Hyett Matthew, Parker Gordon, Breakspear Michael
Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia.
BMC Psychiatry. 2014 Apr 27;14:122. doi: 10.1186/1471-244X-14-122.
Cognitive disturbances in depression are pernicious and so contribute strongly to the burden of the disorder. Cognitive function has been traditionally studied by challenging subjects with modality-specific psychometric tasks and analysing performance using standard analysis of variance. Whilst informative, such an approach may miss deeper perceptual and inferential mechanisms that potentially unify apparently divergent emotional and cognitive deficits. Here, we sought to elucidate basic psychophysical processes underlying the detection of emotionally salient signals across individuals with melancholic and non-melancholic depression.
Sixty participants completed an Affective Go/No-Go (AGN) task across negative, positive and neutral target stimuli blocks. We employed hierarchical Bayesian signal detection theory (SDT) to model psychometric performance across three equal groups of those with melancholic depression, those with a non-melancholic depression and healthy controls. This approach estimated likely response profiles (bias) and perceptual sensitivity (discriminability). Differences in the means of these measures speak to differences in the emotional signal detection between individuals across the groups, while differences in the variance reflect the heterogeneity of the groups themselves.
Melancholic participants showed significantly decreased sensitivity to positive emotional stimuli compared to those in the non-melancholic group, and also had a significantly lower discriminability than healthy controls during the detection of neutral signals. The melancholic group also showed significantly higher variability in bias to both positive and negative emotionally salient material.
Disturbances of emotional signal detection in melancholic depression appear dependent on emotional context, being biased during the detection of positive stimuli, consistent with a noisier representation of neutral stimuli. The greater heterogeneity of the bias across the melancholic group is consistent with a more labile disorder (i.e. variable across the day). Future work will aim to understand how these findings reflect specific individual differences (e.g. prior cognitive biases) and clarify whether such biases change dynamically during cognitive tasks as internal models of the sensorium are refined and updated in response to experience.
抑郁症中的认知障碍具有危害性，因此对该疾病的负担有很大影响。传统上，认知功能是通过让受试者完成特定模态的心理测量任务并使用标准方差分析来分析表现进行研究的。虽然这种方法提供了信息，但可能会忽略更深层次的感知和推理机制，这些机制可能潜在地统一明显不同的情绪和认知缺陷。在这里，我们试图阐明在患有忧郁症和非忧郁症抑郁症的个体中检测情绪显著信号背后的基本心理物理过程。
60名参与者在消极、积极和中性目标刺激块中完成了一项情感Go/No-Go（AGN）任务。我们采用分层贝叶斯信号检测理论（SDT）对患有忧郁症抑郁症、非忧郁症抑郁症的三组以及健康对照组的心理测量表现进行建模。这种方法估计了可能的反应概况（偏差）和感知敏感性（可辨别性）。这些测量值的均值差异反映了不同组个体之间情绪信号检测的差异，而方差差异则反映了各组本身的异质性。
与非忧郁症组相比，忧郁症参与者对积极情绪刺激的敏感性显著降低，并且在检测中性信号时，其可辨别性也显著低于健康对照组。忧郁症组在对积极和消极情绪显著材料的偏差方面也表现出显著更高的变异性。
忧郁症抑郁症中情绪信号检测的障碍似乎取决于情绪背景，在检测积极刺激时存在偏差，这与中性刺激的更嘈杂表征一致。忧郁症组偏差的更大异质性与更不稳定的疾病（即一天中变化不定）一致。未来的工作旨在了解这些发现如何反映特定的个体差异（例如先前的认知偏差），并阐明在认知任务中，随着感官内部模型根据经验进行完善和更新，这种偏差是否会动态变化。
