Structural alignment and cooperation

Marek Placiński

This study investigates the relations that hold between cooperation and linguistic alignment. Linguistic alignment, as defined by Pickering and Garrod (2004; 2005; 2006; 2013) can be defined as mimicry in terms of the vocabulary and structures used in conversation. Though Pickering and Garrod posit that alignment is reached as an outcome of an automatic priming mechanism, studies have shown that it is in fact affected by extra-linguistic context, such as status (Lev-Ari & Peperkamp, 2017), pro-social behaviour (van Baaren et al., 2004; Kulesza et al., 2014), or perception of the interlocutor (Schoot et al., 2016; Balcetis & Dale, 2005; Weatherholtz et al., 2014). Alignment in general, and structural alignment specifically, is beyond conscious control (Branigan et al., 2003), rendering it similar to low-level coordinative devices, which Wacewicz et al. (2017) argue to lay at the foundations of non-linguistic cooperation. Similarly to these devices, structural alignment is a reliable (difficult to fake) and therefore a trustworthy signal, which is why it may modulate cooperative behaviours. Therefore, we hypothesise that structural alignment is a predictor of cooperative success. As a method of testing the hypothesis, we designed a study in which we test structural similarity between interactants and cooperative success. To this end, we gathered a corpus of online conversations from the Ubuntu Conversation Corpus (Uthus & Aha, 2013), containing multi-participant synchronous conversations on the topic of technical problems related to computers. In total, we gathered 207 (n = 207) conversations that were parsed for constituent structure without terminal and unary production rules. The magnitude of alignment in these conversations was computed as a ratio of the number of unique production rules to the total number of production rules in a given conversation. The lower the ratio, the greater the alignment. Each conversation was then classified as “solved” or “unresolved”, depending on whether a given problem was solved or not. The conversations were controlled for length, as longer conversations tend to promote greater alignment (Pickering & Garrod, 2006). In total, with outliers removed, the corpus contained 86 solved (n = 86) and 105 (n = 105) unresolved conversations. We conducted two statistical tests to test the hypothesis, determining (1) whether there is a difference between the two outcomes in terms of alignment magnitude, and (2) whether alignment magnitude can predict cooperative success. One-way ANOVA was conducted to test (1), which revealed that there is a significant difference between the two samples (p = 0.001); a post-hoc Tukey’s HSD test (equal variances, p = 0.3) showed that the ratio is greater in the unresolved sample (p = 0.002). A logistic regression investigating (2)
revealed significant results (p = 0.001). In conclusion, we confirm our hypothesis that there is a link between cooperative success and alignment. Therefore, we provide empirical evidence for the fact that lower-level linguistic representations are conducive to increased cooperation between individuals.


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