Moustafa Ahmed A, Kéri Szabolcs, Somlai Zsuzsanna, Balsdon Tarryn, Frydecka Dorota, Misiak Blazej, White Corey
School of Social Sciences and Psychology, Marcs Institute for Brain and Behaviour, University of Western Sydney, Penrith, NSW, Australia.
Nyírő Gyula Hospital-National Institute of Psychiatry and Addictions, Budapest, Hungary; University of Szeged, Faculty of Medicine, Department of Physiology, Szeged, Hungary; Budapest University of Technology and Economics, Department of Cognitive Science, Hungary.
Behav Brain Res. 2015 Sep 15;291:147-154. doi: 10.1016/j.bbr.2015.05.024. Epub 2015 May 22.
In this study, we tested reward- and punishment learning performance using a probabilistic classification learning task in patients with schizophrenia (n=37) and healthy controls (n=48). We also fit subjects' data using a Drift Diffusion Model (DDM) of simple decisions to investigate which components of the decision process differ between patients and controls. Modeling results show between-group differences in multiple components of the decision process. Specifically, patients had slower motor/encoding time, higher response caution (favoring accuracy over speed), and a deficit in classification learning for punishment, but not reward, trials. The results suggest that patients with schizophrenia adopt a compensatory strategy of favoring accuracy over speed to improve performance, yet still show signs of a deficit in learning based on negative feedback. Our data highlights the importance of applying fitting models (particularly drift diffusion models) to behavioral data. The implications of these findings are discussed relative to theories of schizophrenia and cognitive processing.
在本研究中，我们使用概率分类学习任务测试了精神分裂症患者（n = 37）和健康对照者（n = 48）的奖惩学习表现。我们还使用简单决策的漂移扩散模型（DDM）对受试者的数据进行拟合，以研究患者和对照者在决策过程的哪些组成部分存在差异。建模结果显示，决策过程的多个组成部分存在组间差异。具体而言，患者的运动/编码时间较慢，反应更为谨慎（更注重准确性而非速度），并且在惩罚试验（而非奖励试验）的分类学习中存在缺陷。结果表明，精神分裂症患者采取了一种更注重准确性而非速度的补偿策略来提高表现，但基于负面反馈的学习仍显示出缺陷迹象。我们的数据突出了将拟合模型（特别是漂移扩散模型）应用于行为数据的重要性。相对于精神分裂症理论和认知加工，我们讨论了这些发现的意义。
