ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2019
Memory and mental time travel in humans and social robots.
Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Modelling of What-Where-When Everyday Memories in Rats.The European journal of neuroscience · 2025Article
- Article
- Synthesizing the temporal self: robotic models of episodic and autobiographical memory.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Review
- Personalizing Human-Agent Interaction Through Cognitive Models.Frontiers in psychology · 2020Article
- From social brains to social robots: applying neurocognitive insights to human-robot interaction.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Article
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Authors and funding
5 authors.
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Abstract
From neuroscience, brain imaging and the psychology of memory, we are beginning to assemble an integrated theory of the brain subsystems and pathways that allow the compression, storage and reconstruction of memories for past events and their use in contextualizing the present and reasoning about the future-mental time travel (MTT). Using computational models, embedded in humanoid robots, we are seeking to test the sufficiency of this theoretical account and to evaluate the usefulness of brain-inspired memory systems for social robots. In this contribution, we describe the use of machine learning techniques-Gaussian process latent variable models-to build a multimodal memory system for the iCub humanoid robot and summarize results of the deployment of this system for human-robot interaction. We also outline the further steps required to create a more complete robotic implementation of human-like autobiographical memory and MTT. We propose that generative memory models, such as those that form the core of our robot memory system, can provide a solution to the symbol grounding problem in embodied artificial intelligence. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.
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