Power-law efficient neural codes provide general link between perceptual bias and discriminability
Power-law efficient neural codes provide general link between perceptual bias and discriminability
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幂律高效神经代码提供了感知偏差和可辨别性之间的一般联系
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发表时间：
2018
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通讯作者：
Jonathan W. Pillow
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作者：
Michael J. Morais;Jonathan W. Pillow
最近在理论神经科学方面的工作表明，信息论的“有效”神经编码分配神经资源以最大限度地利用刺激和神经反应之间的相互信息，在人类观察者的各种心理物理任务中观察到的知觉偏差和辨别能力之间存在合法的关系(Wei&Stocker 2017)。在这里，我们推广这些结果，以表明在更大的最优神经码族下出现相同的规律，引入了我们称为幂规律有效编码的统一框架。具体地说，我们证明了当Fisher信息被分配成与先验分布的任何幂成正比时，偏差和可区分性之间存在相同的合法关系。这一系列包括对于最小化任何p的Lp误差最优的神经代码，这表明在人类心理物理数据中观察到的合法关系不需要信息-理论上最优的神经代码。此外，对于不同的幂定律(包括信息理论上最优码，其中幂为2，以及所谓的discrimax码，其中幂为1/2)，以及不同的最佳译码选择，我们导出了控制偏差与可区分性之间关系的精确比例常数。作为一个额外的好处，我们的框架提供了对“反贝叶斯”感知偏差的新见解，在这种偏差中，感知偏离了先验的质量中心。我们推导了一个明确的公式，精确地阐明了神经编码器和解码器的哪种组合可能会引起这种偏差。
Recent work in theoretical neuroscience has shown that information-theoretic "efficient" neural codes, which allocate neural resources to maximize the mutual information between stimuli and neural responses, give rise to a lawful relationship between perceptual bias and discriminability that is observed across a wide variety of psychophysical tasks in human observers (Wei & Stocker 2017). Here we generalize these results to show that the same law arises under a much larger family of optimal neural codes, introducing a unifying framework that we call power-law efficient coding. Specifically, we show that the same lawful relationship between bias and discriminability arises whenever Fisher information is allocated proportional to any power of the prior distribution. This family includes neural codes that are optimal for minimizing Lp error for any p, indicating that the lawful relationship observed in human psychophysical data does not require information-theoretically optimal neural codes. Furthermore, we derive the exact constant of proportionality governing the relationship between bias and discriminability for different power laws (which includes information-theoretically optimal codes, where the power is 2, and so-called discrimax codes, where power is 1/2), and different choices of optimal decoder. As a bonus, our framework provides new insights into "anti-Bayesian" perceptual biases, in which percepts are biased away from the center of mass of the prior. We derive an explicit formula that clarifies precisely which combinations of neural encoder and decoder can give rise to such biases.