Braverman Julia
Department of Psychology, Boston College, Boston, MA 02467, USA.
Pers Soc Psychol Bull. 2005 Nov;31(11):1487-97. doi: 10.1177/0146167205276152.
The purpose of this research is to explore the effect of mood on the detection of covariation. Predictions were based on an assumption that sad moods facilitate a data-driven information elaboration style and careful data scrutinizing, whereas happy moods predispose individuals toward top-down information processing and decrease the attention given to cognitive tasks. The primary dependent variable involved is the detection of covariation between facial features and personal information and the use of this information for evaluating new target faces. The findings support the view that sad mood facilitates both conscious and unconscious detection of covariation because it increases motivation to engage in the task. Limiting available cognitive resources does not eliminate the effect of mood on the detecting of covariation.
本研究的目的是探讨情绪对协变检测的影响。预测基于这样一种假设,即悲伤情绪有助于形成数据驱动的信息加工方式和细致的数据审查,而快乐情绪则使个体倾向于自上而下的信息加工,并减少对认知任务的关注。所涉及的主要因变量是面部特征与个人信息之间协变的检测,以及利用这些信息评估新的目标面孔。研究结果支持了这样一种观点,即悲伤情绪有助于有意识和无意识地检测协变,因为它增加了参与任务的动机。限制可用的认知资源并不能消除情绪对协变检测的影响。