Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multiregion recordings in mice, and (4) decoding position from hippocampal recordings in rats. For each setting, we characterize the overconfidence, and we describe a possible method to correct miscalibration post hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain–machine interfaces that more accurately reflect confidence levels when identifying external variables.

从神经活动的观察中准确解码外部变量是系统神经科学的主要挑战。贝叶斯解码器提供概率估计，是使用最广泛的解码器之一。在这里，我们展示了在许多常见设置中，传统贝叶斯解码器做出的概率预测如何过于自信。也就是说，对解码的刺激或运动变量的估计比应有的更加确定。然后，我们展示了如何使用潜在变量进行贝叶斯解码，考虑到观察中的低维共享变异性，可以改进校准，尽管仍然需要对过度自信进行额外的校正。使用来自雄性的数据，我们检查（1）从猴子初级视觉皮层的尖峰记录中解码光栅刺激的方向，（2）从猴子初级运动皮层的记录中解码运动方向，（3）从猴子的初级运动皮层中解码自然图像小鼠的多区域记录，以及（4）解码大鼠海马记录的位置。对于每种设置，我们都描述了过度自信的特征，并描述了一种事后纠正错误校准的可能方法。正确校准的贝叶斯解码器可能会改变概率群体编码的理论结果，并导致脑机接口在识别外部变量时更准确地反映置信水平。