Rolls Edmund T, Deco Gustavo
Oxford Centre for Computational Neuroscience Oxford, UK.
Front Neurosci. 2011 Mar 17;5:33. doi: 10.3389/fnins.2011.00033. eCollection 2011.
Can decisions be predicted from brain activity? It is frequently difficult in neuroimaging studies to determine this, because it is not easy to establish when the decision has been taken. In a rigorous approach to this issue, we show that in a neurally plausible integrate-and-fire attractor-based model of decision-making, the noise generated by the randomness in the spiking times of neurons can be used to predict a decision for 0.5 s or more before the decision cues are applied. The ongoing noise at the time the decision cues are applied influences which decision will be taken. It is possible to predict on a single trial to more than 68% correct which of two decisions will be taken. The prediction is made from the spontaneous firing before the decision cues are applied in the two populations of neurons that represent the decisions. Thus decisions can be partly predicted even before the decision cues are applied, due to noise in the decision-making process. This analysis has interesting implications for decision-making and free will, for it shows that random neuronal firing times can influence a decision before the evidence for the decision has been provided.
能否根据大脑活动预测决策？在神经影像学研究中，确定这一点往往很困难，因为要确定决策何时做出并不容易。在对这个问题的严谨研究中，我们表明，在基于神经合理的积分发放吸引子的决策模型中，神经元放电时间的随机性产生的噪声可用于在应用决策线索前0.5秒或更长时间预测决策。应用决策线索时的持续噪声会影响做出哪种决策。在单次试验中，可以正确预测两个决策中会做出哪一个的概率超过68%。该预测是根据代表决策的两组神经元在应用决策线索之前的自发放电做出的。因此，由于决策过程中的噪声，甚至在应用决策线索之前，决策就可以部分地被预测。这种分析对决策和自由意志有有趣的启示，因为它表明随机的神经元放电时间可以在提供决策证据之前影响决策。
