Ma Wei Ji
Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
Vision Res. 2010 Oct 28;50(22):2308-19. doi: 10.1016/j.visres.2010.08.035. Epub 2010 Sep 7.
The juxtaposition of established signal detection theory models of perception and more recent claims about the encoding of uncertainty in perception is a rich source of confusion. Are the latter simply a rehash of the former? Here, we make an attempt to distinguish precisely between optimal and probabilistic computation. In optimal computation, the observer minimizes the expected cost under a posterior probability distribution. In probabilistic computation, the observer uses higher moments of the likelihood function of the stimulus on a trial-by-trial basis. Computation can be optimal without being probabilistic, and vice versa. Most signal detection theory models describe optimal computation. Behavioral data only provide evidence for a neural representation of uncertainty if they are best described by a model of probabilistic computation. We argue that single-neuron activity sometimes suffices for optimal computation, but never for probabilistic computation. A population code is needed instead. Not every population code is equally suitable, because nuisance parameters have to be marginalized out. This problem is solved by Poisson-like, but not by Gaussian variability. Finally, we build a dictionary between signal detection theory quantities and Poisson-like population quantities.
已确立的感知信号检测理论模型与近期关于感知中不确定性编码的主张并存，这造成了诸多混淆。后者仅仅是前者的重新表述吗？在此，我们试图精确区分最优计算和概率计算。在最优计算中，观察者在事后概率分布下使预期成本最小化。在概率计算中，观察者逐次试验地使用刺激似然函数的高阶矩。计算可以是最优的但不是概率性的，反之亦然。大多数信号检测理论模型描述的是最优计算。行为数据只有在由概率计算模型能最好地描述时，才为不确定性的神经表征提供证据。我们认为，单神经元活动有时足以进行最优计算，但绝不足以进行概率计算。相反，需要一个群体编码。并非每个群体编码都同样适用，因为讨厌参数必须被边缘化。这个问题由类泊松变异性解决，但高斯变异性无法解决。最后，我们建立了信号检测理论量与类泊松群体量之间的字典。
