Observers reproducing elementary visual features from memory after a short delay produce errors consistent with the encoding-decoding properties of neural populations. While inspired by electrophysiological observations of sensory neurons in cortex, the population coding account of these errors is based on a mathematical idealization of neural response functions that abstracts away most of the heterogeneity and complexity of real neuronal populations. Here we examine a more physiologically grounded model based on the tuning of a large set of neurons recorded in macaque V1 and show that key predictions of the idealized model are preserved. Both models predict long-tailed distributions of error when memory resources are taxed, as observed empirically in behavioral experiments and commonly approximated with a mixture of normal and uniform error components. Specifically, for an idealized homogeneous neural population, the width of the fitted normal distribution cannot exceed the average tuning width of the component neurons, and this also holds to a good approximation for more biologically realistic populations. Examining eight published studies of orientation recall, we find a consistent pattern of results suggestive of a median tuning width of approximately 20°, which compares well with neurophysiological observations. The finding that estimates of variability obtained by the normal-plus-uniform mixture method are bounded from above leads us to reevaluate previous studies that interpreted a saturation in width of the normal component as evidence for fundamental limits on the precision of perception, working memory, and long-term memory.

中文翻译：

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神经编码理论预测了记忆变异性估计的上限。

观察者在短暂延迟后从记忆中再现基本视觉特征会产生与神经群体的编码-解码特性一致的错误。虽然受到皮层感觉神经元电生理观察的启发，但这些错误的群体编码解释是基于神经反应函数的数学理想化，该函数抽象了真实神经元群体的大部分异质性和复杂性。在这里，我们基于对猕猴 V1 中记录的大量神经元的调整检查了一个更具生理学基础的模型，并表明理想化模型的关键预测得到了保留。当内存资源被征税时，两种模型都预测错误的长尾分布，正如在行为实验中凭经验观察到的那样，通常用正常和均匀误差分量的混合来近似。具体来说，对于理想化的同质神经群体，拟合正态分布的宽度不能超过组成神经元的平均调谐宽度，这也适用于更符合生物学现实的群体。检查八项已发表的方向回忆研究，我们发现一致的结果模式表明大约 20° 的中位调谐宽度，这与神经生理学观察结果相得益彰。