arnold2013 {TreeBUGS}

Data of a Source-Monitoring Experiment

Description

Dataset of a source-monitoring experiment by Arnold, Bayen, Kuhlmann, and Vaterrodt (2013) using a 2 (Source; within) x 3 (Expectancy; within) x 2 (Time of Schema Activation; between) mixed factorial design.

Usage

arnold2013

Format

A data frame 13 variables:

subject

Participant code

age

Age in years

group

Between-subject factor "Time of Schema Activation": Retrieval vs. encoding condition

pc

perceived contingency

EE

Frequency of "Source E" responses to items from source "E"

EU

Frequency of "Source U" responses to items from source "E"

EN

Frequency of "New" responses to items from source "E"

UE

Frequency of "Source E" responses to items from source "E"

UU

Frequency of "Source U" responses to items from source "E"

UN

Frequency of "New" responses to items from source "E"

NE

Frequency of "Source E" responses to new items

NU

Frequency of "Source U" responses to new items

NN

Frequency of "New" responses to new items

Details

Eighty-four participants had to learn statements that were either presented by a doctor or a lawyer (Source) and were either typical for doctors, typical for lawyers, or neutral (Expectancy). These two types of statements were completely crossed in a balanced way, resulting in a true contingency of zero between Source and Expectancy. Whereas the profession schemata were activated at the time of encoding for half of the participants (encoding condition), the other half were told about the profession of the sources just before the test (retrieval condition). After the test, participants were asked to judge the contingency between item type and source (perceived contingency pc).

References

Arnold, N. R., Bayen, U. J., Kuhlmann, B. G., & Vaterrodt, B. (2013). Hierarchical modeling of contingency-based source monitoring: A test of the probability-matching account. Psychonomic Bulletin & Review, 20, 326-333.

Examples

head(arnold2013)

## Not run: 
# fit hierarchical MPT model for encoding condition:
EQNfile <- system.file("MPTmodels/2htsm.eqn", package = "TreeBUGS")
d.encoding <- subset(arnold2013, group == "encoding", select = -(1:4))
fit <- betaMPTcpp(EQNfile, d.encoding,
  n.thin = 5,
  restrictions = list("D1=D2=D3", "d1=d2", "a=g")
)
# convergence
plot(fit, parameter = "mean", type = "default")
summary(fit)

## End(Not run)