PDF(Pubmed)
Abstract：
In patients with mood disorders, negative affective biases - systematically prioritising and interpreting information negatively - are common. A translational cognitive task testing this bias has shown that depressed patients have a reduced preference for a high reward under ambiguous decision-making conditions. The precise mechanisms underscoring this bias are, however, not yet understood. We therefore developed a set of measures to probe the underlying source of the behavioural bias by testing its relationship to a participant\'s reward sensitivity, value sensitivity and reward learning rate. One-hundred-forty-eight participants completed three online behavioural tasks: the original ambiguous-cue decision-making task probing negative affective bias, a probabilistic reward learning task probing reward sensitivity and reward learning rate, and a gambling task probing value sensitivity. We modelled the learning task through a dynamic signal detection theory model and the gambling task through an expectation-maximisation prospect theory model. Reward sensitivity from the probabilistic reward task (β = 0.131, p = 0.024) and setting noise from the probabilistic reward task (β = -0.187, p = 0.028) both predicted the affective bias score in a logistic regression. Increased negative affective bias, at least on this specific task, may therefore be driven in part by a combination of reduced sensitivity to rewards and more variable responses.
摘要：
在有情绪障碍的患者中，负面情感偏见-系统地对信息进行优先排序和负面解释-很常见。测试这种偏见的翻译认知任务表明，在模棱两可的决策条件下，抑郁症患者对高回报的偏好降低。强调这种偏见的确切机制是，然而,还不明白。因此，我们开发了一套措施，通过测试行为偏见与参与者的奖励敏感性的关系来探索行为偏见的潜在来源，价值敏感度和奖励学习率。148名参与者完成了三项在线行为任务：原始的模糊线索决策任务，探测负面情感偏见，探索奖励敏感度和奖励学习率的概率奖励学习任务，和探测价值敏感性的赌博任务。我们通过动态信号检测理论模型对学习任务进行了建模，并通过期望最大化前景理论模型对赌博任务进行了建模。概率奖励任务的奖励敏感性（β=0.131，p=0.024）和概率奖励任务的设定噪声（β=-0.187，p=0.028）都预测了逻辑回归中的情感偏差得分。负面情感偏见增加，至少在这个特定的任务上,因此，可能在一定程度上是由对奖励的敏感性降低和反应更加多变的组合所驱动的。
