Community Event

Community Event – Universality and Idiosyncrasy of Perceptual Representations

Community Event

Wednesday, August 13, 10:00 am - 12:00 pm, Room TBA

Universality and Idiosyncrasy of Perceptual Representations

Ev Fedorenko Nikolaus Kriegeskorte

Moderators: Evelina Fedorenko¹, Nikolaus Kriegeskorte²; ¹Massachusetts Institute of Technology, ²Columbia University

Mick Bonner Eghbal Hosseini Brian Cheung

Proponents: Mick Bonner¹, Eghbal Hosseini², Brian Cheung²; ¹Johns Hopkins University, ²Massachusetts Institute of Technology

Jenelle Feather Alex Williams Tal Golan

Critics: Jenelle Feather¹, Alex Williams², Tal Golan³; ¹Carnegie Mellon University, ²Flatiron Institute and New York University, ³Ben-Gurion University of the Negev

Abstract

One of the premises of current cognitive computational modeling is that not all neural network models are equally aligned with the neural circuit under investigation. Models trained on different tasks or datasets or employing different architectures acquire distinct representations, and the idiosyncratic aspects of these representations (i.e., model-specific features) vary in their alignment with biological representations. This premise motivates the systematic benchmarking of various neural networks against the brain, with the aim of approaching brain-aligned models. However, some recent studies suggest the opposite: distinct neural networks learn the same representations. Furthermore, according to the “universal representation hypothesis,” components of representations shared across neural networks are also shared with humans, whereas any idiosyncratic components—specific to individual models—are not shared with humans. The implications are significant: If the “universal representation hypothesis” holds, model comparison is futile. This event brings together proponents and critics of the universal representation hypothesis. Together, we will consider the following questions: Are neural network representations universal or do they also have nonshared features that reflect the architecture, objective, or learning rule? Are features not shared among artificial neural network representations necessarily misaligned with the brain? What empirical tests would adjudicate this debate? And what should cognitive computational neuroscience look like under each hypothesis?

Session Plan

Do all neural network models converge to a universal representation, or do their internal representations differ in ways that are meaningful for understanding the brain? This session will explore the universality and idiosyncrasy of representations in artificial and biological neural systems. The moderators will provide the necessary scientific background and define the core questions at the heart of the debate. These questions will be addressed through a series of short talks presenting contrasting perspectives, followed by a panel discussion and open audience Q&A.

Participants will gain a clearer understanding of what is meant by universal and idiosyncratic representations, why this distinction matters for cognitive computational neuroscience, and how it may shape future research. The session will examine whether model-specific components are necessarily misaligned with the brain and explore empirical criteria for adjudicating between the universality and idiosyncrasy hypotheses.

Community Event – The Algonauts Project 2025 Challenge

Community Event

Time and Room TBA

The Algonauts Project 2025 Challenge

Alessandro Gifford, Domenic Bersch, Marie St-Laurent, Basile Pinsard, Julie Boyle, Lune Bellec, Aude Oliva, Gemma Roig, Radoslaw Cichy

The Algonauts Project, first launched in 2019, is on a mission to bring biological and machine intelligence researchers together on a common platform to exchange ideas and pioneer the intelligence frontier. Inspired by the astronauts’ exploration of space, “algonauts” explore human and artificial intelligence with state-of-the-art algorithmic tools, thus advancing both fields.

The Algonauts Project 2025 challenge focuses on predicting responses in the human brain as participants perceive complex multimodal naturalistic movies. To enable data-hungry modeling, the challenge runs on data from CNeuroMod (https://www.cneuromod.ca/), the largest suitable brain dataset available–almost 80 hours of neural recordings for each of 4 human participants. To ensure the robustness and relevance of results, the challenge features a model selection process based on out-of-distribution evaluation.

During the first part of this session, the Algonauts project and challenge will be introduced, followed by talks by this year’s challenge winners. The second part of the session will be a panel discussion on challenges in cognitive computational neuroscience moderated by Alessandro Gifford, including the participation of Andreas Tolias, Fabian Sinz, Martin Schrimpf, Lune Bellec, and Radoslaw Cichy. The audience participation in the panel discussion is encouraged through both in-person contributions or digital engagement.

More information at https://algonautsproject.com/

Community Event – Conversations on Consciousness: How the CCN Community Can Contribute

Community Event

Wednesday, August 13, 10:00 am - 12:00 pm, Room TBA

Conversations on Consciousness: How the CCN Community Can Contribute

Paul Linton¹, Megan Peters², Steve Fleming³, Lars Muckli⁴; ¹Columbia University, ²University of California, Irvine, ³University College London, ⁴University of Glasgow

Abstract

The CCN Community works on a diverse set of topics from perception to cognition to action. But one question that has been relatively overlooked at CCN is consciousness or subjective experience. Our Community Event explores why this is, and how to address it. The key question is how we should think about consciousness in computational terms. This topic has recently come to the fore with discussions of consciousness in AI, but our focus is the human brain: what kinds of computations appear to correlate with consciousness, and how can we model them? But also, how can we be sure we’re tracking consciousness in the first place? Our event will focus on the progress made in three ongoing Templeton adversarial collaborations. But this Community Event will also be a critical evaluation of recent developments in consciousness science, and we ask the CCN Community to reflect on what we might have missed along the way.

Session Plan

Whilst the topics many of us study lend themselves to thinking about consciousness, we believe there are three reasons why consciousness has not played a larger role at CCN, all of which our Community Event seeks to address:

1. Theories: First, we may feel consciousness science is its own distinct subfield, with its own specialized knowledge. So, the first focus of our Community Event is educational: to bring the CCN Community up to speed with recent developments in consciousness science, so that all will be better equipped to engage critically.

2. Computational Models: Second, we may feel that computational approaches have little to say about consciousness. So, the second focus of our Community Event is computational: to highlight existing computational frameworks for consciousness, and draw on the expertise of the CCN Community to develop new ways of thinking about consciousness in computational terms.

3. Experiments: Third, we may feel that collaborations between Cognitive Science, Neuroscience, and Artificial Intelligence in the context of consciousness science are already catered to by Templeton adversarial collaborations on consciousness. But CCN is uniquely placed to inform these collaborations, and to inform and participate in new lines of research they may inspire. So, the third focus of our Community Event is collaborative: presenting the work of three ongoing Templeton collaborations, and opening the discussion to the CCN Community with the aim of informing future work.

Community Event – Naturalistic Games…: A Project Co-Design Workshop

Community Event

Wednesday, August 13, 10:00 am - 12:00 pm, Room TBA

Naturalistic Games as a Benchmark to Bridge Cognitive Science, Computational Neuroscience and AI: A Community Led, Round-Table Discussion

Jascha Achterberg¹, Laurence Hunt¹, Chris Summerfield¹, Anna Székely²; ¹University of Oxford, ²Budapest University of Technology and Economics

Abstract

In this round-table workshop, we will discuss video games as a potential testbed for comparing biological and artificially intelligent behaviour. Video games capture much of the complexity of real-world decision tasks, such as vast state spaces, multi-step action sequences, interactions with objects and agents, and dynamic interleaving of planning and execution. Yet crucially, they also present an experimentally and computationally tractable testbed which allows for experimental manipulations, and comparison of human vs. machine behaviour and internal computations. We will have short talks from researchers currently using video games as a tool for understanding human and artificial cognition. One central aim will be to identify robust, reliable benchmarks with which human and artificial agents can be compared. The main outcome of this workshop, if successful, would be a co-designed project that is shaped by input from across the CCN community, where resulting data analysis/modelling is shared across a number of labs.

Session Plan

Our workshop will begin with an opportunity for participants to give ‘pitch talks’ (30-45 minutes) in which they pitch an idea about games, benchmarks, or current ongoing research that they consider relevant. If you are attending CCN and would like to be considered for a pitch talk, please fill out our online form.

Participants will then be split into breakout groups (~45 minutes), to discuss the following questions:

What constitutes a useful benchmark against which to evaluate human behavioural and/or neural data during naturalistic gameplay?
What unique questions in cognitive science/neuroscience/AI might be addressed using naturalistic games that are difficult to address using traditional experimental design?
What computational models are most appropriate for comparison with human behavioural and neural data in studying naturalistic behaviour with games?

We will then reconvene for a collective discussion for the remaining time, and summarise how this might inform a future, co-designed data collection project.

Community Event – Representational Alignment (Re^3-Align Collaborative Hackathon)

Community Event

Thursday, August 14, 4:15 - 6:00 pm, Room TBA

Representational Alignment (Re^3-Align Collaborative Hackathon)

Brian Cheung Dota Tianai Dong Erin Grant Ilia Sucholutsky Lukas Muttenthaler Siddharth Suresh

Brian Cheung, Dota Tianai Dong, Erin Grant, Ilia Sucholutsky, Lukas Muttenthaler, Siddharth Suresh

Abstract

Both natural and artificial intelligences form representations of the world that they use to reason, make decisions, and communicate. But how do we best compare and align these representations? There have been numerous debates around the measures used to quantify representational similarity. As of now, there is little consensus on which metric is best aligned(!) with the goal of identifying similarity between systems. This community event takes a hands-on approach to this challenge through a collaborative hackathon that centers on a decades-long debate: How universal vs. variable are the representations that intelligence systems, biological and artificial, form about the world? This debate has been the target of much recent research in representation learning in machine learning, and is also receiving substantial new attention from neuroscience and cognitive science. During the hackathon, Blue Teams will work to show model universality by finding (or creating) large populations of heterogeneous models that exhibit a high degree of representational alignment. Red Teams will highlight model variability by identifying differences in representations among homogeneous populations of models that are expected to align. The event will open with an interactive, hands-on tutorial on evaluating representational similarity, and will end with summative presentations from the winning teams and an engaging panel discussion with invited speakers.

Session Plan

This collaborative hackathon has two primary objectives: (1) to increase the reproducibility of research in representational alignment to establish shared knowledge through participation in the hackathon, and (2) to facilitate open discussion around identifying the most useful ways of measuring and controlling representational similarity, via presentations and panel discussion. The event begins with an interactive tutorial where organizers will provide starter code and demonstrate essential techniques—extracting model activations, identifying the similarities across different models, and analyzing stimuli that reveal meaningful differences between model representations. Next, the winning teams from the previous hackathon stage will showcase their methods and findings. After these presentations, participants will form new teams for the upcoming hackathon phase. The event concludes with an expert panel exploring challenges and future directions in representational alignment.

Community Events

Naturalistic Games as a Benchmark to Bridge Cognitive Science, Computational Neuroscience and AI: A Community Led, Round-Table Discussion

Wednesday, August 13, 10:00 am - 12:00 pm, Room TBA
Organizers: Laurence Hunt, Jascha Achterberg, Chris Summerfield, Anna Szekely