L'Institut des sciences cognitives vous invite à une conférence de Patrck Girard qui aura lieu le 12 septembre prochain au local A-3740 du pavillon Hubert-Aquin à 14 heures.
En arrivant devant les portes barrées dans le couloir du A au 3e étage, vous devrez presser très fort la sonnette à droite de la porte afin qu'on vienne vous ouvrir.
Au plaisir de vous y voir!
L’ISC fusionne les concours de bourse de diffusion et de bourse de soutien à la recherche et accepte les demandes visant :
Jusqu’à 500 $ par demande.
Le concours est ouvert à l’année, mais les demandes doivent être déposées avant le 1er mars 2023 pour l’année universitaire en cours.
Ces bourses visent à soutenir l’avancement et la diffusion des recherches des étudiantes et étudiants des cycles supérieurs qui travaillent à l’UQAM en lien avec les sciences cognitives.
Les coûts liés à la diffusion ou à la collecte de données sont pour l’année universitaire en cours;
L’institut octroie, au maximum, une bourse par année par étudiant-e.
Dossier de présentation de la candidature
Le dossier doit contenir les documents énumérés ci-dessous, et doit être acheminé par courriel à isc@uqam.ca.
Les dossiers sont évalués selon les critères qui suivent :
Les candidatures sont évaluées et classées au mérite par le Comité de coordination de l’ISC dans les 15 jours ouvrables suivant la demande. La décision du comité d’évaluation est finale et sans appel.
Il n’existe pas de procédure de révision.
Par : Todd Gureckis
Résumé :
The ability to reason about the physics of our world (e.g., what arrangements of objects are stable, how things will fall or move under a force) is central to human intelligence. One influential hypothesis is that this capacity stems from the ability to perform “mental simulations” of physical events (in effect, playing a mental “movie” of the future evolution of a scene according to the laws of physics). In this talk, I’ll try to pin down several core commitments of the mental simulation approach that must be present for the general theory to be viable. I then will describe experiments we conducted recently trying to test these commitments. Along the way, we stumbled into several curious and novel errors and biases in human physical reasoning ability that we believe represent limits to the universality of contemporary simulation theories. If there is time, I will discuss a related project considering how efficient or optimal people are when they “experiment” in the physical world in order to learn the covert properties of objects such as mass or attractive/repulsive forces like magnetism.
Bio :
Todd M. Gureckis, Professor of Psychology, New York University, studies how people actively explore their world in order to learn, including everyday reasoning capacities for the physical and social world. His research combines methods of computational modeling, developmental psychology, cognitive neuroscience, and online data collection. He is the founder and a lead developer of the psiTurk package, a tool for facilitating online experiments used in hundreds of research labs. His work has been recognized by the NSF CAREER award, the Presidential Early Career Award (PECASE) from the Office of Science and Technology Policy at the White House, the James S. McDonnell Foundation Scholar award, and several paper and conferences awards with his students including the Marr Prize from the Cognitive Science Society, the Clifford T. Morgan Prize from the Psychonomic Society. He has variously served an Associate Editor for Cognitive Science, Topics in Cognitive Science, and Computational Brain and Behavior.
Références
https://gureckislab.org/ :
https://gureckislab.org/papers/#/ref/ludwin2021limits
https://gureckislab.org/papers/#/ref/ludwinpeery2020broken
https://gureckislab.org/papers/#/ref/bramley2018intuitive
Jeudi 15 décembre, 10 h 30.
Lien zoom : https://uqam.zoom.us/j/88481835073
IMPORTANT : connectez-vous au moins 10 à 15 minutes avant. Lors de la période de question, il est maintenant obligatoire d’ouvrir votre caméra et de poser vos questions directement et surtout PARTICIPER à la discussion.
Par : Karl Friston
Résumé :
This talk offers a formal account of insight and learning in terms of active (Bayesian) inference. It deals with the dual problem of inferring states of the world and learning its statistical structure. In contrast to current trends in machine learning (e.g., deep learning), we focus on how agents learn from a small number of ambiguous outcomes to form insight. I will use simulations of abstract rule-learning and approximate Bayesian inference to show that minimising (expected) free energy leads to active sampling of novel contingencies. This epistemic, curiosity-directed behaviour closes `explanatory gaps' in knowledge about the causal structure of the world, thereby reducing ignorance, in addition to resolving uncertainty about states of the known world. We then move from inference to model selection or structure learning to show how abductive processes emerge when agents test plausible hypotheses about symmetries in their generative models of the world. The ensuing Bayesian model reduction evokes mechanisms associated with sleep and has all the hallmarks of aha moments.
Bio :
Karl Friston is a theoretical neuroscientist and authority on brain imaging. He invented statistical parametric mapping (SPM), voxel-based morphometry (VBM) and dynamic causal modelling (DCM). Mathematical contributions include variational Laplacian procedures and generalized filtering for hierarchical Bayesian model inversion. Friston currently works on models of functional integration in the human brain and the principles that underlie neuronal interactions. His main contribution to theoretical neurobiology is a free-energy principle for action and perception (active inference). Friston received the first Young Investigators Award in Human Brain Mapping (1996), the Weldon Memorial prize and Medal in 2013 for contributions to mathematical biology, the 2016 recipient of the Charles Branch Award for unparalleled breakthroughs in Brain Research and the Glass Brain Award, a lifetime achievement award in the field of human brain mapping. He holds Honorary Doctorates from the University of Zurich and Radboud University.
A repository of active inference papers: GitHub - BerenMillidge/FEP_Active_Inference_Papers: A repository for major/influential FEP and active inference papers. Theoretical lecture on the physics behind active inference: I am therefore I think by Karl Friston - YouTube
Jeudi 8 décembre, 10 h 30.
Lien zoom : : https://uqam.zoom.us/j/88481835073
IMPORTANT : connectez-vous au moins 10 à 15 minutes avant. Lors de la période de question, il est maintenant obligatoire d'ouvrir votre caméra et de poser vos questions directement et surtout PARTICIPER à la discussion.
Présentation faite au 89e congrès de l'Acfas en mai dernier, par Elisabeth Doyon, doctorante en informatique cognitive à l'UQAM, Toufik Mechouma, doctorant en informatique cognitive à l'UQAM, et Julien Vallières, étudiant à la maîtrise en langue et littérature françaises, Université McGill.
Ces 3 membres juniors de l'ISC se sont mérités la bourse de valorisation de projets de 5000 $ de l'ISC, en 2021, pour ce projet : Soixante-trois ans de conférences philosophiques (1933-1995) : pilote d’extraction de corpus et d’indexation analytique par forage textuel des Annales de l’ACFAS.
Leur présentation au congrès de l'ACFAS est accessible sur le YouTube de l'ISC.
RACCOURCIS propose un guide vulgarisé et bilingue des biais cognitifs. Ce guide en ligne fait un tour d'horizon critique de la recherche effectuée dans plusieurs disciplines sur le thème des biais cognitifs et du raisonnement, en plus d'exposer les répercussions que ces biais peuvent engendrer.
Visitez le site pour voir le travail que fait l'équipe, et consultez les détails si vous êtes intéressé-e à contribuer à RACCOURCIS en écrivant une entrée sur un biais ou un raccourcis cognitif L'équipe vous invite aussi à t leur propositions d'appels de textes.
Par : Baptiste Caramiaux
Résumé :
Machine learning algorithms are present in many of the applications and services we use every day. These technologies are often designed in isolation from their users, leading to a standardisation of their uses and a centralised control of their capabilities. Creating learning technologies that are closer to people and their context of use opens up the possibility of more responsive, appropriable and inclusive interactions. In this talk, I will present the context and the research community working on these themes at the intersection between HCI and AI. Then I will focus on my work in this field. I will show examples of research where the artistic approach is sometimes seen as a tool to reflect on technologies as cultural actors, and sometimes seen as a tool to inspire the design of rich and expressive interactions. Finally, I will present concrete ways to design interactions with machine learning algorithms through the concept of Machine Teaching.
Bio :
Baptiste Caramiaux is a CNRS researcher at ISIR, Sorbonne Université in Paris, in the HCI Sorbonne group. He conducts research in human-computer interaction (HCI), studying and designing interactions with machine learning algorithms in the context of performing arts, health and pedagogy, engineering.
Jeudi 3 novembre, 10 h 30.
Lien zoom : https://uqam.zoom.us/j/88481835073
IMPORTANT : connectez-vous au moins 10 à 15 minutes à l’avance, et donnez votre nom complet pour nous faciliter la tâche de vous admettre au séminaire.
Lors de la période de question, nous vous invitons à ouvrir votre caméra et à poser vos questions directement.
Par : Tom Ziemke
Résumé :
People commonly attribute intentional mental states, such as beliefs and goals, to robots (Thellman et al., 2022; Ziemke, 2020). In a recent paper we formulated the perceptual belief attribution problem (Thellman & Ziemke, 2021): How can people interacting with robots understand what they know about the shared physical environment without knowing much about those robots’ sensors, perception, memory, etc.? In this talk I’ll focus on the observer’s grounding problem, which is the other side of the same coin, i.e., the fact that in interaction with a robot people tend to make anthropomorphic, folk-psychological attributions, based on their own grounding rather than the robot’s
Bio :
Tom Ziemke is Professor of Cognitive Systems at Linkoping University, Sweden. His main research interests are in situated/embodied cognition and social interaction, with a current focus on people’s interaction with different types of autonomous technologies, ranging from social robots to automated vehicles. A long-standing research interest is the relation between cognition and computation – and the resulting (mis-) conceptions of AI among both researchers and the general public
References:
Understanding robots https://www.science.org/doi/10.1126/scirobotics.abe2987
Explainability in Social Robotics https://doi.org/10.1145/3461781
Mental State Attribution to Robots https://doi.org/10.1145/3526112
Jeudi 17 novembre, 10 h 30.
Lien zoom : https://uqam.zoom.us/j/88481835073
IMPORTANT : connectez-vous au moins 10 à 15 minutes à l'avance, et donnez votre nom complet. Aussi, lors de la période de question, il est maintenant obligatoire d'ouvrir votre caméra. Posez vos questions directement mais surtout participez à la discussion.
Par : Christian Lebière
Résumé :
Cognitive architectures are computational implementations of unified theories of cognition. Being able to represent human cognition in computational form enables a wide range of applications when humans and machines interact. Using cognitive models to represent common ground between deep learners and human users enables adaptive explanations. Cognitive models representing the behavior of cyber attackers can be used to optimize cyber defenses including techniques such as deceptive signaling. Cognitive models of human-automation interaction can improve robustness of human-machine teams by predicting disruptions to measures of trust under various adversarial situations. Finally, the consensus of 50 years of research in cognitive architectures can be captured in the form of a Common Model of Cognition that can provide a guide for neuroscience, artificial intelligence and robotics.
Bio :
Christian Lebière is a Research Faculty member in the Psychology Department at Carnegie Mellon University. His main research interests are cognitive architectures and their applications to psychology, artificial intelligence, human-computer interaction, decision-making, intelligent agents, network science, cognitive robotics and neuromorphic engineering.
Références:
Cranford, E. A., Gonzalez, C., Aggarwal, P., Tambe, M., Cooney, S., & Lebiere, C. (2021). Towards a cognitive theory of cyber deception. Cognitive Science, 45(7), e13013.
Cranford, E., Gonzalez, C., Aggarwal, P., Cooney, S., Tambe, M., & Lebiere, C. (2020). Adaptive cyber deception: Cognitively informed signaling for cyber defense.
Lebiere, C., Blaha, L. M., Fallon, C. K., & Jefferson, B. (2021). Adaptive cognitive mechanisms to maintain calibrated trust and reliance in automation. Frontiers in Robotics and AI, 8, 652776.
Laird, J. E., Lebiere, C., & Rosenbloom, P. S. (2017). A standard model of the mind: Toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. AI Magazine, 38(4), 13-26.
Lebiere, C., Pirolli, P., Thomson, R., Paik, J., Rutledge-Taylor, M., Staszewski, J., & Anderson, J. R. (2013). A functional model of sensemaking in a neurocognitive architecture. Computational Intelligence and Neuroscience, 2013.
Jeudi 20 octobre, 10 h 30.
Lien zoom : https://uqam.zoom.us/j/88481835073
IMPORTANT : connectez-vous au moins 10 à 15 minutes à l’avance, et donnez votre nom complet pour nous faciliter la tâche de vous admettre au séminaire.
Lors de la période de question, nous vous invitons à ouvrir votre caméra et à poser vos questions directement.
Par : Agnieszka Wykowska
Résumé :
When predicting and explaining behavior of other humans, we adopt the intentional stance, and refer to mental states in order to understand others’ actions. However, it is not clear whether and when we adopt the intentional stance also towards artificial agents, such as humanoid robots. This talk will provide an overview of research conducted in my lab which addresses this question. I will present a tool for measuring adoption of the intentional stance. The likelihood of adopting the intentional stance is coded in specific patterns of neural activity at rest. Interactive scenarios influence adoption of the intentional stance more than mere observation of subtle human-like characteristics of a robot’s behavior. Experiments using interactive joint action protocols with a humanoid robot to study vicarious and joint sense of agency show that the robots’ motor repertoire and our ability to represent its actions with our own sensorimotor repertoire influence vicarious sense of agency. Embedding a non-verbal adaptation of a “Turing test” in a human-robot joint action task showed that human-like variability in the robot’s simple button presses makes the robot pass the test. The talk will conclude with a discussion of the role of the intentional stance and sense of agency in other mechanisms of social cognition, and their implications in applied domains of social robotics in healthcare.
Bio :
Professor Agnieszka Wykowska leads the unit “Social Cognition in Human-Robot Interaction” at the Italian Institute of Technology (Genoa, Italy). The research foci of Prof. Wykowska are interdisciplinary, bridging psychology, cognitive neuroscience, robotics and healthcare. She combines cognitive neuroscience methods with human-robot interaction to understand the human brain mechanisms in interaction with other humans and with robots. Her research is also dedicated to applications of social robotics to healthcare: her team develops robot-assisted training protocols to help children diagnosed with autism-spectrum disorder in improving social skills.
References:
Bossi, F., Willemse, C., Cavazza, J., Marchesi, S., Murino, V., Wykowska, A. (2020). The human brain reveals resting state activity patterns that are predictive of biases in attitudes towards robots. Science Robotics, 5:46, eabb6652: https://www.science.org/doi/10.1126/scirobotics.abb6652
Marchesi, S., De Tommaso, D., Perez-Osorio, J., Wykowska A. (2022). Belief in sharing the same phenomenological experience increases the likelihood of adopting the intentional stance towards a humanoid robot. Technology, Mind and Behavior, 3(3): https://www.apa.org/pubs/journals/releases/tmb-tmb0000072.pdf
Ciardo, F., De Tommaso, D., Wykowska, A. (2022). Human-like behavioural variability blues the distinction between a human and a machine in a nonverbal Turing test. Science Robotics, 7, eabo 1241: https://www.science.org/doi/10.1126/scirobotics.abo1241
Roselli, C., Ciardo, F., De Tommaso, D., Wykowska, A. (2022). Human‐likeness and attribution of intentionality predict vicarious sense of agency over humanoid robot actions. Nature: Scientific Reports, 12:13845: https://www.nature.com/articles/s41598-022-18151-6.pdf
Note: for the papers behind paywall, please visit our website, where you can find access links to all papers: https://instanceproject.eu/publications/list-of-publications
Jeudi 6 octobre, 10 h 30.
Lien zoom : https://uqam.zoom.us/j/88481835073
IMPORTANT : connectez-vous au moins 10 à 15 minutes à l’avance, et donnez votre nom complet pour nous faciliter la tâche de vous admettre au séminaire.
Lors de la période de question, nous vous invitons à ouvrir votre caméra et à poser vos questions directement.
