Alignment between human brain networks and artificial models is actively studied in machine learning and neuroscience. A widely adopted approach to explore their functional alignment is to identify metamers for both humans and models. Metamers refer to input stimuli that are physically different but equivalent within a given system. If a model's metameric space completely matched the human metameric space, the model would achieve functional alignment with humans. However, conventional methods lack direct ways to search for human metamers. Instead, researchers first develop biologically inspired models and then infer about human metamers indirectly by testing whether model metamers also appear as metamers to humans. Here, we propose the Multidimensional Adaptive Metamer Exploration (MAME) framework, enabling direct high-dimensional exploration of human metameric space. MAME leverages online image generation guided by human perceptual feedback. Specifically, it modulates reference images across multiple dimensions by leveraging hierarchical responses from convolutional neural networks (CNNs). Generated images are presented to participants whose perceptual discriminability is assessed in a behavioral task. Based on participants' responses, subsequent image generation parameters are adaptively updated online. Using our MAME framework, we successfully measured a human metameric space of over fifty dimensions within a single experiment. Experimental results showed that human discrimination sensitivity was lower for metameric images based on low-level features compared to high-level features, which image contrast metrics could not explain. The finding suggests that the model computes low-level information not essential for human perception. Our framework has the potential to contribute to developing interpretable AI and understanding of brain function in neuroscience.

翻译：人脑网络与人工模型之间的对齐是机器学习和神经科学领域积极研究的课题。探索二者功能对齐的一种广泛采用方法是识别人类和模型的同色异谱刺激。同色异谱指在物理上不同但在给定系统内等效的输入刺激。若模型的同色异谱空间完全匹配人类的同色异谱空间，则该模型可实现与人类的功能对齐。然而，传统方法缺乏直接探索人类同色异谱的途径。研究者通常先开发生物启发模型，然后通过测试模型同色异谱对人类是否同样呈现同色异谱特性，间接推断人类同色异谱。本文提出多维自适应同色异谱探索（MAME）框架，实现了对人类同色异谱空间的直接高维探索。MAME利用人类感知反馈引导的在线图像生成技术，具体通过卷积神经网络（CNN）的层级响应在多个维度上调制参考图像。生成的图像呈现给参与者，并通过行为任务评估其感知辨别能力。基于参与者的反应，系统在线自适应更新后续图像生成参数。运用MAME框架，我们在单次实验中成功测量了超过五十个维度的人类同色异谱空间。实验结果表明，基于低层特征生成的同色异谱图像比基于高层特征的图像具有更低的人类辨别敏感度，这一现象无法用图像对比度指标解释。该发现表明模型计算的低层信息对人类感知并非必需。本框架有望为开发可解释人工智能及理解神经科学中的脑功能机制作出贡献。