Computational modeling of 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.
Solidity Meets Surprise: Cerebral and Behavioral Effects of Learning from Episodic Prediction Errors