ArticleFrontiers in neurorobotics2020
Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations.
Article in Frontiers in neurorobotics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Memorizing the future: Encoding anticipated interactions enhances human memory for dynamic stimuli.PNAS nexus · 2026Article
- Spatial prediction of dynamic interactions in rats.PloS one · 2025Article
- Score Prediction of Sports Events Based on Parallel Self-Organizing Nonlinear Neural Network.Computational intelligence and neuroscience · 2022Article
- Toward Reflective Spiking Neural Networks Exploiting Memristive Devices.Frontiers in computational neuroscience · 2022Review
- Spatial Memory in a Spiking Neural Network with Robot Embodiment.Sensors (Basel, Switzerland) · 2021Article
- Static internal representation of dynamic situations reveals time compaction in human cognition.Journal of advanced research · 2021Article
- Universal principles justify the existence of concept cells.Scientific reports · 2020Article
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Authors and funding
8 authors.
Funding
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Abstract
Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot.
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Registered trials
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