Taming big data: Applying the experimental method to naturalistic data sets.

Psychological researchers have traditionally focused on lab-based experiments to test their theories and hypotheses. Although the lab provides excellent facilities for controlled testing, some questions are best explored by collecting information that is difficult to obtain in the lab. The vast amounts of data now available to researchers can be a valuable resource in this respect. By incorporating this new realm of data and translating it into traditional laboratory methods, we can expand the reach of the lab into the wilderness of human society. This study demonstrates how the troves of linguistic data generated by humans can be used to test theories about cognition and representation. It also suggests how similar interpretations can be made of other research in cognition. The first case tests a long-standing prediction of Gentner's natural partition hypothesis: that verb meaning is more subject to change due to the textual context in which it appears than is the meaning of nouns. Within a diachronic corpus, verbs and other relational words indeed showed more evidence of semantic change than did concrete nouns. In the second case, corpus statistics were employed to empirically support the existence of phonesthemes-nonmorphemic units of sound that are associated with aspects of meaning. A third study also supported this measure, by demonstrating that it corresponds with performance in a lab experiment. Neither of these questions can be adequately explored without the use of big data in the form of linguistic corpora.

Language Use and Coalition Formation in Multiparty Negotiations.

The alignment of bargaining positions is crucial to a successful negotiation. Prior research has shown that similarity in language use is indicative of the conceptual alignment of interlocutors. We use latent semantic analysis to explore how the similarity of language use between negotiating parties develops over the course of a three-party negotiation. Results show that parties that reach an agreement show a gradual increase in language similarity over the course of the negotiation. Furthermore, reaching the most financially efficient outcome is dependent on similarity in language use between the parties that have the most to gain from such an outcome.

Purity homophily in social networks.

Does sharing moral values encourage people to connect and form communities? The importance of moral homophily (love of same) has been recognized by social scientists, but the types of moral similarities that drive this phenomenon are still unknown. Using both large-scale, observational social-media analyses and behavioral lab experiments, the authors investigated which types of moral similarities influence tie formations. Analysis of a corpus of over 700,000 tweets revealed that the distance between 2 people in a social-network can be predicted based on differences in the moral purity content-but not other moral content-of their messages. The authors replicated this finding by experimentally manipulating perceived moral difference (Study 2) and similarity (Study 3) in the lab and demonstrating that purity differences play a significant role in social distancing. These results indicate that social network processes reflect moral selection, and both online and offline differences in moral purity concerns are particularly predictive of social distance. This research is an attempt to study morality indirectly using an observational big-data study complemented with 2 confirmatory behavioral experiments carried out using traditional social-psychology methodology.

Identity, causality, and pronoun ambiguity.

This article looks at the way people determine the antecedent of a pronoun in sentence pairs, such as: Albert invited Ron to dinner. He spent hours cleaning the house. The experiment reported here is motivated by the idea that such judgments depend on reasoning about identity (e.g., the identity of the he who cleaned the house). Because the identity of an individual over time depends on the causal-historical path connecting the stages of the individual, the correct antecedent will also depend on causal connections. The experiment varied how likely it is that the event of the first sentence (e.g., the invitation) would cause the event of the second (the house cleaning) for each of the two individuals (the likelihood that if Albert invited Ron to dinner, this would cause Albert to clean the house, versus cause Ron to clean the house). Decisions about the antecedent followed causal likelihood. A mathematical model of causal identity accounted for most of the key aspects of the data from the individual sentence pairs.

Identifying issue frames in text.

Framing, the effect of context on cognitive processes, is a prominent topic of research in psychology and public opinion research. Research on framing has traditionally relied on controlled experiments and manually annotated document collections. In this paper we present a method that allows for quantifying the relative strengths of competing linguistic frames based on corpus analysis. This method requires little human intervention and can therefore be efficiently applied to large bodies of text. We demonstrate its effectiveness by tracking changes in the framing of terror over time and comparing the framing of abortion by Democrats and Republicans in the U.S.

What difference reveals about similarity.

Detecting that two images are different is faster for highly dissimilar images than for highly similar images. Paradoxically, we showed that the reverse occurs when people are asked to describe how two images differ--that is, to state a difference between two images. Following structure-mapping theory, we propose that this disassociation arises from the multistage nature of the comparison process. Detecting that two images are different can be done in the initial (local-matching) stage, but only for pairs with low overlap; thus, "different" responses are faster for low-similarity than for high-similarity pairs. In contrast, identifying a specific difference generally requires a full structural alignment of the two images, and this alignment process is faster for high-similarity pairs. We described four experiments that demonstrate this dissociation and show that the results can be simulated using the Structure-Mapping Engine. These results pose a significant challenge for nonstructural accounts of similarity comparison and suggest that structural alignment processes play a significant role in visual comparison.