Understanding how neural networks rely on visual cues offers a human-interpretable view of their internal decision processes. The cue-conflict benchmark has been influential in probing shape-texture preference and in motivating the insight that stronger, human-like shape bias is often associated with improved in-domain performance. However, we find that the current stylization-based instantiation can yield unstable and ambiguous bias estimates. Specifically, stylization may not reliably instantiate perceptually valid and separable cues nor control their relative informativeness, ratio-based bias can obscure absolute cue sensitivity, and restricting evaluation to preselected classes can distort model predictions by ignoring the full decision space. Together, these factors can confound preference with cue validity, cue balance, and recognizability artifacts. We introduce REFINED-BIAS, an integrated dataset and evaluation framework for reliable and interpretable shape-texture bias diagnosis. REFINED-BIAS constructs balanced, human- and model- recognizable cue pairs using explicit definitions of shape and texture, and measures cue-specific sensitivity over the full label space via a ranking-based metric, enabling fairer cross-model comparisons. Across diverse training regimes and architectures, REFINED-BIAS enables fairer cross-model comparison, more faithful diagnosis of shape and texture biases, and clearer empirical conclusions, resolving inconsistencies that prior cue-conflict evaluations could not reliably disambiguate.

翻译：理解神经网络如何依赖于视觉线索，为其内部决策过程提供了可解释的视角。线索冲突基准在探究形状-纹理偏好方面具有重要影响，并激发了一个关键洞见：更强的、类似于人类的形状偏好通常与领域内性能的提升相关。然而，我们发现当前基于风格化的实现方式可能产生不稳定且模糊的偏差估计。具体而言，风格化可能无法可靠地实例化感知有效且可分离的线索，也无法控制其相对信息量；基于比率的偏差可能掩盖绝对的线索敏感性；将评估局限于预选类别可能因忽略完整决策空间而扭曲模型预测。这些因素共同可能使偏好与线索有效性、线索平衡性及可识别性伪影相混淆。我们提出了REFINED-BIAS，一个用于可靠且可解释的形状-纹理偏差诊断的综合数据集与评估框架。REFINED-BIAS通过使用形状和纹理的明确定义构建平衡的、人类和模型可识别的线索对，并基于排序度量在完整标签空间上测量线索特定敏感性，从而实现更公平的跨模型比较。在多种训练范式和架构下，REFINED-BIAS支持更公平的跨模型比较、更忠实的形状与纹理偏差诊断，以及更清晰的实证结论，解决了先前线索冲突评估无法可靠区分的矛盾。