Representational formats of human memory traces

Neural representations are internal brain states that constitute the brain's model of the external world or some of its features. In the presence of sensory input, a representation may reflect various properties of this input. When perceptual information is no longer available, the brain can still activate representations of previously experienced episodes due to the formation of memory traces. In this review, we aim at characterizing the nature of neural memory representations and how they can be assessed with cognitive neuroscience methods, mainly focusing on neuroimaging. We discuss how multivariate analysis techniques such as representational similarity analysis (RSA) and deep neural networks (DNNs) can be leveraged to gain insights into the structure of neural representations and their different representational formats. We provide several examples of recent studies which demonstrate that we are able to not only measure memory representations using RSA but are also able to investigate their multiple formats using DNNs. We demonstrate that in addition to slow generalization during consolidation, memory representations are subject to semantization already during short-term memory, by revealing a shift from visual to semantic format. In addition to perceptual and conceptual formats, we describe the impact of affective evaluations as an additional dimension of episodic memories. Overall, these studies illustrate how the analysis of neural representations may help us gain a deeper understanding of the nature of human memory.

Automated summary

Neural representations are internal brain states that model the external world or its features. Memory traces allow the brain to activate representations of past experiences even in the absence of sensory input. This review explores the nature of neural memory representations and how cognitive neuroscience methods, particularly neuroimaging, can assess them. The analysis of neural representations provides insights into their structure and different formats using techniques such as representational similarity analysis (RSA) and deep neural networks (DNNs). Multiple recent studies demonstrate the measurement and investigation of memory representations, revealing format shifts from visual to semantic and the impact of affective evaluations in episodic memories. Ultimately, the analysis of neural representations enhances our understanding of human memory.