National Brain Research Centre, Nainwal Mode, Manesar, Haryana, India.
Prog Brain Res. 2013;202:321-45. doi: 10.1016/B978-0-444-62604-2.00017-4.
Accumulator models of decision making provide a unified framework to understand decision making and motor planning. In these models, the evolution of a decision is reflected in the accumulation of sensory information into a motor plan that reaches a threshold, leading to choice behavior. While these models provide an elegant framework to understand performance and reaction times, their ability to explain complex behaviors such as decision making and motor control of sequential movements in dynamic environments is unclear. To examine and probe the limits of online modification of decision making and motor planning, an oculomotor "redirect" task was used. Here, subjects were expected to change their eye movement plan when a new saccade target appeared. Based on task performance, saccade reaction time distributions, computational models of behavior, and intracortical microstimulation of monkey frontal eye fields, we show how accumulator models can be tested and extended to study dynamic aspects of decision making and motor control.
决策的累加器模型为理解决策和运动规划提供了一个统一的框架。在这些模型中,决策的演变反映在将感觉信息积累到达到阈值的运动计划中,从而导致选择行为。虽然这些模型为理解性能和反应时间提供了一个优雅的框架,但它们解释复杂行为的能力,如动态环境中序列运动的决策和运动控制,尚不清楚。为了检验和探究在线修改决策和运动规划的极限,使用了眼动“重定向”任务。在这里,当出现新的扫视目标时,要求受试者改变他们的眼球运动计划。基于任务表现、扫视反应时间分布、行为的计算模型以及猴子额眼运动前区的皮质内微刺激,我们展示了如何测试和扩展累加器模型,以研究决策和运动控制的动态方面。