Dataset Information

Bioluminescent backlighting illuminates the complex visual signals of a social squid in the deep sea.

ABSTRACT: Visual signals rapidly relay information, facilitating behaviors and ecological interactions that shape ecosystems. However, most known signaling systems can be restricted by low light levels-a pervasive condition in the deep ocean, the largest inhabitable space on the planet. Resident visually cued animals have therefore been hypothesized to have simple signals with limited information-carrying capacity. We used cameras mounted on remotely operated vehicles to study the behavior of the Humboldt squid, Dosidicus gigas, in its natural deep-sea habitat. We show that specific pigmentation patterns from its diverse repertoire are selectively displayed during foraging and in social scenarios, and we investigate how these behaviors may be used syntactically for communication. We additionally identify the probable mechanism by which D. gigas, and related squids, illuminate these patterns to create visual signals that can be readily perceived in the deep, dark ocean. Numerous small subcutaneous (s.c.) photophores (bioluminescent organs) embedded throughout the muscle tissue make the entire body glow, thereby backlighting the pigmentation patterns. Equipped with a mechanism by which complex information can be rapidly relayed through a visual pathway under low-light conditions, our data suggest that the visual signals displayed by D. gigas could share design features with advanced forms of animal communication. Visual signaling by deep-living cephalopods will likely be critical in understanding how, and how much, information can be shared in one of the planet's most challenging environments for visual communication.

SUBMITTER: Burford BP

PROVIDER: S-EPMC7165453 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Bioluminescent backlighting illuminates the complex visual signals of a social squid in the deep sea.

Burford Benjamin P BP Robison Bruce H BH

Proceedings of the National Academy of Sciences of the United States of America 20200323 15

Visual signals rapidly relay information, facilitating behaviors and ecological interactions that shape ecosystems. However, most known signaling systems can be restricted by low light levels-a pervasive condition in the deep ocean, the largest inhabitable space on the planet. Resident visually cued animals have therefore been hypothesized to have simple signals with limited information-carrying capacity. We used cameras mounted on remotely operated vehicles to study the behavior of the Humboldt ...[more]

PMID: 32205436

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Project description:Social robotics represents a branch of human-robot interaction dedicated to developing systems to control the robots to operate in unstructured environments with the presence of human beings. Social robots must interact with human beings by understanding social signals and responding appropriately to them. Most social robots are still pre-programmed, not having great ability to learn and respond with actions adequate during an interaction with humans. Recently more elaborate methods use body movements, gaze direction, and body language. However, these methods generally neglect vital signs present during an interaction, such as the human emotional state. In this article, we address the problem of developing a system to turn a robot able to decide, autonomously, what behaviors to emit in the function of the human emotional state. From one side, the use of Reinforcement Learning (RL) represents a way for social robots to learn advanced models of social cognition, following a self-learning paradigm, using characteristics automatically extracted from high-dimensional sensory information. On the other side, Deep Learning (DL) models can help the robots to capture information from the environment, abstracting complex patterns from the visual information. The combination of these two techniques is known as Deep Reinforcement Learning (DRL). The purpose of this work is the development of a DRL system to promote a natural and socially acceptable interaction among humans and robots. For this, we propose an architecture, Social Robotics Deep Q-Network (SocialDQN), for teaching social robots to behave and interact appropriately with humans based on social signals, especially on human emotional states. This constitutes a relevant contribution for the area since the social signals must not only be recognized by the robot but help him to take action appropriated according to the situation presented. Characteristics extracted from people’s faces are considered for extracting the human emotional state aiming to improve the robot perception. The development and validation of the system are carried out with the support of SimDRLSR simulator. Results obtained through several tests demonstrate that the system learned satisfactorily to maximize the rewards, and consequently, the robot behaves in a socially acceptable way.

Dataset Information

Bioluminescent backlighting illuminates the complex visual signals of a social squid in the deep sea.

Publications

Bioluminescent backlighting illuminates the complex visual signals of a social squid in the deep sea.

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