Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence

2025-05-15

AI Summary

Current multimodal fusion methods in AI predominantly employ static weighting strategies, overlooking the neuroscientific principle of inverse effectiveness—the phenomenon wherein weaker unimodal cues elicit stronger multimodal integration gains. To address this, we propose Inverse-Effectiveness-Driven Multimodal Fusion (IEMF), the first approach to formalize this principle as a learnable, differentiable dynamic fusion mechanism that adaptively modulates integration strength based on unimodal confidence estimates. The IEMF module integrates attention mechanisms, dynamic gating, and cross-modal confidence estimation, and is compatible with both artificial neural networks (ANNs) and spiking neural networks (SNNs). Evaluated on audio-visual classification, continual learning, and multimodal question answering, IEMF significantly enhances model robustness—particularly under noisy or sparse inputs—while reducing computational overhead by up to 50%. Moreover, it demonstrates strong generalization across diverse tasks and modalities.

Technology Category

Machine Learning: Multimodal LearningIntelligent Robots: Multimodal Perception & Sensor FusionComputer Vision: Multi-modal Vision

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSearch and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAGWeb Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Multimodal learning enhances the perceptual capabilities of cognitive systems by integrating information from different sensory modalities. However, existing multimodal fusion research typically assumes static integration, not fully incorporating key dynamic mechanisms found in the brain. Specifically, the brain exhibits an inverse effectiveness phenomenon, wherein weaker unimodal cues yield stronger multisensory integration benefits; conversely, when individual modal cues are stronger, the effect of fusion is diminished. This mechanism enables biological systems to achieve robust cognition even with scarce or noisy perceptual cues. Inspired by this biological mechanism, we explore the relationship between multimodal output and information from individual modalities, proposing an inverse effectiveness driven multimodal fusion (IEMF) strategy. By incorporating this strategy into neural networks, we achieve more efficient integration with improved model performance and computational efficiency, demonstrating up to 50% reduction in computational cost across diverse fusion methods. We conduct experiments on audio-visual classification, continual learning, and question answering tasks to validate our method. Results consistently demonstrate that our method performs excellently in these tasks. To verify universality and generalization, we also conduct experiments on Artificial Neural Networks (ANN) and Spiking Neural Networks (SNN), with results showing good adaptability to both network types. Our research emphasizes the potential of incorporating biologically inspired mechanisms into multimodal networks and provides promising directions for the future development of multimodal artificial intelligence. The code is available at https://github.com/Brain-Cog-Lab/IEMF.

Problem

• Dynamic multimodal fusion lacks brain-inspired inverse effectiveness mechanisms
• Existing methods assume static integration, limiting robust cognition
• Proposing IEMF strategy to enhance efficiency and performance

Innovation

• Brain-inspired inverse effectiveness fusion strategy
• Dynamic multimodal integration in neural networks
• Reduced computational cost by 50%