We are excited to announce the below Workshops for RLDM 2025:
- Chess as a Bridge Between Human Cognition and Artificial Intelligence
- Representational Codes and Learning Rules: How Neuroscience Informs RLDM
- Saving the Phenomena of Minds
- PIMBAA: Principles for Intrinsic Motivations in Biological and Artificial Agents
- Reinforcement Learning “In the Wild”: Modelling Real-World Data
- Representational Alignment and Aging
- Reinforcement Learning as a Model of Social Behavior and Inference: Progress and Pitfalls
- Game On: Leveraging Gamification to Understand Cognitive and Computational Mechanisms
Please continue reading for further details on each workshop.
Chess as a Bridge Between Human Cognition and Artificial Intelligence
This workshop will bring together researchers using chess to understand human cognition and build state-of-the-art artificial intelligence systems. Topics will include: (1) superhuman chess play and the mechanistic interpretability of artificial intelligence that can do so, (2) algorithms that are specifically designed to be more human-like in their performance, and (3) characterizing human behavior in chess and similar two-player combinatorial games.
Organisers: Ionatan Kuperwajs, Evan M. Russek.
Representational Codes and Learning Rules: How Neuroscience Informs RLDM
This workshop will bring together researchers at the intersection of representations and learning algorithms who draw on neuroscience techniques or theory to advance our understanding of learning and decision making. Confirmed speakers will present experiments that weave together cutting-edge approaches from neuroscience (intracranial electrophysiology, fMRI, pharmacology, and PET), artificial intelligence, and cognitive psychology, each answering the guiding questions of the workshop: What did we learn about representations and learning rules from these approaches? And what new theoretical questions do these experiments raise?
Organisers: Ian Ballard, Angela Radulescu.
Saving the Phenomena of Minds
“Saving the phenomena” is an expression that describes when a theory adequately captures empirical aspects of the world it intends to model. In other words, the theory does reality justice. In many respects, the sciences in attendance at RLDM aim to model and explain the phenomena of minds. This workshop asks: how the theoretical framework of RL, implemented in decision sciences, saves the phenomena of minds?
Website for Saving the Phenomena of Minds workshop
Organisers: Michael Dennis, Aniek Fransen, John D. Martin, Shruti Mishra, Prabhat Nagarajan, Guy Davidson, Aditya A. Ramesh, Noam Goldway.
PIMBAA: Principles for Intrinsic Motivations in Biological and Artificial Agents
The goal of this workshop is to bring together experts in intrinsic motivations across cognitive sciences, neuroscience, developmental science, robotics and AI. Each of these fields uses reinforcement learning to understand, model, or generate good decisions. Critically, concurrent work in all of these fields is beginning to demonstrate that the objectives that give rise to good decisions are richer than maximizing extrinsic reward. Long-standing disciplinary divides mean that there is currently a zoo of different intrinsic motivation frameworks and little in the way of organizing principles that would be useful for understanding natural agents or building better artificial ones. Because these experts all attend RLDM, this workshop would take advantage of this rare opportunity to coalesce the community around a core understanding of the principles of intrinsic motivations.
Organisers: R. Becket Ebitz, Franziska Brändle, Jorge Ramírez-Ruiz, Surabhi S. Nath.
Reinforcement Learning “In the Wild”: Modelling Real-World Data
This workshop will bring together experts from psychology, computer science, neuroscience, and psychiatry to discuss open questions in this emerging field, including what types of real-world data are amenable to RL modelling, what adjustments are required to adapt existing frameworks to the complexity and variability of real-world data, and what opportunities exist for advancing AI agents and mental health research.
Website for RL “In the Wild” workshop
Organisers: Dan Mircea-Mirea, Georgia Turner, Ana da Silva Pinho.
Representational Alignment and Aging
This workshop aims to bridge the gap between the representation of socioemotional functions in natural and artificial intelligence by bringing together experts across diverse interdisciplinary perspectives (e.g., computational and/or affective neuroscience, computer science). First, we will introduce several key challenges of representational alignment of socioemotional function between human and artificial agents. We also highlight several avenues that may be fruitful for moving the needle toward building a clearer understanding of how agents select and prioritize goals to optimize their decision-making across the lifespan.
Organisers: Debbie Yee, Angela Radulescu, Robert Wilson.
Reinforcement Learning as a Model of Social Behavior and Inference: Progress and Pitfalls
In this workshop, we will provide a broad overview of the diverse tools and theoretical frameworks employed to model social behavior by drawing on recent advances in computational neuroscience, behavioral economics, social psychology, cognitive science, and computer science. A major theme of this workshop is connecting across levels of analysis by emphasizing the mutual challenges of modeling social signals in the brain, in laboratory tasks, in naturalistic settings. By identifying the successes and challenges across these frameworks and methods we will facilitate interdisciplinary discussion around the promises of RL as a model of social reasoning, blinds spots and pitfalls, and promising future directions.
Organisers: Amrita Lamba, Joseph Barnby.
Game On: Leveraging Gamification to Understand Cognitive and Computational Mechanisms
Gamification of cognitive tasks has been increasingly popular in Cognitive Neuroscience and Computational Psychiatry research. This workshop aims to bring together experts from different fields to discuss possible advantages and drawbacks of using games to study the mind.
Organisers: Luianta Verra, Ondrej Zika, Ingrid Martin
WHAT ARE RLDM WORKSHOPS?
The goal of the RLDM workshops is to encourage interdisciplinary discussion and to provide an informal forum for researchers to discuss important research questions and challenges focused on a specific topic.
Click here to see workshops from RLDM 2022.
FREQUENTLY ASKED QUESTIONS
Q: Is there any travel funding available for workshop speakers?
RLDM will not be able to provide travel funding for workshop speakers. In previous years, some workshops have sought and received funding from external sources to bring in outside speakers and RLDM is open to that model. Organizers should list all funding sources in the proposal.
Q: Who should I contact with questions?
Please feel free to contact us at hanneke.denouden@donders.ru.nl and dmabel@deepmind.com if you have any questions!
Q: How will proposals be evaluated?
Workshop proposals will be evaluated and selected by the RLDM Workshop Chairs.
Evaluation will be based on the following metrics, in no particular order:
1. Relevance to the RLDM community
2. Interdisciplinary scope of the proposal
3. Current interest in the topic within the community
4. Diversity and equity in the proposed presenters/speakers/organizers
5. Potential for new research directions and interactions to emerge
Q: What if I want to get involved with a workshop proposal, but I don’t have co-organizers?
Please get in touch with us over email; we might be able to pair with you other possible co-organizers.
Q: How many workshops will there be?
Around six