PDF(Pubmed)
Abstract：
Operant conditioning of neural activation has been researched for decades in humans and animals. Many theories suggest two parallel learning processes, implicit and explicit. The degree to which feedback affects these processes individually remains to be fully understood and may contribute to a large percentage of non-learners. Our goal is to determine the explicit decision-making processes in response to feedback representing an operant conditioning environment. We developed a simulated operant conditioning environment based on a feedback model of spinal reflex excitability, one of the simplest forms of neural operant conditioning. We isolated the perception of the feedback signal from self-regulation of an explicit unskilled visuomotor task, enabling us to quantitatively examine feedback strategy. Our hypothesis was that feedback type, biological variability, and reward threshold affect operant conditioning performance and operant strategy. Healthy individuals (N = 41) were instructed to play a web application game using keyboard inputs to rotate a virtual knob representative of an operant strategy. The goal was to align the knob with a hidden target. Participants were asked to \"down-condition\" the amplitude of the virtual feedback signal, which was achieved by placing the knob as close as possible to the hidden target. We varied feedback type (knowledge of performance, knowledge of results), biological variability (low, high), and reward threshold (easy, moderate, difficult) in a factorial design. Parameters were extracted from real operant conditioning data. Our main outcomes were the feedback signal amplitude (performance) and the mean change in dial position (operant strategy). We observed that performance was modulated by variability, while operant strategy was modulated by feedback type. These results show complex relations between fundamental feedback parameters and provide the principles for optimizing neural operant conditioning for non-responders.
摘要：
神经激活的操作调节已经在人类和动物中研究了数十年。许多理论提出了两个并行的学习过程，隐式和显式。反馈单独影响这些过程的程度仍有待充分理解，并可能导致很大比例的非学习者。我们的目标是确定明确的决策过程，以响应代表操作条件环境的反馈。我们基于脊髓反射兴奋性的反馈模型开发了一个模拟的操作性调节环境，神经操作条件的最简单形式之一。我们将反馈信号的感知与明确的不熟练的视觉运动任务的自我调节隔离开来，使我们能够定量地检查反馈策略。我们的假设是反馈类型，生物变异性，奖励阈值影响操作条件性能和操作策略。健康个体(N=41)被指示使用键盘输入来旋转表示操作策略的虚拟旋钮来玩网络应用游戏。目标是将旋钮与隐藏的目标对齐。参与者被要求“向下调节”虚拟反馈信号的幅度，这是通过将旋钮尽可能靠近隐藏目标来实现的。我们改变了反馈类型(绩效知识，结果知识)，生物变异性(低，高),和奖励阈值(简单，中度,困难)在阶乘设计中。从实际操作条件数据中提取参数。我们的主要结果是反馈信号幅度（性能）和刻度盘位置的平均变化（操作策略）。我们观察到性能是由变异性调节的，而操作策略由反馈类型调制。这些结果表明了基本反馈参数之间的复杂关系，并为非响应者的神经操作条件优化提供了原理。
