Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

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.

基于灵长类动物感觉运动加工的主流观点，对潜在动作的选择与具体化并非串行操作。相反，动作决策源于不同运动计划间的竞争——这些运动计划的具体化与选择均为并行开展。
针对基于模糊感觉输入的动作选择任务，额顶叶感觉运动皮层（frontoparietal sensorimotor areas）被认为是参与动作选择与具体化的共同核心神经基质之一。研究表明，该脑区可在运动规划阶段并行编码多种空间运动目标，并展现出对这些目标进行基于价值的竞争性选择的特征。
由于该同一网络还参与感觉运动联结的学习，因此竞争性动作选择（决策）不仅会受偏向各动作的感觉证据与预期奖赏驱动，同时也会受被试对不同感觉运动联结的学习历史影响。
此前的竞争性神经决策计算模型均采用感觉输入与对应运动输出间的预定义联结，这种固定接线方式无法模拟感觉运动学习或动态变化的奖赏偶联如何对决策产生影响。
我们提出了一种动态神经场模型（dynamic neural field model），该模型可通过奖赏驱动的赫布学习（Hebbian learning）算法学习任意感觉运动联结。
我们证实，该模型可精准模拟不同奖赏偶联条件下的动作选择动力学过程，这与猴皮层记录的实验结果一致；同时该模型还准确预测了对照实验中选择错误的模式。
借助本自适应模型，我们展示了神经可塑性——这是联结学习与适应新奖赏偶联的必要条件——如何对选择行为产生影响。
该神经场模型为模糊选择情境下决策所需的感觉运动整合、工作记忆（working memory）与动作选择等神经操作提供了整合且动态的阐释。