D.Reitter.JD.Moore/Joumal of Memory and Language 76(2014)29-6 37 tactic rules.In both corpora.we also found reliable effects sed at a time e red to y et al.(2 vay)?Th the lo measure In the Map Task c y indirectly(e.g.via topi which consists of task nted dialogues find evidence for stronger overall priming ng evidence for the Experiment 3:task success and short-term priming of the sit- To test the AM sting of syntactic priming and the alignme ation mo dels present in task ed dialogue.The inter is designed to detect co-variance of short-term priming priming() d b munic s.CPpriming and task success. 0 Method make fine-grained rout tition.As Map a concurrent explanation semantic and le material have facilitated is me asured in ute that th wer ha We concede that dialogues in the two corpora differ rs of the Map Task the th ters and their linguistic variability.While d ir e of cav-bas can be expe We cor uage.it is still unclear which differences petween the c g m ally cau DE e thi (s) b (a task su ed co diti to distinguish sh nd l a positive estimate for this interaction syntact arge corpora present us with an oppe rtunity to evalu Results poraenctioaretndtapoint 005).For ins Table ows the full model.As before.short-tern a single utteran will typ 150,P<.0001).N bly.how path devi another.I orpus study ted the (andom sub-languages or by contrasting primed and non-primed sam Discussion by using decay as the taget metric Ne hav a clea A final methodological concern is coherence:adjacent utterances do not jump from topic to topic- -inst does this i ndic tha coherent(Grosz&Sidner.1986)Clustering may be bu tactic rules. In both corpora, we also found reliable effects of both production-production (PP) priming (self-priming) and comprehension-production (CP) priming. With the clear PP priming effect in spontaneous conversation, we also add a new finding compared to Dubey et al. (2005), who did not detect reliable evidence of adaptation within speakers in Switchboard for selected syntactic rules in coordinate structures. In the Map Task corpus, which consists of task-oriented dialogues, we find evidence for stronger overall priming than in Switchboard, a corpus of spontaneous conversa￾tion. We consider this effect supporting evidence for the Interactive Alignment Model (Pickering & Garrod, 2004). According to the IAM, what we observe is the reciprocal boosting of syntactic priming and the alignment of the sit￾uation models present in task-oriented dialogue. The inter￾action partners synchronize their situation models in the task-oriented setting, which co-occurs with cross-speaker priming (CP) on other communicative levels. CP priming appears to be enhanced by the need for a shared situation model. Recurring coordination moves enable speakers to make fine-grained distinctions of the path described, and these may provide an explanation for increased local repe￾tition. As a concurrent explanation, semantic and lexical material that occurs in clusters may also have facilitated local syntactic repetition. We concede that dialogues in the two corpora differ greatly with respect to the overall goals of the speakers, their mode of interaction, the durations of their turns, their language registers and their linguistic variability. While the underlying, decay-based methodology can be expected to be robust with respect to general differences in lan￾guage, it is still unclear which differences between the cor￾pora actually caused priming to be stronger in Map Task. The next experiments address this concern. We will exam￾ine only data from the Map Task corpus, which was col￾lected under well-controlled conditions. We also broaden our view to distinguish short-term and long-term adapta￾tion, and to evaluate to what extent task success can be predicted and estimated based on lexical and syntactic adaptation. Large corpora present us with an opportunity to evalu￾ate small effects and multiple interactions. Yet, data points gained from linguistic corpora are never independent (Kilgarriff, 2005). For instance, a single utterance will typ￾ically yield multiple syntactic data points, but of course, the choices of syntactic constructions in a sentence depend heavily on one another. In the corpus study presented here, care is taken to group such linguistic interdependencies in the (random effects) models. A further issue arises due to sub-languages resulting from corpus choice, genre, or speaker. The model structure controls for such variation by contrasting primed and non-primed samples within the same corpus, and by using decay as the target metric to measure priming. A final methodological concern is coherence: adjacent utterances do not jump from topic to topic—instead, they form clusters or discourse segments that are topically coherent (Grosz & Sidner, 1986). Clustering may be present as a result of convention or processing constraints, but it may also be introduced by the task as it is in Map Task, where the path is typically drawn step-by-step, with the area around one landmark being discussed at a time. Could clusters be responsible for the short-term priming effect, producing more repetition inside a cluster than outside (and further away)? This potential confound would affect the short-term priming, but not the long-term adaptation measure. Most importantly, topic chains are reflected pri￾marily in lexical choice, and only indirectly (e.g., via topic status) in syntactic configuration. Experiment 3: task success and short-term priming Under the IAM, we expect successful dialogues to show more priming than unsuccessful ones. To test the IAM hypothesis, we assume that success at the Map Task is an indicator of aligned situation models. The next experiment is designed to detect co-variance of short-term priming and task success. Method The Map Task consists of re-tracing a defined route according to the interactive description provided by the other interlocutor. So, task performance is measured in terms of how far the route that the follower has drawn deviates from the route shown on the giver’s map. To com￾pute this for each dialogue, the developers of the Map Task corpus overlaid the giver’s map on the follower’s map and computed the area covered in between the paths (PATHDEV). Task success is then defined as the inverse of PATHDEV. We correlate short-term priming levels in each dialogue with path deviation. The underlying model is the same as in Experiment 1, except that an interaction of DIST and PATH￾DEV is included to measure this relationship. Prime-target distance lnðDistÞ is measured in time (s). Under the IAM, we expect there to be more priming with greater task suc￾cess. As DIST is lower for stronger priming, and PATHDEV is lower for more successful dialogue outcomes, we expect a positive estimate for this interaction. Results Table 4 shows the full model. As before, short-term priming is reliably correlated (negatively) with lnðDistÞ, hence we see a decay and priming effect (lnðDistÞ, b ¼ 0:150; p < :0001). Notably, however, path deviation and short-term priming did not correlate. We tested for reliable PATHDEV and lnðDistÞ interactions, separately for PP and CP situations via contrasts. In neither case did we find a reliable interaction. Discussion We have shown that although there is a clear priming effect in the short term, the size of this priming effect does not correlate with task success. But does this indicate that there is no strong functional component to priming in the dialogue context? There may still be an influence of cogni￾tive load due to speakers working on the task, or an overall disposition for higher priming in task-oriented dialogue: D. Reitter, J.D. Moore / Journal of Memory and Language 76 (2014) 29–46 37