Data, Syntax, and Materials for "'Forward Flow': A New Measure to Quantify Free Thought and Predict Creativity"

Data, syntax and thought plot for the eponymous paper in American Psychologist.

Web application:
www.forwardflow.org

Open-access source code for LSA engine:
https://github.com/chene5/dynamics

Abstract:
When the human mind is free to roam, its subjective experience is characterized by a continuously evolving stream of thought. Although there is a technique that captures people’s streams of free thought—free association—its utility for scientific research is undermined by two open questions: 1) How can streams of thought be quantified? and 2) Do such streams predict psychological phenomena? We resolve the first issue—quantification—by presenting a new metric, “forward flow,” that uses latent semantic analysis (LSA) to capture the semantic evolution of thoughts over time (i.e., how much present thoughts diverge from past thoughts). We resolve the second issue—prediction—by examining whether forward flow predicts creativity in the lab and the real-world. Our studies reveal that forward flow predicts creativity in college students (Study 1) and a representative sample of Americans (Study 2), even when controlling for intelligence. Studies also reveal that membership in real-world creative groups—performance majors (Study 3), professional actors (Study 4) and entrepreneurs (Study 5)—is predicted by forward flow, even when controlling for performance on divergent thinking tasks. Study 6 reveals that forward flow in celebrities’ social media posts (i.e., on Twitter) predicts their creative achievement. In addition to creativity, forward flow may also help predict mental illness, emotional experience, leadership ability, adaptability, neural dynamics, group productivity, and cultural success. We present open-access online tools at www.forwardflow.org for assessing and visualizing forward flow for both illustrative and large-scale data analytic purposes.

《美国心理学家》杂志中同名论文的数据、句法及思维轨迹。网络应用：www.forwardflow.org

开源的 LSA 引擎源代码：
https://github.com/chene5/dynamics

摘要：当人的心灵得以自由翱翔时，其主观体验表现为一种不断演进的思维流。尽管存在一种捕捉人们自由思维流的技术——自由联想——但其对科学研究的价值受到两个未解问题的质疑：1）如何量化思维流？2）此类思维流能否预测心理现象？我们通过提出一种新的度量指标——“正向流动”，利用潜在语义分析（LSA）来捕捉思维随时间的语义演变（即当前思维与过去思维的差异程度）解决了第一个问题——量化。通过考察“正向流动”是否能够预测实验室和现实世界的创造力，我们解决了第二个问题——预测。我们的研究表明，即使在控制智力的情况下，“正向流动”也能预测大学生的创造力（研究1）和代表性美国人的创造力（研究2）。研究还表明，即使在控制发散性思维任务表现的情况下，现实世界中的创造性群体——表演专业（研究3）、专业演员（研究4）和企业家（研究5）的成员资格也能通过“正向流动”预测。研究6揭示，名人在社交媒体帖子中的“正向流动”（即在推特上）可以预测他们的创造力成就。除了创造力之外，“正向流动”还可能有助于预测心理疾病、情绪体验、领导能力、适应性、神经动力学、群体生产力和文化成功。我们提供了开放访问的在线工具，可在 www.forwardflow.org 上用于评估和可视化“正向流动”，以用于说明性和大规模数据分析目的。