Person:
Calvo Tapia, Carlos

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First Name
Carlos
Last Name
Calvo Tapia
URI
https://hdl.handle.net/20.500.14352/74305
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Biológicas
Department
Biodiversidad, Ecología y Evolución
Area
Matemática Aplicada
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2018 - 20203
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Author: Sánchez Jiménez, Abel × Has files: true ×

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Now showing 1 - 3 of 3
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    Static internal representation of dynamic situations reveals time compaction in human cognition
    (Journal of Advanced Research, 2020) Villacorta-Atienza, José Antonio; Calvo Tapia, Carlos; Díez-Hermano, Sergio; Sánchez Jiménez, Abel; Lobov, Sergei; Krilova, Nadia; Murciano Cespedosa, Antonio; López-Tolsa, Gabriela E.; Pellón, Ricardo; Makarov Slizneva, Valeriy
    Introduction: The human brain has evolved under the constraint of survival in complex dynamic situations. It makes fast and reliable decisions based on internal representations of the environment. Whereas neural mechanisms involved in the internal representation of space are becoming known, entire spatiotemporal cognition remains a challenge. Growing experimental evidence suggests that brain mechanisms devoted to spatial cognition may also participate in spatiotemporal information processing. Objectives: The time compaction hypothesis postulates that the brain represents both static and dynamic situations as purely static maps. Such an internal reduction of the external complexity allows humans to process time-changing situations in real-time efficiently. According to time compaction, there may be a deep inner similarity between the representation of conventional static and dynamic visual stimuli. Here, we test the hypothesis and report the first experimental evidence of time compaction in humans. Methods: We engaged human subjects in a discrimination-learning task consisting in the classification of static and dynamic visual stimuli. When there was a hidden correspondence between static and dynamic stimuli due to time compaction, the learning performance was expected to be modulated. We studied such a modulation experimentally and by a computational model. Results: The collected data validated the predicted learning modulation and confirmed that time compaction is a salient cognitive strategy adopted by the human brain to process time-changing situations. Mathematical modelling supported the finding. We also revealed that men are more prone to exploit time compaction in accordance with the context of the hypothesis as a cognitive basis for survival. Conclusions: The static internal representation of dynamic situations is a human cognitive mechanism involved in decision-making and strategy planning to cope with time-changing environments. The finding opens a new venue to understand how humans efficiently interact with our dynamic world and thrive in nature.
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    Cognitive Neural Network Driving DoF-Scalable Limbs in Time-Evolving Situations
    (2018) Calvo Tapia, Carlos; Villacorta Atienza, José Antonio; Kastalskiy, Innokentiy; Díez Hermano, Sergio; Sánchez Jiménez, Abel; Makarov Slizneva, Valeriy
    Object handling and manipulation are vital skills for humans and autonomous humanoid robots. The fundamental bases of how our brain solves such tasks remain largely unknown. Here we develop a novel approach that addresses the problem of limb movements in time-evolving situations at an abstract cognitive level. We exploit the concept of generalized cognitive maps constructed in the so-called handspace by a neural network simulating a wave simultaneously exploring different subject actions independently on the number of objects in the workspace. We show that the approach is scalable to limbs with minimalistic and redundant numbers of degrees of freedom (DoF). It also allows biasing the effort of reaching a target among different DoF.
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    Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations
    (Frontiers in Neurorobotics, 2020) Calvo Tapia, Carlos; Villacorta Atienza, José Antonio; Díez Hermano, Sergio; Khoruzkho, Maxim; Lobov, Sergey; Potapov, Ivan; Sánchez Jiménez, Abel; Makarov Slizneva, Valeriy
    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.