Anxiety-Like Behavioural Inhibition Is Normative under Environmental Threat-Reward Correlations.
Anxiety-Like Behavioural Inhibition Is Normative under Environmental Threat-Reward Correlations.
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DOI：
10.1371/journal.pcbi.1004646
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发表时间：
2015-12
影响因子：
4.3
通讯作者：
Bach DR
中科院分区：
文献类型：
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作者：
Bach DR
行为抑制是啮齿类动物和人类中的一种关键的焦虑样行为，与避免危险不同，并通过抗焦虑药物减少。在某些情况下，尚不清楚行为抑制是如何将伤害最小化或使代理人的利益最大化的，甚至可能出现适得其反的情况。对这一现象的现有解释利用了描述性模型，但没有提供对其适应价值的正式评估。这阻碍了对焦虑行为背后的神经计算的更好理解。在这里，我们分析了一个标准的啮齿动物焦虑模型，操作性冲突测试。我们收获贝叶斯决策理论表明，在时间相关的环境中，行为抑制作为成本最小化策略规范地出现。重要的是，只有当行为抑制的目的是最大限度地减少成本时，它才取决于威胁的概率和程度。利用虚拟计算机游戏，我们在四个实验中与人类参与者测试模型预测。人类表现出的行为抑制与威胁的概率和程度有很强的线性关系。引人注目的是，抑制发生在运动执行之前，并取决于虚拟环境，因此可能是神经优化过程而不是预编程机制的结果。个人特质焦虑分数预测行为抑制，强调这种焦虑模型的有效性。这些发现将焦虑行为置于成本最小化和最佳推理的背景下，并可能最终为人类焦虑症中神经计算出错的机械理解铺平道路。在动物和人类中，在焦虑的情况下观察到行为抑制。在某些情况下，不清楚它如何使代理人的伤害最小化或利益最大化，甚至可能适得其反。这阻碍了对底层神经计算的理解。在这里，我们提供了第一个正式的评估其适应性价值在控制焦虑模型，并确认预测在四个实验与人类。结果可能表明依赖于在线成本最小化的神经实现。这一发现可以更好地理解人类焦虑症和潜在的神经计算。
Behavioural inhibition is a key anxiety-like behaviour in rodents and humans, distinct from avoidance of danger, and reduced by anxiolytic drugs. In some situations, it is not clear how behavioural inhibition minimises harm or maximises benefit for the agent, and can even appear counterproductive. Extant explanations of this phenomenon make use of descriptive models but do not provide a formal assessment of its adaptive value. This hampers a better understanding of the neural computations underlying anxiety behaviour. Here, we analyse a standard rodent anxiety model, the operant conflict test. We harvest Bayesian Decision Theory to show that behavioural inhibition normatively arises as cost-minimising strategy in temporally correlated environments. Importantly, only if behavioural inhibition is aimed at minimising cost, it depends on probability and magnitude of threat. Harnessing a virtual computer game, we test model predictions in four experiments with human participants. Humans exhibit behavioural inhibition with a strong linear dependence on threat probability and magnitude. Strikingly, inhibition occurs before motor execution and depends on the virtual environment, thus likely resulting from a neural optimisation process rather than a pre-programmed mechanism. Individual trait anxiety scores predict behavioural inhibition, underlining the validity of this anxiety model. These findings put anxiety behaviour into the context of cost-minimisation and optimal inference, and may ultimately pave the way towards a mechanistic understanding of the neural computations gone awry in human anxiety disorder. Behavioural inhibition is observed in situations of anxiety, both in animals and humans. In some situations, it is not clear how it minimises harm or maximises benefit for the agent, and can even appear counterproductive. This prevents an understanding of the underlying neural computations. Here, we furnish the first formal assessment of its adaptive value in a controlled anxiety model, and confirm predictions in four experiments with humans. Results may suggest a neural implementation that relies on online cost minimisation. This finding could afford a better understanding of human anxiety disorder and the underlying neural computations.