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 repetition 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 corpora. 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 corpus contains 80,000 transcribed utterances were annotated 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 repetition cases, the data were balanced by re-sampling, yielding 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 different phrase structure rules. Using exactly the same methodology 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 quantify 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 levels 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 approximately 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 compatible 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 priming; 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
D.Reitter.JD.Moore/Joumal of Memory and Language 76(2014)29-6 35 odels of short-tes 1 PP is th cepted correlation between covariates 02:CP was resid ManTask Switchboard SE OR 0011 050 -0.176 084 (T 0.011 005 The speakers were told that their goal was pear to paint a different upposes ratio say whatever was necessa to compl te the task.It wa that s that an municative or situationl outcome If we accept this as All maps consisted of landmarks represented as line M5son.1989 priming levels vary at all with dialogue purpose.they tend task-rtd dialogue marked only on the giver's map.Landmarks ong th First.ifpr h s,4 onon the giv infuenced by dialogue purpos or contextual working (typically one per map pair)had diffe memory conter nts,then we .The seemore pri iction or ev dintecrori ce on 。e giver's ma sitio once a whole. The follower had only one repeated landmark.which was minrhhn twice as tailored their utterances to match the instruction giver e grammand syn room and half of the pairs could mak eeye contact.From tation in M his is contrary to what would be We pool the two datasets(Switchboard and Map Task) 0 will be the remainder of this paper. them ls ac or the experiments,except that the Disr covariate is now mea Like Switchboard.the Map Task is a corpus of spoke wo-person.th length differs). interlocutors work together to perform a task as quickly obtain the
The IAM suggests that priming benefits speakers in conversation. At the same time, we observe that independently fitted statistical models appear to paint a different picture of priming in spontaneous conversation, as opposed to priming in task-oriented dialogue. The test of the IAM we put forward presupposes rationality in cognitive processes, that is, that variation in an individual’s linguistic processes tends to optimize the communicative or situational outcome. If we accept this as a general principle (Anderson & Milson, 1989; Chater & Oaksford, 1999), then the IAM predicts that if speaker’s priming levels vary at all with dialogue purpose, they tend to vary such that task-oriented dialogue shows stronger priming than less goal-driven interaction, i.e., spontaneous conversation or small talk. Let us briefly consider the alternatives. First, if priming is the result of a mechanistic memory effect that is not influenced by dialogue purpose or contextual working memory contents, then we should not observe any difference in priming between the dialogue genres. Second, if we do find different priming levels, and we see more priming in spontaneous conversation, we would interpret this as a violation of the IAM prediction or even rationality as a whole. The differences in dialogue situation may have affected priming levels through a different mechanism than IAM. Speakers may have tailored their utterances to match the needs of their audience: In the experimental design that led to the Map Task data, participants were in the same room and half of the pairs could make eye contact. From an audience design perspective, the richer communication channel may have led them to reduce their levels of adaptation in Map Task. This is contrary to what would be expected under the IAM. Next, we describe the Map Task in detail. This corpus will be used throughout the remainder of this paper. The Map Task Like Switchboard, the Map Task is a corpus of spoken, two-person dialogues in English. Unlike Switchboard, the Map Task dialogues are task-oriented dialogues, in which interlocutors work together to perform a task as quickly and efficiently as possible. In each trial, the two speakers sat opposite one another and each had a map, which the other could not see. One of them, the instruction giver, had a map with a route drawn on it; the other participant, the instruction follower, had no route drawn on her map. The speakers were told that their goal was to reproduce the Instruction giver’s route on the Instruction follower’s map. The maps were not identical, and before they began the task the participants were told explicitly that their maps may differ in some respects, and that they could say whatever was necessary to complete the task. It was up to the participants to discover how the two maps differed (see Figs. 4 and 5). All maps consisted of landmarks represented as line drawings which are labelled with their intended name. All map routes began with a starting point, which was marked on both maps, and an end point, which was marked only on the giver’s map. Landmarks along the map alternated between those that appeared on both maps and those that appeared on only one map. For each map, 8 landmarks appeared on both maps, 4 on only the giver’s map, and 3 on only the follower’s map. In addition, some landmarks (typically one per map pair) had different names on the two maps. These names were identical in form and location but had different labels on the two maps (e.g., mill wheel vs. old mill). Finally, 2 landmarks appeared twice on the giver’s map, once in a position close to the route and once in a position more distant from the route. The follower had only one repeated landmark, which was distant. Each subject participated in four dialogues, twice as instruction giver and twice as instruction follower. The spoken interactions were recorded, transcribed and syntactically annotated with phrase structure grammar.6 Method We pool the two datasets (Switchboard and Map Task), distinguishing them via a factor SOURCE. The methodology to quantify priming levels is the same as for the previous experiments, except that the DIST covariate is now measured in seconds instead of utterances (the notion of utterance is not the same in each corpus, and average utterance length differs).7 Table 2 Two regression models of short-term rule repetition (Experiment 1). Prime-target distance in utterances. All continuous predictors were centred; CP was coded as 1, PP is the base case. Response variable (repetition probability), effect sizes (b) and standard errors (SE) in logits. Random effects of intercept and slope (distance), grouped by utterance. Maximum accepted correlation between covariates 0.2; CP was residualized. MapTask Switchboard Covariate b OR SE b OR SE Intercept 1:721 0.18 0:011⁄⁄⁄ 1:079 0.34 0:025⁄⁄⁄ lnðDistÞ 0:073 0.93 0:011⁄⁄⁄ 0:080 0.92 0:012⁄⁄⁄ lnðFreqÞ 0:722 2.06 0:01⁄⁄⁄ 0:884 2.42 0:006⁄⁄⁄ CP 0:684 0.50 0:013⁄⁄⁄ 0:176 0.84 0:011⁄⁄⁄ lnðDistÞ:CP 0:018 0.98 0:019 0:017 0.98 0:014 lnðDistÞ:lnðFreqÞ 0:043 1.04 0:011⁄⁄⁄ 0:057 1.06 0:006⁄⁄⁄ ⁄ p < 0.05. ⁄⁄⁄ p < 0.0001 (by jzj) . 6 Many other types of annotation are also available. See http:// www.hcrc.ed.ac.uk/maptask/ for a description and instructions of how to obtain the corpus. 7 Elsewhere, we have documented that time-based vs. utterance-based analysis does not confound the comparisons between the corpora Reitter (2008). D. Reitter, J.D. Moore / Journal of Memory and Language 76 (2014) 29–46 35