D.Reirter.I.D.Moore/of Memory and Longuage 76 (2014)29-46 suspected to becomes less likely as the dista red in utterance from the first ence incr In(FREQ n summ hboard In(DiST is gives us an indicatio 8= -0.080.p0001),and the effect is reduced by we th on prot d to t fo In(FREO)interacts with In(DiST)(=0.05 rime to 15s or 25 utterances afterward (predictor:In(DisT)Thus,we is,we find less priming for more common rules. take has not been repeated. Discussion Experiment 1:repetition in corpora nce i repea While controlled expe o one another.the str ments have showr ifypriming by estimating the decay effect was developed that is in semantic The priming effect obtained in these corpora confirm common goal. onger time periods Method els indisti guishable from the prior after abou glance g separnatetgdcs Sh rke ously.Thmi that。i to)decav only after 140 words hich would be rd(Marcus et al ).a corpus of spontane mately 45s ata speech rate/min)Howeve mly paire most of the priming effect"dedline topic to discuss.but were otherwise unrestricted.The cor 0 words ent【oca.5s yielding 472,000 phrase structure rules with 4700 distinc ceeudedinh Niss 2004).After extracting all potential cann e he im inganequ number of repetition and non-repetit cas The second da is the M hlp3gecoaaining20400uteranCeg Experiment 2:priming and decay over time in different genres In this section.we develop the first of two hypotheses Results designed to test the IAM or some of its assumptions. Two r ssion models were fitted.one to each datase ingse (Table 2)Th ycontain the n()covariate toestima 0911ae dcha be ed t identify comprehension-production prming between na the A In Map Task,In(DIsT)reliably predicts declining rule repetition (8=-0.073.p<.0001).Repetition of a rule distance (p<0000)psycholinguistic models and has long been suspected to interact with priming (e.g., Scheepers, 2003). In summary, our model demonstrates a priming effect by observing a decay, that is, a negative parameter for lnðDistÞ. How strong this decay is gives us an indication of how much repetition probability we see shortly after the stimulus (prime) compared to the probability of chance repetition—without ever explicitly calculating such a prior. We define the strength of priming as the decay rate of rep￾etition probability, from shortly after the prime to 15 s or 25 utterances afterward (predictor: lnðDistÞ). Thus, we take several samples at varying distances (d), looking at cases of structural repetition, and cases where structure has not been repeated. Experiment 1: repetition in corpora While controlled experiments have shown syntactic priming, we first aim to demonstrate a sensitive method that can quantify and contrast priming magnitudes in cor￾pora. We will examine two types of text: (a) spontaneous conversation, that is, in a situation where the semantics of the dialogue are not controlled and (b) task-oriented dialogue, where interlocutors collaborate to achieve a common goal. Method We use two datasets in this experiment and build two separate statistical models. Short-term priming effects are measured as described previously. The first dataset is Switchboard (Marcus et al., 1994), a corpus of spontaneous spoken telephone dialogues among randomly paired, North American English speakers who were given a general topic to discuss, but were otherwise unrestricted. The cor￾pus contains 80,000 transcribed utterances were anno￾tated with phrase structure trees (Marcus et al., 1994), yielding 472,000 phrase structure rules with 4700 distinct rules. Words in this portion of the corpus, included in the Penn Treebank, were time-tagged (Carletta, Dingare, Nissim, & Nikitina, 2004). After extracting all potential rep￾etition cases, the data were balanced by re-sampling, yield￾ing an equal number of repetition and non-repetition cases. The second dataset is the HCRC Map Task corpus (Anderson et al., 1991), which consists of 128 task-oriented dialogues containing 20,400 utterances, using 759 differ￾ent phrase structure rules. Using exactly the same method￾ology as for Switchboard, we extracted 157,000 rules. Results Two regression models were fitted, one to each dataset (Table 2). They contain the lnðDistÞ covariate to estimate priming levels (negative effects indicate stronger priming), lnðFreqÞ for the effects of frequency, and a factor CP (to identify comprehension-production priming between speakers). In Map Task, lnðDistÞ reliably predicts declining rule repetition (b ¼ 0:073; p < :0001). Repetition of a rule becomes less likely as the distance measured in utterances from the first occurrence increases: lnðFreqÞ interacts reliably with lnðDistÞ (b ¼ 0:043; p < :0001). In Switchboard, lnðDistÞ also predicts declining rule repetition (b ¼ 0:080; p < :0001), and the effect is reduced by increasing frequency. Prime Type CP (priming between speakers) does not interact with the decay coefficient for lnðDistÞ. 3 lnðFreqÞ interacts with lnðDistÞ (b ¼ 0:057; p < :0001), which suggests that repetition probability decreases less quickly for rules with high frequencies. That is, we find less priming for more common rules. Discussion A speaker is more likely to use a syntactic rule shortly after using the same rule. The closer prime and target are to one another, the stronger the preference is to repeat. Priming occurs both within a speaker (PP) and between speakers (CP), and it decays rapidly. The method to quan￾tify priming by estimating the decay effect was developed initially for the Switchboard corpus; Map Task was not used to design or tune the regression modeling methods. The priming effect obtained in these corpora confirms experimental results by Bock and Griffin (2000) and Branigan, Pickering, and Cleland (1999). These studies find syntactic priming over short and longer time periods.4 The decay we observe is remarkable: repetition rates reach lev￾els indistinguishable from the prior after about 5–6 s. At first glance, this contrasts with Szmrecsanyi (2006, p. 188) results, who finds that future marker choices (will vs. going to) decay only after 140 words (which would be approxi￾mately 45 s at a speech rate of 180 words/min). However, as Szmrecsanyi points out, due to the logarithmic nature of the forgetting function, most of the priming effect ‘‘declines within an interval of 10 words (.), equivalent to ca. 5 s of speech.’’ With our data, a log-linear model (for distance) yielded a better fit than a linear–linear one,5 which is com￾patible with general models of memory (Anderson, Bothell, Lebiere, & Matessa, 1998). The models produced for Switchboard and Map Task cannot be used to quantify the strengths of syntactic prim￾ing; they just show the decay effects separately for the two corpora. In the next experiment, we compare priming between the corpora. Experiment 2: priming and decay over time in different genres In this section, we develop the first of two hypotheses designed to test the IAM or some of its assumptions. 3 The resulting estimate for lnðDistÞ in our model (for a syntactic rule of average frequency) would be 0:080 for PP (odds ratio: 0.92), but 0.080 to 0.017 (odds ratio 0.91) for CP priming. Because a negative b indicates decay, this indicates CP and PP priming in Switchboard. 4 The effect of CP on bias may be related to general levels of speaker idiosyncrasies, i.e., increased chance repetition within speakers. Fitting the main effect controls for that. 5 Applying the Akaike Information Criterion, the model in Table 3 would be exceedingly unlikely, if it employed linear distance instead of log-linear distance (p < :0000). 34 D. Reitter, J.D. Moore / Journal of Memory and Language 76 (2014) 29–46