This document preregisters two adult artificial language learning studies that follow on the adult study preregistered at https://rpubs.com/AnnaSamara/458000
Experiment1: This will be an exact replication of our previous study.
Experiment2: This will replicate our study above with exactly double the amount of training for each participant, administered over two sessions days. We intend to see whether doubling the training increases the size of the pre-emption and entrenchment effects observed in Experiment 1 (and the earlier experiment pregistered at https://rpubs.com/AnnaSamara/458000)
All aspects of the design and procedure will be identical to the original preregistered experiment, except that in Experiment2, each participant will perform 384 training trials over two days (twice as many as in Experiment 1; N = 192), followed by the tests detailed at https://rpubs.com/AnnaSamara/458000 (administered on the sessions administered on day 2).
As detailed at https://rpubs.com/AnnaSamara/458000
As detailed at https://rpubs.com/AnnaSamara/458000
As detailed at https://rpubs.com/AnnaSamara/458000
As detailed at https://rpubs.com/AnnaSamara/458000
Roughly expected effects will be drawn from the verb study with adults pre-registered at https://rpubs.com/AnnaSamara/458000 that we are replicating here
Production performance for the alternating verb
Production performance for the novel verb
Grammaticality judgment performance for the alternating and novel verbs
Summary of data for each type of verb: mean and SE for main effect of Semantic Appropriateness from bayesian lmes
Value to inform H1 for each type of verb: mean of theory = 0; roughly predicted difference between ratings for semantically appropriate vs. inappropriate trials from adult data: 2.331 points on the scale and 1.924 points on the scale for the alternating and novel verb, respectively
We have seen a significant effect of pre-emption in the verb study with adults pre-registered at https://rpubs.com/AnnaSamara/458000 that we are replicating here. Roughly expected effects will be drawn from this work
Analysis 1
Summary of data for preemption condition: mean and SE for main effect of the “attested_unattested.ct” variable (capturing if a sentence has been attested during training) from bayesian lmes in this condition
Value to inform H1 for preemption condition: mean of theory = 0; roughly predicted difference between attested and unattested sentences from our previous study with adults: 2.55
Analysis 2 (key analysis)
Summary of data for preemption condition: mean and SE for main effect of the variable capturing if a sentence was unwitnessed restricted or unwitnessed novel from bayesian lmes in this condition
Value to inform H1 for preemption condition: mean of theory = 0; roughly predicted difference between unwitnessed restricted vs. unwitnessed novel from our previous study with adults: 0.65
We will inform our bayes factor analyses on the basis of the verb study (entrenchment condition) with adults that we are replicating, pre-registered at https://rpubs.com/AnnaSamara/458000, as follows:
Analysis 1
Summary of data: mean and SE for main effect of the “attested_unattested.ct” variable (capturing if a sentence has been attested during training) from bayesian lmes in this condition
Value to inform H1: We previously saw a significant effect of entrenchment in the pre-registered analyses that compared ratings for ‘attested vs. unattested restricted verbs’. We will use this as a roughly predicted effect. Mean of theory = 0; roughly predicted difference between attested and unattested sentences: 0.38
Analysis 2 (key analysis)
Summary of data: mean and SE for main effect of the variable capturing if a sentence was unwitnessed restricted or unwitnessed novel from bayesian lmes in this condition
Value to inform H1: We do not have a roughly expected effect as there was no effect of entrenchment in these analyses in our previous work. We will therefore use the difference between attested and unattested in our previous adult study as a maximum of what we expect here. mean of theory = 0; roughly expected maximum rating difference between unwitnessed restricted and unwitnessed novel from our previous study with adults: 0.38. As outlined in “Note on data analyses” at http://rpubs.com/AnnaSamara/429816, the SD will be set to half of these max value, i.e., SD = 0.38/2
We have seen a significant difference in pre-emption > entrenchment effects in the verb study with adults pre-registered at https://rpubs.com/AnnaSamara/458000 (across analyses). We will use these effects as roughly predicted effects of the advantage for pre-emption vs. entrenchment in this study
Analysis 1
Summary of data for condition comparison: mean and SE for the interaction between the “attested_unattested.ct” variable and condition from bayesian lmes
Value to inform H1: mean of theory = 0; roughly predicted difference between attested and unattested sentences in entrenchment vs. preemption from our previous study with adults: 2.11
Analysis 2 (key analysis)
Summary of data for preemption condition: mean and SE for interaction between condition and the variable capturing if a sentence is unwitnessed restricted or unwitnessed novel from bayesian lmes
Value to inform H1 for preemption condition: mean of theory = 0; roughly predicted rating difference between conditions from our previous study with adults: 1.00
As above for all analyses