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Aligning the Brain with Language Models Through a Nonlinear and Multimodal Approach

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.7Download Report PDF
neurosciencefMRIencoding model
Abstract:

Self-supervised language and audio models effectively predict brain responses to speech. However, while nonlinear approaches have become standard in vision encoding, speech encoding models still predominantly rely on linear mappings from unimodal features. This linear approach fails to capture the complex integration of auditory signals with linguistic information across widespread brain networks during speech comprehension. Here, we introduce a nonlinear, multimodal prediction model that combines audio and linguistic features from pre-trained models (e.g., Llama, Whisper). Our approach achieves a 17.2% and 17.9% improvement in prediction performance (unnormalized and normalized correlation) over traditional unimodal linear models, as well as a 7.7% and 14.4% improvement over prior state-of-the-art models relying on weighted averaging of linear unimodal predictions. These substantial improvements not only represent a major step towards future robust in-silico testing and improved decoding performance, but also reveal distributed multimodal processing patterns across the cortex that support key neurolinguistic theories including the Motor Theory of Speech Perception, Convergence-Divergence Zone model, and embodied semantics. Overall, our work highlights the often neglected potential of nonlinear and multimodal approaches to speech encoding, paving the way for future studies to embrace these strategies in naturalistic neurolinguistics research.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces a nonlinear multimodal encoding model that combines audio and linguistic features from pre-trained models (Llama, Whisper) to predict brain responses to naturalistic speech. It resides in the 'Nonlinear Multimodal Prediction Models' leaf, which contains only three papers total, indicating a relatively sparse but emerging research direction. This leaf sits within the broader 'Multimodal Feature Integration for Brain Encoding' branch, which also includes linear and weighted averaging approaches as well as language model feature representation studies, suggesting the field is actively exploring different integration strategies.

The taxonomy reveals that the paper's immediate neighbors include linear and weighted averaging approaches (a separate leaf with two papers) and language model feature representation studies (another leaf with two papers). Beyond this branch, the field encompasses temporal dynamics research (acoustic/linguistic coupling, multi-timescale modeling, oscillatory tracking), spatial organization studies (connectivity, regional specialization), and predictive processing frameworks. The paper's focus on nonlinear integration distinguishes it from the linear methods in adjacent leaves, while its use of pre-trained models connects it to the language model feature representation work, though that leaf emphasizes architecture comparisons rather than nonlinear integration.

Among the three contributions analyzed, the first two—the nonlinear multimodal encoding model and the demonstration of nonlinear multimodal interactions—each examined ten candidates and found one refutable prior work, suggesting some overlap with existing literature within the limited search scope of thirty total candidates. The third contribution, RED-based clustering analysis for spatiotemporal tracking, examined ten candidates with none appearing to refute it, indicating this methodological component may be more novel. The analysis explicitly notes this is based on top-K semantic search plus citation expansion, not an exhaustive review, so these findings reflect the most semantically similar work rather than the entire field.

Given the limited search scope and the sparse population of the target leaf (three papers), the work appears to advance an emerging direction in brain encoding research. The contribution-level statistics suggest the core modeling approach has some precedent among the thirty candidates examined, while the spatiotemporal analysis method shows less overlap. The taxonomy structure indicates this sits at the intersection of multiple active research threads—multimodal integration, nonlinear modeling, and naturalistic speech processing—where methodological innovation is ongoing.

Taxonomy

Core-task Taxonomy Papers
39
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: predicting brain responses to naturalistic speech from multimodal features. The field has organized itself around several complementary perspectives. One major branch focuses on multimodal feature integration for brain encoding, exploring how acoustic, visual, and linguistic cues combine to drive neural activity—ranging from linear models to more sophisticated nonlinear architectures. A second branch examines neural tracking and temporal dynamics, investigating how the brain follows speech at multiple timescales and how oscillatory mechanisms support comprehension. Spatial and network-level organization studies map where different features are processed across cortical regions, while predictive processing frameworks ask how context and expectation shape neural responses. Additional branches address clinical and special populations (e.g., cochlear implant users, individuals with autism or schizophrenia), methodological advances including new datasets and recording techniques, multimodal interaction effects such as audiovisual occlusion, and even behavioral modeling of listener agents that simulate human-like responses. Within the multimodal integration branch, a particularly active line of work contrasts linear versus nonlinear prediction models. Earlier efforts often relied on additive or simple weighted combinations of features, but recent studies reveal that nonlinear interactions—captured by neural networks or kernel methods—can substantially improve encoding accuracy. Nonlinear Brain Language Alignment[0] sits squarely in this nonlinear modeling cluster, emphasizing how deep architectures better capture the complex, context-dependent mappings between multimodal inputs and brain signals. It shares methodological kinship with Nonlinear Multimodal Gap[10], which similarly highlights the limitations of linear assumptions, and contrasts with more traditional approaches that treat modalities as independent additive components. Meanwhile, neighboring work such as Multimodal Seq2Seq Transformer[20] explores sequence-to-sequence architectures for similar prediction tasks, underscoring an ongoing shift toward flexible, data-driven models that can learn intricate feature interactions directly from naturalistic stimuli.

Claimed Contributions

Nonlinear multimodal encoding model for naturalistic speech

The authors introduce a nonlinear encoding model that combines audio features from Whisper and semantic features from language models like Llama using a single-hidden-layer MLP with PCA preprocessing. This approach achieves substantial improvements (17.2% and 17.9% in unnormalized and normalized correlation) over traditional linear unimodal baselines and reveals distributed multimodal processing patterns across the cortex.

10 retrieved papers
Can Refute
Demonstration that nonlinear multimodal interactions drive encoding improvements

The authors systematically compare linear models, reduced-rank linear models (MLLinear), and delayed interaction MLPs (DIMLP) to isolate the contribution of nonlinearity versus dimensionality reduction. They show that linear models fail to capture complex interactions between audio and language information, whereas nonlinear encoders model these interactions more effectively with fewer parameters.

10 retrieved papers
Can Refute
RED-based clustering analysis for spatiotemporal neural response tracking

The authors propose Relative Error Difference (RED) as a metric that preserves temporal dynamics alongside spatial patterns, enabling joint analysis of spatiotemporal organization. This approach achieves superior functional clustering compared to linear encoders and standard connectivity analysis, revealing previously hidden patterns of brain organization and language processing dynamics.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Nonlinear multimodal encoding model for naturalistic speech

The authors introduce a nonlinear encoding model that combines audio features from Whisper and semantic features from language models like Llama using a single-hidden-layer MLP with PCA preprocessing. This approach achieves substantial improvements (17.2% and 17.9% in unnormalized and normalized correlation) over traditional linear unimodal baselines and reveals distributed multimodal processing patterns across the cortex.

Contribution

Demonstration that nonlinear multimodal interactions drive encoding improvements

The authors systematically compare linear models, reduced-rank linear models (MLLinear), and delayed interaction MLPs (DIMLP) to isolate the contribution of nonlinearity versus dimensionality reduction. They show that linear models fail to capture complex interactions between audio and language information, whereas nonlinear encoders model these interactions more effectively with fewer parameters.

Contribution

RED-based clustering analysis for spatiotemporal neural response tracking

The authors propose Relative Error Difference (RED) as a metric that preserves temporal dynamics alongside spatial patterns, enabling joint analysis of spatiotemporal organization. This approach achieves superior functional clustering compared to linear encoders and standard connectivity analysis, revealing previously hidden patterns of brain organization and language processing dynamics.

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