Human tactile perception of materials relies on complex multisensory touch cues, yet the relationship between low-level tactile signals and perceptual representations remains poorly understood. This knowledge gap hinders the integration of touch in digital environments and the development of robots capable of human-like tactile perception. Here, we present an interpretable computational framework for modeling human material perception and recognition using multisensory touch data. Our framework comprises three interconnected models: Model 1 maps finger-surface interaction features to psychophysical sensory attributes, Model 2 classifies materials based on these perceptual representations, and Model 3 directly classifies materials from tactile features. The results showed that combining information from pressing, static contact, and sliding interactions improves prediction accuracy, and that thermal cues are particularly informative for both perceptual modeling and material classification. These findings highlight the importance of thermal and compliance cues, which remain underrepresented in current robotic fingers and haptic displays. Incorporating such cues may enhance artificial systems' ability to approximate human material perception and guide the design of more perceptually grounded haptic interfaces.

翻译：人类对材料的触觉感知依赖于复杂的多感官触觉线索，然而低层级触觉信号与感知表征之间的关系仍未被充分理解。这一认知鸿沟阻碍了触觉在数字环境中的整合以及能够实现类人触觉感知的机器人开发。本文提出一个基于多感官触觉数据进行人类材料感知建模与识别的可解释计算框架。该框架包含三个相互关联的模型：模型1将手指-表面交互特征映射至心理物理感官属性，模型2基于这些感知表征进行材料分类，模型3则直接从触觉特征实现材料分类。结果表明：融合按压、静态接触与滑动交互的信息可提升预测精度，其中热觉线索对感知建模与材料分类均具有显著信息价值。这些发现凸显了热觉与柔顺性线索的重要性——当前机器人手指与触觉显示设备对此类线索的表征仍不充分。融入此类线索有望增强人工系统逼近人类材料感知的能力，并为更符合感知规律的触觉接口设计提供指导。