不确定性下多模态仲裁的感觉精度推断
Sensory Precision Inference for Multimodal Arbitration under Uncertainty
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中文总结 AI 辅助
本文提出一种多模态感知推断模型，通过动态推断感觉精度并引入学习先验，在感觉冲突下实现稳健的潜在信念更新与模态仲裁，实验验证其提升重建鲁棒性和推断连贯性。
中文摘要 AI 辅助
基于多感官数据运行的自主智能体不能假设所有感觉模态始终保持信息一致性。在真实环境中，感觉流经常受到噪声、数据缺失或模态间不一致性的破坏，需要在竞争性的感觉假设之间进行自适应仲裁。虽然主动推断为不确定性引导的推断提供了原则性框架，但在感觉冲突下，动态推断的感觉精度在生成式多模态仲裁中的作用仍相对未被充分探索。我们提出了一种多模态感知推断模型，在该模型中，潜在信念和模态特定的感觉精度通过迭代自由能最小化共同更新。在我们提出的模型中，感觉精度动态不仅反映感觉不确定性，还在多模态冲突期间积极塑造潜在信念的演化。此外，我们引入了一个关于感觉精度的学习先验，该先验诱导出结构化的、类别依赖的精度模式，并影响跨模态推断动态。我们使用一个合成的多模态MNIST数据集评估该模型，该数据集结合了数字类别的视觉、听觉和触觉表示，并在受控的感觉噪声和模态间不一致性下进行测试。结果表明，动态精度推断在感觉证据受损的情况下提高了重建鲁棒性，支持从减少的感觉证据中进行连贯的潜在推断，并能够在冲突模态之间实现稳定仲裁。此外，学习到的精度先验生成可解释的精度结构，这些结构塑造推断动态和跨模态潜在结构。这些发现支持感觉精度推断作为不确定性下自适应多模态信念形成的机制性控制过程，突出了精度动态作为稳健且可解释的多感官整合的计算机制。
英文摘要
Autonomous agents operating on multisensory data cannot assume that all sensory modalities remain consistently informative. In real environments, sensory streams are frequently corrupted by noise, missing data, or inter-modal incongruence, requiring adaptive arbitration between competing sensory hypotheses. While active inference provides a principled framework for uncertainty-guided inference, the role of dynamically inferred sensory precision in generative multimodal arbitration under sensory conflict remains comparatively underexplored. We propose a multimodal perceptual inference model in which latent beliefs and modality-specific sensory precisions are jointly updated through iterative free-energy minimization. In our proposed model, sensory precision dynamics not only reflect sensory uncertainty but actively shape the evolution of latent beliefs during multimodal conflict. In addition, we introduce a learned prior over sensory precisions that induces structured, class-dependent precision patterns and influences cross-modal inference dynamics. We evaluate the model using a synthetic multimodal MNIST dataset combining visual, auditory, and tactile representations of digit classes under controlled sensory noise and inter-modal incongruence. Results show that dynamic precision inference improves reconstruction robustness under corrupted sensory evidence, supports coherent latent inference from reduced sensory evidence, and enables stable arbitration between conflicting modalities. Furthermore, learned precision priors generate interpretable precision structures that shape inference dynamics and cross-modal latent structure. These findings support sensory precision inference as a mechanistic control process for adaptive multimodal belief formation under uncertainty, highlighting precision dynamics as a computational mechanism for robust and interpretable multisensory integration.
发表机构
- The University of Osaka（大阪大学）
- IRCN, The University of Tokyo（东京大学国际神经智能研究中心）
机构由 AI 辅助整理，请以论文原文为准。
