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Population Models, Not Analyses, of Human Neuroscience Measurements
Annual Review of Vision Science ( IF 5.0 ) Pub Date : 2021-09-15 , DOI: 10.1146/annurev-vision-093019-111124
Justin L Gardner 1 , Elisha P Merriam 2
Affiliation  

Selectivity for many basic properties of visual stimuli, such as orientation, is thought to be organized at the scale of cortical columns, making it difficult or impossible to measure directly with noninvasive human neuroscience measurement. However, computational analyses of neuroimaging data have shown that selectivity for orientation can be recovered by considering the pattern of response across a region of cortex. This suggests that computational analyses can reveal representation encoded at a finer spatial scale than is implied by the spatial resolution limits of measurement techniques. This potentially opens up the possibility to study a much wider range of neural phenomena that are otherwise inaccessible through noninvasive measurement. However, as we review in this article, a large body of evidence suggests an alternative hypothesis to this superresolution account: that orientation information is available at the spatial scale of cortical maps and thus easily measurable at the spatial resolution of standard techniques. In fact, a population model shows that this orientation information need not even come from single-unit selectivity for orientation tuning, but instead can result from population selectivity for spatial frequency. Thus, a categorical error of interpretation can result whereby orientation selectivity can be confused with spatial frequency selectivity. This is similarly problematic for the interpretation of results from numerous studies of more complex representations and cognitive functions that have built upon the computational techniques used to reveal stimulus orientation. We suggest in this review that these interpretational ambiguities can be avoided by treating computational analyses as models of the neural processes that give rise to measurement. Building upon the modeling tradition in vision science using considerations of whether population models meet a set of core criteria is important for creating the foundation for a cumulative and replicable approach to making valid inferences from human neuroscience measurements.

中文翻译:


人类神经科学测量的人口模型,而不是分析

视觉刺激的许多基本属性(例如方向)的选择性被认为是在皮质柱的规模上组织的,因此很难或不可能直接用非侵入性人类神经科学测量进行测量。然而,神经影像数据的计算分析表明,可以通过考虑整个皮层区域的反应模式来恢复方向的选择性。这表明计算分析可以揭示在比测量技术的空间分辨率限制所暗示的更精细的空间尺度上编码的表示。这可能为研究更广泛的神经现象开辟了可能性,而这些神经现象是通过非侵入性测量无法获得的。然而,正如我们在本文中回顾的那样,大量证据表明这种超分辨率帐户的另一种假设:方向信息在皮质地图的空间尺度上可用,因此在标准技术的空间分辨率下很容易测量。事实上,人口模型表明,这种定向信息甚至不需要来自定向调整的单单元选择性,而是可以来自空间频率的人口选择性。因此,可能导致解释的分类错误,由此方向选择性可能与空间频率选择性混淆。对于基于用于揭示刺激方向的计算技术的更复杂表示和认知功能的大量研究结果的解释,这同样是有问题的。我们在这篇综述中建议,通过将计算分析视为产生测量的神经过程的模型,可以避免这些解释上的歧义。建立在视觉科学建模传统的基础上,考虑人口模型是否满足一组核心标准,这对于为从人类神经科学测量中做出有效推论的累积和可复制方法奠定基础非常重要。

更新日期:2021-09-17
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