The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies

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

REVIEW 4 major objections 5 minor 6 cited by

The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper introduces a brain-encoding challenge whose central claim is that the best models of the brain are those that generalize beyond their training distribution, and it operationalizes that claim by selecting the winner solely on…

desk verdict A well-specified challenge announcement, not a results paper; the OOD leaderboard may be too noisy to crown a reliable winner, but that is a fixable design gap, not a fatal flaw. read the letter →

arxiv 2501.00504 v2 pith:E4EN5GUO submitted 2024-12-31 q-bio.NC

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

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Radoslaw M. Cichy

This is my paper · ORCID

classification q-bio.NC

keywords brainencodingmodelsout-of-distributiongeneralizationmultimodalmoviesnaturalisticstimulilarge-scalefMRIcommunitybenchmarkmoviewatching

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces a community competition for building models of how the brain responds to movies, and states the central goal: encoding models should predict whole-brain fMRI responses to naturalistic, multimodal stimulation and keep working on stimuli outside their training distribution. To make that goal testable, the challenge supplies almost 80 hours of movie-watching fMRI data per subject, including sitcom episodes and feature films, together with visual, audio, and transcript tracks. Participants train on six seasons of the sitcom and four films, then submit predicted brain responses for a held-out seventh season with unlimited attempts and for two hours of unrevealed out-of-distribution movies with at most ten attempts. The winning models are chosen solely on the out-of-distribution scores. The baseline encoding model scores r=0.20 in-distribution but only r=0.09 out-of-distribution, so the paper's design makes the generalization gap the explicit target.

What carries the argument

The central mechanism is a two-phase, two-leaderboard evaluation split. In the six-month building phase, models train on 55 hours of the sitcom and 10 hours of four films, and are tested on the held-out subsequent season with unlimited submissions; this gives in-distribution performance. In the one-week selection phase, models predict brain responses to two hours of withheld movies from outside that distribution, with a maximum of ten submissions, and winners are ranked solely on this out-of-distribution score. Scoring is done by averaging Pearson correlation over the 1,000 functionally defined brain parcels, then over out-of-distribution movies or held-out episodes, then over the four subjects. The load-bearing baseline is a linearizing encoding model that maps extracted visual, audio, and language features to fMRI responses, achieving r=0.20 in-distribution and r=0.09 out-of-distribution.

What would settle it

Compute a feature-space distance, using a pretrained audio-visual or language model, between the withheld out-of-distribution movies and the sitcom and film training material; if that distance is no larger than the distance between two seasons of the same sitcom, the out-of-distribution set is not actually out-of-distribution and the challenge's main ranking would not measure generalization.

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Extended reading notes

Core claim

The paper's claim is that the next generation of brain encoding models should be multimodal and should be selected by their out-of-distribution generalization, and it offers a concrete way to measure that: the largest single-subject fMRI movie-watching dataset assembled so far, split so that training, in-distribution testing, and out-of-distribution testing draw on different content. An encoding model is any algorithm that maps movie stimuli, including visual frames, audio, and time-stamped language transcripts, to predicted fMRI activity in 1,000 cortical parcels for each of four subjects. The quality of a model is a single number: Pearson correlation between predicted and recorded responses, averaged across parcels, then across stimuli, then across subjects. The challenge is organized so that the winner cannot be chosen by repeated probing of the out-of-distribution test set; the out-of-distribution movies are revealed only in a one-week selection phase, and only ten submissions are allowed. The intended payoff is a transparent leaderboard that ranks models both on a familiar test from the same TV series and on a genuinely novel test, making out-of-distribution robustness part of what a good brain model means.

Load-bearing premise

The whole design rests on two hours of withheld movies being different enough from the training films, and reliable enough, that an out-of-distribution correlation score is a fair and stable ranking of model quality; if those movies are too similar, too noisy, or too short, the central test of generalization fails.

Editorial extensions

If this is right

Multimodal fusion of visual frames, audio, and transcripts becomes a prerequisite for top scores, since the stimuli and scoring combine all three modalities.
Unlimited in-distribution submissions let teams tune their models on a familiar test, while the ten-submission out-of-distribution cap makes the final ranking a test of generalization rather than leaderboard overfitting.
The single averaged correlation score makes models of any architecture directly comparable on identical data and identical preprocessing.
The indefinite post-challenge phase turns the challenge into a permanent public benchmark with separate in-distribution and out-of-distribution leaderboards.
If participants close the gap between the r=0.20 and r=0.09 baselines, that would demonstrate that data-hungry, end-to-end trained encoding models can generalize to new movies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A natural extension the paper gestures at but does not implement is disaggregating out-of-distribution scores by modality or by movie genre; such breakdowns could reveal exactly which stimulus properties models fail to capture.
The design implicitly predicts that in-distribution leaderboard ranks will not match out-of-distribution ranks; if they do match closely, selecting winners by out-of-distribution performance would add little information.
After the challenge, a decisive validity check would be to compare the out-of-distribution-selected winners against a fresh, never-revealed movie set; if their advantage evaporates, the two-hour out-of-distribution set was too small or too similar to the training data.
If the benchmark works, the same train-withhold-out-of-distribution structure could be adopted for other brain-encoding problems, for example predicting responses during tasks with active cognition rather than passive movie watching.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should know this is a benchmark specification, not a research paper with a falsifiable claim. What is actually new: the 2025 Algonauts edition moves to multimodal movie stimuli, provides roughly 80 hours of fMRI per subject from CNeuroMod, and makes out-of-distribution generalization the explicit winner-selection criterion. That combination is a genuine step beyond the 2021 and 2023 editions. The protocol is clearly described, the rules are sensible (no using withheld responses, one account, code release for top-3), and the development kit lowers the entry barrier. The baseline correlations (ID r=0.20, OOD r=0.09) give participants a reference point. Credit where due: the paper does not oversell; it presents the challenge as a platform and acknowledges that prediction and explanation are different things.

The soft spots are the ones you would expect from a design-only paper. Most importantly, the OOD leaderboard may be too noisy to rank models reliably. The selection phase uses only 2 hours of withheld movies, the baseline OOD correlation is 0.09, and the leaderboard is updated after each of up to ten submissions. With a noisy metric, selecting the best of ten attempts can become selection on noise. The manuscript gives no split-half reliability, bootstrap confidence intervals, or subject-wise breakdowns. Averaging over 1000 parcels and 4 subjects helps, but it does not automatically save the ranking. This is a real, testable risk, and the authors should either report a reliability estimate for the OOD score or reduce the number of selection submissions. It is not fatal to the challenge concept, but it would be embarrassing if the 2025 winner turned out to be a chance high draw.

Two minor points: the baseline is reported without any measure of variance, which is easy to fix, and the OOD stimuli are described only as "withheld movies"—readers cannot judge how far out of distribution they are. The authors probably keep the identity secret to prevent overfitting, but a post-hoc characterization would help.

Overall, this is a useful community resource that deserves a serious referee. The central design is sound; the OOD noise concern is legitimate but addressable. I would send it to review with the request that the authors add a reliability analysis for the OOD metric before publication. For a reading group on benchmarks and encoding models, it is a reasonable "maybe"—the paper itself is short and the real content is the challenge infrastructure.

Referee Report

4 major / 5 minor

Summary. This manuscript introduces the Algonauts Project 2025 challenge, which asks participants to build encoding models that predict fMRI responses to multimodal movie stimuli. The challenge uses the CNeuroMod dataset: four subjects, almost 80 hours of fMRI per subject, with training data from six seasons of Friends and a set of four movies (Movie10), an in-distribution test on season 7 of Friends, and a two-hour out-of-distribution test on withheld movies. Winners are selected solely on OOD performance during a one-week selection phase with at most ten submissions, after a six-month building phase with unlimited ID submissions. The paper describes the data, phases, rules, baseline model (a linearizing encoding model with visual, audio, and language features, reporting r=0.20 ID and r=0.09 OOD), development kit, and the scientific rationale for multimodal naturalistic stimulation and OOD generalization.

Significance. If the challenge runs as designed, it will provide the field with a large, open, multimodal fMRI encoding benchmark with a public leaderboard and an explicit OOD generalization test, which are valuable and complementary to existing initiatives such as Brain-Score and Sensorium. The choice of CNeuroMod data and the release of a development kit and automated scoring infrastructure are concrete strengths. The paper's central scientific promise, however, is that winners selected on the OOD score will be models that genuinely generalize beyond the training distribution; that promise depends on the statistical reliability of the OOD leaderboard, which the manuscript does not yet establish.

major comments (4)

[Model selection phase and Baseline model] The central claim that winners are selected 'solely based on their OOD performance' depends on the OOD score being reliable enough to rank models. The paper reports only r=0.09 for the baseline OOD score, with no error bars, split-half reliability, bootstrap confidence intervals, or subject-wise and parcel-wise variance. With 2 hours of OOD stimuli per subject and averaging over 1,000 parcels, the sampling variance of this mean correlation is non-negligible; the manuscript should quantify the reliability of the OOD leaderboard (e.g., split-half correlation across OOD movies, bootstrap CI on the baseline, or a noise ceiling estimate) and state a criterion for when differences between models are meaningful.
[Rules and Model selection phase] Because the OOD leaderboard is updated after each of the up to ten submissions, participants can choose their best of ten scores on the same small OOD set. If the OOD metric is as noisy as the r=0.09 baseline suggests, this protocol selects on noise rather than on generalization. The paper should describe a safeguard, such as a final hold-out split of OOD data used only after the ten submissions are frozen, or a statistical test comparing the top submission against the baseline and against other top submissions.
[Baseline model] The baseline description is too underspecified to be reproduced: 'extracts visual, audio, and language features' does not state which features are used, how they are temporally aligned to the fMRI time series, what regression or regularization is applied, or whether the model is fit per subject and parcel. Since the baseline is the reference score against which all entries are judged, this omission weakens the scientific value of the benchmark and should be fixed by providing a precise specification or a link to the baseline code.
[Model selection phase] The term 'out-of-distribution' is used without defining the distribution shift. Because the OOD movies are unrevealed until the selection phase, participants cannot know the shift, but the organizers should specify what dimensions of shift are intended (e.g., new narrative content, different genres, new audiovisual statistics) and ideally provide a planned post-hoc measure of distribution shift to verify that the OOD set is actually outside the training distribution.

minor comments (5)

[Model building phase] The phrase 'all episodes of seasons 7 of the Friends dataset' should be 'season 7'.
[Discussion] The word 'premiating' should be replaced with 'rewarding' or 'prizing'.
[References] The two Richards et al. 2019 entries are identical; one duplicate should be removed.
[Data] The sentence 'Further information on the challenge stimuli and fMRI data is provided on the challenge data repository and development kit' lacks a URL or specific citation; please add the repository link.
[Figure 2c] The caption and text mention the maximum of ten submissions in the model selection phase, but the figure should visually indicate this cap to avoid ambiguity.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this challenge specification paper contains no derivation whose output reduces to its inputs, and the baseline scores are reference values, not predictions.

full rationale

The paper is a challenge specification, not a derivation-based study. It describes challenge data, phases, rules, and a baseline model, but it makes no scientific claim that is derived from fitted parameters. The baseline model is reported as a reference score (r = 0.20 in-distribution, r = 0.09 out-of-distribution) with the explicit status of a baseline in the 'Baseline model' section, not as evidence for an underlying theory. The OOD evaluation protocol is designed to avoid fitting to the withheld test set: the OOD stimuli are unrevealed until the model selection phase, and the winners are selected solely on OOD performance with a capped number of submissions. This is the opposite of a fitted-input-called-prediction pattern, since the withheld responses are not used to tune model parameters. The paper cites prior Algonauts challenge papers by overlapping authors, but these citations are contextual references to prior challenge editions and are not load-bearing for any central claim that requires independent validation; the current challenge is defined by its own new data, protocol, and benchmark structure. The skeptical concern that the two-hour OOD leaderboard may be too noisy to rank models reliably is a testable design risk about benchmark validity, not a circularity in the reasoning. Because there is no derivation chain whose outputs are equivalent to its inputs by construction, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper contains no mathematical derivation, so the ledger records the design assumptions on which the validity of the challenge depends. These are assumptions about data representativeness, evaluation metrics, and the OOD protocol, not free parameters.

assumptions (4)

domain assumption fMRI responses in 1,000 Schaefer parcels, normalized to MNI, are a sufficient and meaningful target for encoding model evaluation.
Invoked in the Data section and Figure 2b, where the prediction target is defined as parcel-averaged BOLD time series.
domain assumption Averaging Pearson correlations across parcels, then movies, then subjects produces a meaningful model ranking.
Invoked in the scoring descriptions for the model building and model selection phases; no validation is provided that this average is not dominated by a few parcels.
domain assumption The withheld OOD movie set is sufficiently out-of-distribution and sufficiently long to rank models on generalization.
Invoked in the Model selection phase section; OOD movies are unrevealed until selection, but no quantitative distribution shift is documented.
domain assumption The provided training set, 55 hours of Friends seasons 1 to 6 plus 10 hours of Movie10, is large and representative enough to train generalizable multimodal encoding models.
Invoked in the Model building phase section, where these data are offered as the training basis for all participants.

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Pith. "Pith review of The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies." pith.science (2026). https://pith.science/paper/E4EN5GUO

@misc{pith2026250100504,
  author       = {Pith},
  title        = {Pith review of: The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/E4EN5GUO}},
  note         = {Machine review of arXiv:2501.00504}
}

read the original abstract

There is growing symbiosis between artificial and biological intelligence sciences: neural principles inspire new intelligent machines, which are in turn used to advance our theoretical understanding of the brain. To promote further collaboration between biological and artificial intelligence researchers, we introduce the 2025 edition of the Algonauts Project challenge: How the Human Brain Makes Sense of Multimodal Movies (https://algonautsproject.com/). In collaboration with the Courtois Project on Neuronal Modelling (CNeuroMod), this edition aims to bring forth a new generation of brain encoding models that are multimodal and that generalize well beyond their training distribution, by training them on the largest dataset of fMRI responses to movie watching available to date. Open to all, the 2025 challenge provides transparent, directly comparable results through a public leaderboard that is updated automatically after each submission to facilitate rapid model assessment and guide development. The challenge will end with a session at the 2025 Cognitive Computational Neuroscience (CCN) conference that will feature winning models. We welcome researchers interested in collaborating with the Algonauts Project by contributing ideas and datasets for future challenges.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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A transformer-based encoder that combines text, audio, and video embeddings predicts whole-brain fMRI responses to movies across subjects and won the Algonauts 2025 competition.
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A two-stage multimodal Transformer with fixed pretrained feature extractors predicts fMRI activity from movies with Pearson r = 0.32 (in-domain) and 0.21 (out-of-domain), beating the challenge baseline by about 0.12.

Reference graph

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Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions. PLOS Biol. 21, e3002366. https://doi.org/10.1371/journal.pbio.3002366 Turishcheva, P., Fahey, P.G., Hansel, L., Froebe, R., Ponder, K., Vystrčilová, M., Willeke, K.F., Bashiri, M., Wang, E., Ding, Z., Tolias, A.S., S...

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Reviewed August 10, 2026 · model on record in the stance chip above.

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work page Pith review arXiv doi:10.48550/arxiv.2305.19654