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Semantic alignment is a key process underlying interpersonal and team communication. However, semantic similarity is difficult to quantify, and statistical approaches designed to measure it often rely on methods that make the identification of the relative importance of key words difficult. This study outlines how conceptual recurrence analysis (CRA) can address these issues and can be used to detect conceptual structure in interpersonal communication. We developed several novel CRA metrics to analyze communication data reported previously by Mancuso, Finomore, Rahill, Blair, and Funke (Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 58, 405-409, 2014), gathered from teams who worked cooperatively on a logic puzzle under different cognitive biasing contexts. CRA, like other measures of semantic coordination, relies on parameters whose values affect estimates of semantic alignment. We evaluated how the dimensionality of semantic spaces affects metrics quantifying the conceptual similarity of communicative exchanges, and whether metrics calculated from top-down, a priori semantic spaces or bottom-up semantic spaces empirically derived from each data set were more sensitive to biasing context. We found that the novel CRA measures were sensitive to manipulations of cognitive bias, and that higher-dimensional, bottom-up semantic spaces generally yielded more sensitivity to the experimental manipulations, though when the communication was evaluated with respect to specific key concepts, lower-dimensional, top-down spaces performed nearly as well. We conclude that CRA is sensitive to experimental manipulations in ways consistent with prior findings and that it presents a customizable framework for testing predictions about interpersonal communication patterns and other linguistic exchanges.

Beyond frequency counts: Novel conceptual recurrence analysis metrics to index semantic coordination in team communications