Department of Research Methods and Information Science, University of Denver, Denver, Colorado, United States of America.
Actor's Equity Association, New York, NY, United States of America.
PLoS One. 2020 Oct 22;15(10):e0240728. doi: 10.1371/journal.pone.0240728. eCollection 2020.
As a profession, acting is marked by a high-level of economic and social riskiness concomitantly with the possibility for artistic satisfaction and/or public admiration. Current understanding of the psychological attributes that distinguish professional actors is incomplete. Here, we compare samples of professional actors (n = 104), undergraduate student actors (n = 100), and non-acting adults (n = 92) on 26 psychological dimensions and use machine-learning methods to classify participants based on these attributes. Nearly all of the attributes measured here displayed significant univariate mean differences across the three groups, with the strongest effect sizes being on Creative Activities, Openness, and Extraversion. A cross-validated Least Absolute Shrinkage and Selection Operator (LASSO) classification model was capable of identifying actors (either professional or student) from non-actors with a 92% accuracy and was able to sort professional from student actors with a 96% accuracy when age was included in the model, and a 68% accuracy with only psychological attributes included. In these LASSO models, actors in general were distinguished by high levels of Openness, Assertiveness, and Elaboration, but professional actors were specifically marked by high levels of Originality, Volatility, and Literary Activities.
作为一种职业,表演伴随着艺术满足和/或公众赞赏的可能性,同时具有高度的经济和社会风险。目前对区分专业演员的心理特征的理解并不完整。在这里,我们将专业演员(n=104)、大学生演员(n=100)和非演员成年人(n=92)的样本进行比较,比较了 26 个心理维度,并使用机器学习方法根据这些属性对参与者进行分类。这里测量的几乎所有属性在三组之间都显示出显著的单变量均值差异,其中最强的效应大小是在创造性活动、开放性和外向性上。交叉验证的最小绝对收缩和选择算子(LASSO)分类模型能够以 92%的准确率从非演员中识别出演员(专业或学生),并且当模型中包含年龄时,能够以 96%的准确率将专业演员与学生演员区分开来,仅包含心理属性时的准确率为 68%。在这些 LASSO 模型中,一般来说,演员以高水平的开放性、果断性和详细阐述为特征,但专业演员的特点是具有高水平的创造力、波动性和文学活动。