Bernstein Center for Computational Neuroscience, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany.
PLoS Comput Biol. 2012;8(11):e1002774. doi: 10.1371/journal.pcbi.1002774. Epub 2012 Nov 15.
According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations.
根据灵长类动物感觉运动加工的一种突出观点，选择和指定可能的动作不是顺序操作。相反，一个动作的决定是从不同运动计划之间的竞争中产生的，这些计划是并行指定和选择的。对于基于模糊感觉输入的动作选择，额顶感觉运动区被认为是选择和指定动作的共同基础神经基质的一部分。这些区域已被证明能够在运动规划过程中并行编码替代空间运动目标，并显示出这些目标之间基于竞争价值的选择的特征。由于相同的网络也参与了感觉运动关联的学习，因此竞争动作选择（决策）不仅应该受到有利于任一动作的感觉证据和预期奖励的驱动，还应该受到主体对不同感觉运动关联的学习历史的影响。以前的竞争神经决策计算模型使用了感觉输入和相应运动输出之间的预定义关联。这种硬连线不允许建模决策如何受到感觉运动学习或改变奖励条件的影响。我们提出了一个动态神经场模型，该模型使用奖励驱动的赫布学习算法学习任意感觉运动关联。我们表明，该模型准确地模拟了不同奖励条件下的动作选择动力学，与猴子皮层记录中的观察结果一致，并且它正确地预测了对照实验中的选择错误模式。通过我们的自适应模型，我们展示了网络可塑性如何影响选择行为，这种可塑性对于关联学习和适应新的奖励条件是必需的。该场模型为感觉运动整合、工作记忆和动作选择的操作提供了一个综合和动态的解释，这些操作是在模糊选择情况下进行决策所必需的。
