objects in different variants, the backgrounds represent the context, e.g. the room in which the object can be found. Objects or digits are preferably found in certain contexts or in front of certain backgrounds, e.g. a toaster in the kitchen (congruent context) and not in the bathroom (incongruent context) or in our simulation a '2' in front of a background with triangles and not squares. We have already reproduced the following experimental results with the model: (i) higher attention improves episodic memory, (ii) objects in congruent context are better remembered than in incongruent context, and (iii) if the correct context is not remembered for an object, at least a semantically congruent context is usually remembered.Episodic memory is not reliable and can be modified by many influences. For example, we do not like to remember situations that were embarrassing to us, and we like to bring our memories more in line with the image we have of ourselves in retrospect. Conversely, our memories naturally influence our self-image. It is also known that our episodic memory can be altered by social interaction. In particular, we tend to align memories with opinions of our interaction partners when we feel connected to them. These aspects are the subject of further research on our model in cooperation with philosophers who are thinking about the self-model and with psychologists who are doing experiments on the influence of social interaction on memory.

尽管大量的实验和概念研究表明，情景记忆是生成的，计算模型几乎完全采用存储的观点。在这个项目中，我们开发了一个用于编码和检索个人经历事件的生成模型，该模型描述了海马和新皮层之间的相互作用。该模型包括（a）一个感知语义网络，该网络是分层结构的，并逐渐将感知图像转换为更具语义的表示，以及（B）语义网络，其能够在循环过程中以合理的方式补充不完整的语义表示。前者是由一个“矢量量化变分自动编码器（VQ-VAE）”，后者是由一个“像素卷积神经网络（PixelCNN）”。当一个片段被编码时，VQ-VAE首先将其转换为语义表示，然后存储其中的一部分。当注意力高时，大部分被存储;当注意力低时，只有一小部分被存储。在回忆过程中，这一部分被再次读出，并由PixelCNN合理地完成。VQ-VAE可以向后应用，并从完整的语义表示中重建一个具体的情节。数字表示不同变体中的对象，背景表示上下文，例如可以在其中找到对象的房间。对象或数字优选地在某些上下文中或在某些背景的前面找到，例如，在厨房中的烤面包机（一致的上下文）而不是在浴室中的烤面包机（不一致的上下文），或者在我们的模拟中，在具有三角形而不是正方形的背景前面的“2”。我们已经用这个模型再现了以下实验结果：（1）较高的注意力提高了情景记忆;（2）一致性语境中的对象比不一致性语境中的对象更容易被记住;（3）如果一个对象没有记住正确的语境，那么至少一个语义一致的语境通常会被记住。例如，我们不喜欢回忆那些让我们尴尬的情景，我们喜欢让我们的记忆与我们回想起来的自己的形象更加一致。相反，我们的记忆自然会影响我们的自我形象。我们还知道，我们的情景记忆可以通过社会互动来改变。特别是，当我们感到与他们有联系时，我们倾向于将记忆与我们的互动伙伴的意见联系起来。 这些方面是我们与思考自我模型的哲学家和正在做社会互动对记忆影响实验的心理学家合作，对我们的模型进行进一步研究的主题。