Discovering kinds of future-oriented thought using automated machine-learning techniques

Abstract

Humans have a remarkable ability to think about the future. Our abilities to think about the future are essential for the level of goal construction, planning, and execution of plans that is only observable in humans. Thinking about the future has also been found to be important for the development of sense of self and for health and well-being. In spite of the importance of future-oriented thought, very little empirical work has been conducted on the nature of future-oriented thought. In this research, we demonstrate how automated methodologies can be used to identify references to the future from natural text (Study 1) and how machine-learning techniques can be used to identify categories of future-oriented thought (Study 2). We also demonstrate how the categories that emerge from these analyses can help us better understand the relation between future-oriented thought and many of the positive outcomes associated with future-oriented thought (Study 3).


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