McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Quebec H3A 2B4, Canada.
Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, N6A 5C1, Canada.
Cereb Cortex. 2020 Sep 3;30(10):5471-5483. doi: 10.1093/cercor/bhaa129.
Current models of decision-making assume that the brain gradually accumulates evidence and drifts toward a threshold that, once crossed, results in a choice selection. These models have been especially successful in primate research; however, transposing them to human fMRI paradigms has proved it to be challenging. Here, we exploit the face-selective visual system and test whether decoded emotional facial features from multivariate fMRI signals during a dynamic perceptual decision-making task are related to the parameters of computational models of decision-making. We show that trial-by-trial variations in the pattern of neural activity in the fusiform gyrus reflect facial emotional information and modulate drift rates during deliberation. We also observed an inverse-urgency signal based in the caudate nucleus that was independent of sensory information but appeared to slow decisions, particularly when information in the task was ambiguous. Taken together, our results characterize how decision parameters from a computational model (i.e., drift rate and urgency signal) are involved in perceptual decision-making and reflected in the activity of the human brain.
目前的决策模型假设大脑逐渐积累证据,并向一个阈值漂移,一旦超过这个阈值,就会导致选择。这些模型在灵长类动物研究中尤其成功;然而,将它们转化为人类 fMRI 范式被证明是具有挑战性的。在这里,我们利用选择性视觉系统,测试在动态感知决策任务中,从多元 fMRI 信号解码的情感面部特征是否与决策计算模型的参数相关。我们表明,在梭状回中神经活动模式的逐次变化反映了面部的情感信息,并在审议过程中调节漂移率。我们还观察到基于尾状核的反向紧迫性信号,该信号与感觉信息无关,但似乎会减缓决策速度,尤其是在任务信息不明确时。总的来说,我们的结果描述了计算模型中的决策参数(即漂移率和紧迫性信号)如何参与感知决策,并反映在人类大脑的活动中。