A biologically plausible computational theory for value integration and action selection in decisions with competing alternatives.
A biologically plausible computational theory for value integration and action selection in decisions with competing alternatives.
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DOI：
10.1371/journal.pcbi.1004104
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发表时间：
2015-03
影响因子：
4.3
通讯作者：
Andersen RA
中科院分区：
文献类型：
--
作者：
Christopoulos V;Bonaiuto J;Andersen RA
决策是人类和动物行为的重要组成部分，涉及在备选方案之间进行选择，并产生实施选择的行动。虽然决策可以像选择一个目标，然后追求它一样简单，但人类和动物通常必须在动态环境中做出决策，其中选项的价值和可用性随着时间和先前的行动而不可预测地变化。一个捕食者追逐多个猎物证明了目标如何在持续的行动中动态变化和竞争。经典心理学理论认为，决策发生在额叶区域，是一个独立于感知和行动的过程。然而，最近的研究结果表明，行动之间的决定往往是通过计划和指导行动执行的同一大脑区域内的持续竞争而产生的。根据这些发现，感觉运动系统产生并行的行动计划，竞争的目标，并使用在线信息，以偏见的竞争，直到一个单一的目标是追求。这些信息是多种多样的，与目标的动态价值和行动成本相关，这在真实的实时整合这些不同变量的信息方面产生了一个具有挑战性的问题。我们引入了一个计算框架，动态整合价值信息从不同的来源在决策任务与竞争行动。我们在一系列眼动和达成决策任务中评估了该框架，发现它捕捉了选择/运动行为的许多特征，以及以前无法解释的神经基础。在高压力的情况下，比如在高速公路上开车或驾驶飞机，人们在行动时在竞争选项之间做出选择的时间有限。每个选项通常都伴随着奖励福利（例如，避免业务）和动作成本（例如，燃料消耗），这是期权价值的特征。即使在正在进行的行动中，选项的价值和可用性也会动态变化，这加剧了决策挑战。大脑如何动态地整合来自不同来源的价值信息，并在竞争选项之间进行选择，仍然知之甚少。在目前的研究中，我们提出了一个神经动力学框架，以显示如何分布式大脑网络可以解决问题的价值整合和行动选择的决策与竞争的替代品。它结合了动态神经场理论和随机最优控制理论，包括感知、期望回报、努力成本和决策电路。它提供了一个原则性的方法来解释在人类和动物研究中的一系列视觉决策任务的神经和行为发现。例如，该模型显示了感觉运动区域内和感觉运动区域之间的神经元群体之间的竞争性相互作用如何导致“空间平均”运动，以及决策变量如何影响神经活动和选择行为。
Decision making is a vital component of human and animal behavior that involves selecting between alternative options and generating actions to implement the choices. Although decisions can be as simple as choosing a goal and then pursuing it, humans and animals usually have to make decisions in dynamic environments where the value and the availability of an option change unpredictably with time and previous actions. A predator chasing multiple prey exemplifies how goals can dynamically change and compete during ongoing actions. Classical psychological theories posit that decision making takes place within frontal areas and is a separate process from perception and action. However, recent findings argue for additional mechanisms and suggest the decisions between actions often emerge through a continuous competition within the same brain regions that plan and guide action execution. According to these findings, the sensorimotor system generates concurrent action-plans for competing goals and uses online information to bias the competition until a single goal is pursued. This information is diverse, relating to both the dynamic value of the goal and the cost of acting, creating a challenging problem in integrating information across these diverse variables in real time. We introduce a computational framework for dynamically integrating value information from disparate sources in decision tasks with competing actions. We evaluated the framework in a series of oculomotor and reaching decision tasks and found that it captures many features of choice/motor behavior, as well as its neural underpinnings that previously have eluded a common explanation. In high-pressure situations, such as driving on a highway or flying a plane, people have limited time to select between competing options while acting. Each option is usually accompanied with reward benefits (e.g., avoid traffic) and action costs (e.g., fuel consumption) that characterize the value of the option. The value and the availability of an option can change dynamically even during ongoing actions which compounds the decision-making challenge. How the brain dynamically integrates value information from disparate sources and selects between competing options is still poorly understood. In the current study, we present a neurodynamical framework to show how a distributed brain network can solve the problem of value integration and action selection in decisions with competing alternatives. It combines dynamic neural field theory with stochastic optimal control theory, and includes circuitry for perception, expected reward, effort cost and decision-making. It provides a principled way to explain both the neural and the behavioral findings from a series of visuomotor decision tasks in human and animal studies. For instance, the model shows how the competitive interactions between populations of neurons within and between sensorimotor regions can result in “spatial-averaging” movements, and how decision-variables influence neural activity and choice behavior.
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DOI：
10.1098/rstb.2007.2054
发表时间：
2007-09-29
影响因子：
6.3
作者：
Cisek, Paul
通讯作者：
Cisek, Paul
影响因子：
16.2
作者：
Kable, Joseph W.;Glimcher, Paul W.
通讯作者：
Glimcher, Paul W.
影响因子：
5.4
作者：
Erlhagen, W;Schöner, G
通讯作者：
Schöner, G
影响因子：
5.3
作者：
Chen-Harris, Haiyin;Joiner, Wilsaan M.;Shadmehr, Reza
通讯作者：
Shadmehr, Reza
DOI：
10.1037/0096-1523.14.3.331
发表时间：
1988-08-01
影响因子：
2.1
作者：
GRATTON, G;COLES, MGH;DONCHIN, E
通讯作者：
DONCHIN, E