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DTSTART;TZID=Europe/Berlin:20201029T110000
DTEND;TZID=Europe/Berlin:20201029T123000
DTSTAMP:20260409T151138
CREATED:20200824T125720Z
LAST-MODIFIED:20250604T095856Z
UID:8547-1603969200-1603974600@www.scienceofintelligence.de
SUMMARY:Giovanni Pezzulo\, ISTC-CNR Rome (Host: Verena Hafner): Human Sensorimotor Communication During Human Joint Action: Experimental and Computational Perspectives
DESCRIPTION:Giovanni Pezzulo is a researcher at the Institute of Cognitive Sciences and Technologies\, National Research Council in Rome\, Italy. His research centers on the neuronal and computational mechanisms of predictive processing\, goal-directed behaviour\, and the sensorimotor foundations of higher cognition. \nHuman sensorimotor communication during human joint action: experimental and computational perspectives \nDuring online social interactions\, humans engage in various forms of non-linguistic communication. I will discuss recent research in my lab and others about sensorimotor communication (SCM): the study of the subtle communicative signals embedded within our everyday pragmatic actions. SMC is ubiquitous during realistic social interactions. For example\, soccer players often carve their body movements in ways that are informative for their teammates or deceptive for their adversaries. Furthermore\, we have many ways to perform the same goal-directed action – say\, offer a glass of wine – and small kinematic differences can make the same action rude\, polite or snobbish\, thus potentially unveiling our hidden intentions. I will present some human-human experiments on SMC and discuss them in the context of a theoretical and computational model that we have been developing\, and which considers both the costs and benefits of SMC (e.g.\, in terms of increased interaction success). More broadly\, I will discuss our current understanding of the computational (and\, in part\, neural) mechanisms underlying social interaction at large\, including e.g.\, action observation\, prediction and planning mechanisms. \n— \nRe background material: \nFor a good introduction https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079876Photo by Uriel SC on Unsplash \nThe Zoom Link will be sent the day before the lecture. (Contact communication@scioi.de for specific questions)
URL:https://www.scienceofintelligence.de/event/distinguished-speaker-series-giovanni-pezzulo-hosted-by-verena-hafner/
CATEGORIES:Distinguished Speaker Series
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DTSTART;TZID=Europe/Berlin:20201022T160000
DTEND;TZID=Europe/Berlin:20201022T173000
DTSTAMP:20260409T151138
CREATED:20200824T124927Z
LAST-MODIFIED:20250604T100024Z
UID:8544-1603382400-1603387800@www.scienceofintelligence.de
SUMMARY:Guy Theraulaz\, (CNRS\, Toulouse\, France. Host: Pawel Romanczuk): Ethological Analysis and Computational Modeling of Social Interactions in Schooling Fish
DESCRIPTION:Abstract: Swarms of insects\, schools of fish and flocks of birds display an impressive variety of collective movement patterns that emerge from interactions among group members. These puzzling phenomena raise a variety of questions about the interaction rules that govern the coordination of individuals’ motion and the emergence of large-scale patterns. While numerous models have been proposed\, there is still a strong need for detailed experimental studies to foster the biological understanding of such collective motion phenomena. I will first describe the methods that we have developed in the recent years to characterize social interactions between individuals involved in the coordination of swimming in Rummy-nose tetra (Hemigrammus rhodostomus) from data gathered at the individual scale. This species of tropical fish performs burst-and-coast swimming behavior that consists of sudden heading changes combined with brief accelerations followed by quasi-passive\, straight decelerations. Our results show that both attraction and alignment behaviors control the reaction of fish to a neighbor. Then I will present how these results can be used to build a model of spontaneous burst-and-coast swimming and social interactions of fish\, with all parameters being estimated or directly measured from experiments. This model shows that the simple addition of the pairwise interactions with two neighbors quantitatively reproduces the collective behavior observed in groups of five fish. Increasing the number of interacting neighbors does not significantly improve the simulation results. Remarkably\, we find that groups remain cohesive and polarized even when each agent only interacts with only one of its neighbors: the one that has the strongest contribution to the heading variation of the focal agent. Finally\, I will present a swarm robotic platform with which we investigate the impact of collision avoidance based on speed control on the group behavior. This platform combines the implementation of the fish behavioral model and an engineering-minded control system to deal with real-world physical constraints. Remarkably\, and as already observed in the model simulations\, even when robots only interact with their most influential neighbor\, our results show that the group remains highly cohesive and polarized while reproducing the behavioral patterns observed in groups of fish in experimental conditions. Overall\, our results suggest that fish have to acquire only a minimal amount of information about their environment to coordinate their movements when swimming in groups.\n  \nShort bio: Guy Theraulaz is a senior research fellow at the National Center for Scientific Research (CNRS) and an expert in the study of collective animal behaviors. He is also a researcher in the field of swarm intelligence\, primarily studying social insects but also distributed algorithms\, e.g. for collective robotics\, directly inspired by nature. His research focuses on the understanding of a broad spectrum of collective behaviors in animal societies by quantifying and then modeling the individual level behaviors and interactions\, thereby elucidating the mechanisms generating the emergent\, group-level properties. He has published many papers on nest construction in ant\, wasp and termite colonies\, collective decision-making in ants and cockroaches\, collective motion in fish schools and human crowds and collective estimation in human groups. He has also coauthored five books\, among which Swarm Intelligence: From Natural to Artificial Systems (Oxford University Press\, 1999) and Self-organization in biological systems (Princeton University Press\, 2001) that are now considered as reference textbooks. In 2019\, he has been appointed visiting chair professor in Collective Behavior\, at the Indian Institute of Science in Bangalore by the Infosys Foundation.\n  \nWebpage: http://crca.cbi-toulouse.fr/en/guytheraulaz/\n \nThe Zoom Link will be sent the day before the lecture. (Contact communication@scioi.de for specific questions)
URL:https://www.scienceofintelligence.de/event/distinguished-speaker-series-guy-theraulazhost-pawel-romanczuk/
CATEGORIES:Distinguished Speaker Series
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DTSTART;TZID=Europe/Berlin:20200625T160000
DTEND;TZID=Europe/Berlin:20200625T180000
DTSTAMP:20260409T151138
CREATED:20200309T121608Z
LAST-MODIFIED:20250604T100103Z
UID:7262-1593100800-1593108000@www.scienceofintelligence.de
SUMMARY:Jacqueline Gottlieb (Hosted by Martin Rolfs): Curiosity and Information Demand: How We Can Study Them and Why We Should Care
DESCRIPTION:Curiosity and information demand: how we can study them and why we should care \nA rapidly growing literature has recently emphasized the importance of our sense-making instincts\, including complex investigative behaviors such as curiosity\, for behavior and brain function. While much of this literature has focused on simple forms of decision making\, we explored its significance for attention allocation. To make adaptive decisions in realistic multi-dimensional environments\, animals must infer the relevant features and afford them priority for the control of learning and actions. Prioritizing sources of information is the role of executive control and attention\, but its neural mechanisms are poorly understood. I will review studies of information demand suggesting that the fronto-parietal network encodes both optimal and non-optimal mechanisms for attentional prioritization. Converging evidence shows that parietal neurons encode prior uncertainty and likelihood\, two quantities required for Bayesian prioritization that maximizes the reduction in uncertainty. However\, the neurons also encode priority based on hedonic stimulus-reward associations\, in ways that seem non-optimal and interfere with the reduction of uncertainty. Continued studies of information demand will shed light on the vital question of how animals make complex decisions in realistic settings – specifically\, how they endogenously assign salience or priority to competing sources of information that subsequently control learning and actions. \nJacqueline Gottlieb\, Department of Neuroscience\, The Kavli Institute for Brain Science\, The Zuckerman Institute for Mind Brain and Behavior\, Columbia University\, New York\, NY\, 10032\, www.gottlieblab.com \nThe Zoom Link will be sent the day before the lecture. (Contact communication@scioi.de for specific questions) \n  \n(Photo by JR Korpa on Unsplash)
URL:https://www.scienceofintelligence.de/event/distinguished-speaker-series-jacqueline-gottlieb/
LOCATION:On ZOOM (Contact us for Link)
CATEGORIES:Distinguished Speaker Series
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