A history and modular future of multiscale spatial filtering models
A history and modular future of multiscale spatial filtering models
复制标题
多尺度空间滤波模型的历史和模块化未来
DOI：
10.1167/jov.21.9.2824
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发表时间：
2021
影响因子：
1.8
通讯作者：
J. And Maertens
中科院分区：
文献类型：
--
作者：
Vincent;J. And Maertens
人类的亮度感知是非常强大的波动，在感官输入的功能，产生令人印象深刻的视觉错觉，并已被广泛的心理物理学研究的主题，用于测试感知机制提出参与。现代计算方法也使得研究神经处理的各个方面如何导致亮度感知的非真实性成为可能，而不是由特定的机制或策略引起的。特别成功的是在多个空间尺度上使用空间滤波来建模亮度感知。虽然没有捕获所有非真实性，但这些模型代表了早期视觉处理，并且可以定性地预测对许多刺激的感知反应。为了将这种多尺度空间滤波模型与其他提出的亮度感知机制联合收割机相结合，我们询问哪些模型组件已被证明对解释亮度感知有用（以及在多大程度上），以及哪些组件仅在解释特定模型的特定现象时才是必要的。我们提供了一个历史路线图，从早期到当代模型的演变，以及模型之间的主要差异及其对模型预测结果的影响的示意性概述。该示意图还使空间滤波模型的新的模块化实施成为可能。multyscalePython包是完全开源的，不依赖于专有软件。它实现了几个多尺度模型，并为模型和相关主题提供了文档化的示例、演示和教程。功能强大的模块化实现演示，通过预测感知亮度的心理物理学范式，在细节上，这将是困难的和（计算）耗时与以前的实现。概念验证给出了拟合模型参数的心理物理数据，超越了以前的实现。未来的努力可以利用模块化的性质，通过将多尺度空间滤波的元素与机械方法（例如，轮廓整合）或统计方法（例如，深度学习）相结合。
Human lightness perception is remarkably robust against fluctuations in the sensory input–a feature that yields impressive visual illusions and has been subject of extensive psychophysical study for testing perceptual mechanisms proposed to be involved. Modern computational methods additionally have made it possible to investigate how aspects of neural processing might lead to non-veridicality in lightness perception as an emergent property—rather than resulting from specific mechanisms or strategies. Particularly successful is modeling lightness perception using spatial filtering at multiple spatial scales. While not capturing all non-veridicalities, these models are representative of early visual processing and can qualitatively predict perceptual responses to many stimuli. To combine such multiscale spatial filtering models with other proposed mechanisms of lightness perception, we ask which model components have proven useful to explain lightness perception in general (and to what degree), and which components are necessary only when explaining specific phenomena from specific models. We provide a historical roadmap the evolution from earlier to contemporary models, as well as a schematic overview of major differences between models and their effect on resulting model predictions. The schematic overview also made possible an accompanying new, modular, implementation of spatial filtering models. ThemultyscalePython package is fully open-source, with no dependency on proprietary software. It implements several multiscale models, and provides documented examples, demos and tutorials for the models and related topics. The powerful modular implementation is demonstrated, by predicting perceived lightness in a psychophysical paradigm, in detail that would have been difficult and (computationally) time-consuming with previous implementations. Proof-of-concept is given for fitting model parameters to psychophysical data, going beyond the use of previous implementations. Future endeavors can take advantage of the modular nature, by integrating elements of multiscale spatial filtering with either mechanistic approaches (eg, contour integration) or statistical (eg, deep learning).