Dynamics of task-based confidence in schizophrenia using seasonal decomposition approach

IF 2.3 Q2 PSYCHIATRY
Varsha D. Badal , Colin A. Depp , Amy E. Pinkham , Philip D. Harvey
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Abstract

Objective

Introspective Accuracy (IA) is a metacognitive construct that refers to alignment of self-generated accuracy judgments, confidence, and objective information regarding performance. IA not only refers to accuracy and confidence during tasks, but also predicts functional outcomes. The consistency and magnitude of IA deficits suggest a sustained disconnect between self-assessments and actual performance. The cognitive origins of IA are unclear and are not simply due to poor performance. We tried to capture task and diagnosis-related differences through examining confidence as a timeseries.

Method

This relatively large sample (N = 171; Bipolar = 71, Schizophrenia = 100) study used item by item confidence judgments for tasks including the Wisconsin Card Sorting Task (WCST) and the Emotion Recognition task (ER-40). Using a seasonal decomposition approach and AutoRegressive, Integrative and Moving Averages (ARIMA) time-series analyses we tested for the presence of randomness and perseveration.

Results

For the WCST, comparisons across participants with schizophrenia and bipolar disorder found similar trends and residuals, thus excluding perseverative or random responding. However, seasonal components were weaker in participants with schizophrenia, reflecting a reduced impact of feedback on confidence. In contrast, for the ER40, which does not require identification of a sustained construct, seasonal, trend, and residual analyses were highly comparable.

Conclusion

Seasonal analysis revealed that confidence judgments in participants with schizophrenia on tasks requiring responses to feedback reflected diminished incorporation of external information, not random or preservative responding. These analyses highlight how time series analyses can specify potential faulty processes for future intervention.

Abstract Image

Abstract Image

精神分裂症任务型置信度的季节分解动态研究
客观反思准确性(IA)是一种元认知结构,指的是自我产生的准确性判断、信心和关于表现的客观信息的一致性。IA不仅指任务中的准确性和信心,还预测功能结果。IA缺陷的一致性和严重性表明自我评估与实际表现之间存在持续的脱节。IA的认知起源尚不清楚,不仅仅是由于表现不佳。我们试图通过将置信度作为时间序列进行检验来捕捉与任务和诊断相关的差异。方法这项相对较大的样本(N=171;双相情感障碍=71,精神分裂症=100)研究对包括威斯康星卡片分类任务(WCST)和情绪识别任务(ER-40)在内的任务进行了逐项置信度判断。使用季节分解方法和自回归、积分和移动平均(ARIMA)时间序列分析,我们测试了随机性和持久性的存在。结果对于WCST,精神分裂症和双相情感障碍参与者之间的比较发现了相似的趋势和残差,因此排除了持续或随机反应。然而,精神分裂症参与者的季节性成分较弱,反映出反馈对信心的影响减少。相反,对于不需要识别持续结构的ER40,季节性、趋势性和残差分析具有高度可比性。结论季节性分析显示,精神分裂症参与者对需要对反馈做出反应的任务的信心判断反映了外部信息的结合减少,而不是随机或保留反应。这些分析强调了时间序列分析如何指定未来干预的潜在故障过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.60
自引率
10.70%
发文量
54
审稿时长
67 days
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