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.
Keywords: attractor network; computational neuroscience; decision prediction; decision-making; fMRI; free will; noise in the brain; prediction.