This document preregisters an adult Artificial Language Learning (ALL) study that follows on the child ALL experiment preregistered at https://rpubs.com/AnnaSamara/539534
We seek to conduct the same study (detailed at https://rpubs.com/AnnaSamara/539534) with adult participants recruited via Prolific.
We will run the same study preregistered at https://rpubs.com/AnnaSamara/539534 with two minor changes in the Procedure.
Participants will complete the training phase of the experiment in a single session.
When verbal responses are required (e.g., copying task, production task), participants will type (rather than speaking) their responses (see also https://rpubs.com/AnnaSamara/856144 and https://rpubs.com/AnnaSamara/458000 for a similar approach).
We will take the same approach as detailed at https://rpubs.com/AnnaSamara/539534
As detailed at https://rpubs.com/AnnaSamara/539534
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
Production performance for the alternating noun
Production performance for the novel noun
Grammaticality judgment performance for the alternating and novel nouns
Summary of data for each type of noun: mean and SE for main effect of Semantic Appropriateness from bayesian lmes
Value to inform H1 for each type of noun: 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 noun, respectively
We have seen a significant effect of pre-emption in the verb study with adults pre-registered at https://rpubs.com/AnnaSamara/458000. 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, pre-registered at https://rpubs.com/AnnaSamara/458000, as follows:
Analysis 1
Summary of data for entrenchment 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: We previously saw a significant effect of entrenchment in the pre-registered analyses on ratings for ‘attested vs. unattested restricted verbs’. We will use this effect 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 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: 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 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