Overall, CNPC isles was basically rated rather higher than Subject isles (b = 0.09, SE = 0.03, |t| = dos.82). Additionally, critiques for CNPC target sentences was more than having primes it doesn’t matter off repetition sort of. By contrast, feedback getting prime and you can address Subject island phrases don’t differ.
Contour step one. Indicate evaluations getting Complex-NP Restriction (CNPC) and you can Subject islands inside Lag1. Brutal scores is actually displayed into a beneficial 5-part size, in which 1 = Totally Unsuitable and you can 5 = Completely Appropriate. Mistake taverns show ±step 1 standard mistake. Getting visibility, we reveal singular–3 facts for the scale.
Mathematically, there can be a serious effectation of demonstration sorts of (? = 0.05, SE = 0.02, |t| = 2.3), however, this is modulated by a limited sentence-by-demonstration telecommunications (? = 0.09, SE = 0.05, |t| = jak wysłać komuś wiadomość na fruzo step one.81). The presence of the correspondence impression suggests that priming will not exist across-the-board: Target sentences were far more appropriate than simply primes into the CNPC islands (? = 0.step 1, SE = 0.04, |t| = 2.67), however Topic isles (? = 0.01, SE = 0.03, |t| = 0.40).
There was no significant main effect of repetition type (? = -0.01, SE = 0.02, |t| = 0.41) and no significant interactions (|t|’s < 0.36) involving repetition type: Lexically repeating the head noun of the island itself vs. a phrase unrelated to the island did not affect ratings.
Acceptability Ratings for Lag5
Ratings for prime and target sentences in Lag5 are shown in Figure 2. Mean ratings for CNPC islands were higher than for Subject islands, but this difference was only marginally reliable (? = 0.08, SE = 0.04, |t| = 1.91). Unlike in Lag1, there was no significant effect of trial type (? = 0.03, SE = 0.02, |t| = 1.62) and no significant sentence-by-trial interaction (? = 0.04, SE = 0.05, |t| = 0.91): Ratings for target sentences did not significantly differ from prime sentences, either in CNPC or Subject islands. Lag5 also showed no main or interaction effects involving repetition type (|t|’s < 1.15). Thus, in contrast to the improvements that we observed for CNPC islands in Lag1, no rating improvements were observed in Lag5, where primes and targets are separated by five intervening sentences.
Shape dos. Indicate feedback to have CNPC and you will Subject countries inside Lag5. Brutal scores are displayed towards the a 5-section size, in which 1 = Entirely Unsuitable and you will 5 = Totally Acceptable. Mistake bars depict ±step one practical mistake. Having profile, we inform you only 1–3 things on scale.
Discussion
Check out 1 examined acceptability score advancements having CNPC and you may Topic isles inside the prime-target pairs. If you’re earlier in the day work with satiation enjoys compared score improvements along side course of a complete analysis, our priming-build (prime-target) design allowed me to sample whether or not situations recognized to affect priming may also connect with satiation also. If that’s the case, this could promote cause to think that priming and you may satiation display underlying systems. We looked at one or two points: (1) lexical repetition and you will (2) distance from coverage between your prime and you will target phrases. We ranged lexical repetition such that primes and you will objectives common possibly the head of the island statement or an expression unrelated to help you the latest island. I predicted you to repetition of the direct regarding isle sentences you are going to write a great priming ‘boost’ comparable to ‘lexical boost’ consequences that have been seen in priming functions. Likewise, we ranged proximity from publicity by the influencing the amount of unrelated sentences (one to versus. five) between primes and you may purpose, so you can probe whether or not prospective acceptability advancements was brief-name (e.g., out of activation rust off architectural representations) or much time-title (e.grams., down to implicit structural learning).
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