论文标题

除线性回归之外:认知神经科学中的映射模型应与研究目标保持一致

Beyond linear regression: mapping models in cognitive neuroscience should align with research goals

论文作者

Ivanova, Anna A., Schrimpf, Martin, Anzellotti, Stefano, Zaslavsky, Noga, Fedorenko, Evelina, Isik, Leyla

论文摘要

许多认知神经科学研究使用大型特征集来预测和解释大脑活动模式。功能集采取多种形式,从人类刺激注释到深神网络中的表示形式。在所有这些研究中,至关重要的重要性是映射模型,该模型定义了特征和神经数据之间可能关系的空间。直到最近,大多数编码和解码研究都使用了线性映射模型。大型数据集和计算资源的可用性增加已使一些研究人员改为采用更灵活的非线性映射模型。但是,非线性映射模型是否可以产生有意义的科学见解的问题仍然存在争议。在这里,我们讨论了在三个总体Desiderata的背景下选择映射模型的选择:预测准确性,可解释性和生物学合理性。我们表明,与流行的直觉相反,这些Desiderata不会清晰地映射到线性/非线性鸿沟上。取而代之的是,每个Desideratum都可以指多个研究目标,每个研究目标都对映射模型施加了自己的约束。此外,我们认为,我们应该旨在估计这些模型的复杂性,而不是将映射模型分类为线性或非线性。我们表明,在许多情况下,复杂性可更准确地反映了各种研究目标所施加的限制。最后,我们概述了几个可用于有效评估映射模型的复杂度指标。

Many cognitive neuroscience studies use large feature sets to predict and interpret brain activity patterns. Feature sets take many forms, from human stimulus annotations to representations in deep neural networks. Of crucial importance in all these studies is the mapping model, which defines the space of possible relationships between features and neural data. Until recently, most encoding and decoding studies have used linear mapping models. Increasing availability of large datasets and computing resources has recently allowed some researchers to employ more flexible nonlinear mapping models instead; however, the question of whether nonlinear mapping models can yield meaningful scientific insights remains debated. Here, we discuss the choice of a mapping model in the context of three overarching desiderata: predictive accuracy, interpretability, and biological plausibility. We show that, contrary to popular intuition, these desiderata do not map cleanly onto the linear/nonlinear divide; instead, each desideratum can refer to multiple research goals, each of which imposes its own constraints on the mapping model. Moreover, we argue that, instead of categorically treating the mapping models as linear or nonlinear, we should instead aim to estimate the complexity of these models. We show that, in many cases, complexity provides a more accurate reflection of restrictions imposed by various research goals. Finally, we outline several complexity metrics that can be used to effectively evaluate mapping models.

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