Individuals who possess extensive domain knowledge use their knowledge when understanding, discussing, and remembering events. The purpose of this study was to assess the extent to which natural language processing (NLP) tools could be used to predict domain knowledge from typed descriptions of events. Participants watched videos of basketball and recalled them after viewing. Knowledge of basketball was assessed. NLP tools were utilized to assess whether linguistic features of participants’ event descriptions could be used to predict domain knowledge. Moreover, the extent to which linguistic indices could be classified as relating to linguistic complexity or features of mental model construction was explored. Results from machine learning models suggest that domain knowledge (high, low) could be predicted with up to 90% accuracy. Additionally, 90% of predictors could be categorized. Higher knowledge individuals tended to describe events with more linguistic complexity and produced more words related to spatial, temporal, and social relations.

In a recent wave of events, President Obama had announced his open acceptance and support of gay marriage in America; many celebrities have endorsed and said “It Gets Better”, to help with gay teenagers – and the majority of America stands behind them. But the trend towards an acceptance of gays has been established way before Obama, Lady Gaga, or “It Gets Better” Campaign. In this study, I seek the answer to the puzzling question of, “what made the public opinion change regarding the gay population?” After looking at public opinion trend, I seek the answer through some quantitative ways: through the political interest groups’ financial and media data; seeking media infiltration of “gay” in the news and TV shows; and lastly, the elite’s opinion through the channels of Supreme Court of the United States, State of the Union Address, and Presidential Campaign platforms of each parties. My data analysis goes back through roughly last thirty-five years (from 1977 to the present) of what “gay” is, and how it changed in the eyes of the American public. As none of the previous literature does a comprehensive look at all three factors as the change in attitude towards “the gays”, I discover that there is a general pattern, as there are more influences in the interest groups, the media, and the elites’ opinion, the public opinion in America went up, seemingly the former leading the latter; however, that pattern is not always so true – in fact, public opinion may lead elite opinion or media exposure.

Although Artificial Intelligence (AI) is expected to outperform humans in many domains of decision-making, the process by which AI arrives at its superior decisions is often hidden and too complex for humans to fully grasp. As a result, humans may find it difficult to learn from AI, and accordingly, our knowledge about whether and how humans learn from AI is also limited. In this paper, we aim to expand our understanding by examining human decision-making in the board game Go. Our analysis of 1.3 million move decisions made by professional Go players suggests that people learned to make decisions like AI after they observe reasoning processes of AI, rather than mere actions of AI. Follow-up analyses compared the decision quality of two groups of players: those who had access to AI programs and those who did not. In line with the initial results, decision quality significantly improved for the players with AI access after they gained access to reasoning processes of AI, but not for the players without AI access. Our results demonstrate that humans can learn from AI even in a complex domain where the computation process of AI is also complicated.