While deep learning models can learn human-like features at earlier levels, which suggests their utility in modeling human vision, few attempts exist to incorporate these features by design. Current approaches mostly optimize all parameters blindly, only constraining minor architectural aspects. This paper demonstrates how parametrizing neural network layers enables more biologically-plausible operations while reducing trainable parameters and improving interpretability. We constrain operations to functional forms present in human vision, optimizing only these functions' parameters rather than all convolutional tensor elements independently. We present two parametric model versions: one with hand-chosen biologically plausible parameters, and another fitted to human perception experimental data. We compare these with a non-parametric version. All models achieve comparable state-of-the-art results, with parametric versions showing orders of magnitude parameter reduction for minimal performance loss. The parametric models demonstrate improved interpretability and training behavior. Notably, the model fitted to human perception, despite biological initialization, converges to biologically incorrect results. This raises scientific questions and highlights the need for diverse evaluation methods to measure models' humanness, rather than assuming task performance correlates with human-like behavior.

翻译：尽管深度学习模型能够在较低层级学习到类人特征，这表明它们在模拟人类视觉方面具有潜力，但通过设计来整合这些特征的尝试却很少。当前方法大多盲目地优化所有参数，仅对次要架构层面施加约束。本文论证了如何通过参数化神经网络层来实现更具生物学合理性的操作，同时减少可训练参数并提升可解释性。我们将操作约束在人类视觉中存在的函数形式，仅优化这些函数的参数而非独立优化所有卷积张量元素。我们提出了两种参数化模型版本：一种采用人工选择的生物学合理参数，另一种则拟合人类感知实验数据。我们将这些模型与非参数化版本进行比较。所有模型均取得了可比的最新成果，其中参数化版本在性能损失极小的前提下实现了数量级的参数削减。参数化模型展现出更好的可解释性和训练行为。值得注意的是，尽管经过生物学初始化，拟合人类感知的模型却收敛到生物学上不正确的结果。这引发了科学问题，并凸显了需要采用多样化评估方法来衡量模型的"拟人程度"，而非假定任务表现与类人行为必然相关。