selectiontask

0th

Percentile

Wason Selection Task (WST) and Helpful Hints

In the Wason selection task, a participant is presented with four cards, each one having a letter side and a number side, e.g., A B 3 4. The task is to select the card(s) that have to be turned around in order to test the rule "If there is an A on the letter side then there is a 3 on the number side." Klauer, Stahl, and Erdfelder (2007) report a series of experiments to test their WST model using the aggregate frequencies of the 16 possible response patterns.

Keywords
datasets
Usage
data(selectiontask)
Note

In the original analyses, a constant of one was added to all frequencies.

Format

A data frame consisting of four variables:

group

factor. The control group (CG) received standard instructions, the experimental group (EG) got additional helpful hints.

pattern

character. Response pattern indicating which card(s) were selected (1) or not selected (0).

exp1, exp2

the aggregate response frequencies for Experiment 1 and 2, respectively.

See Also

mpt.

Aliases
  • selectiontask
Examples
# NOT RUN {
data(selectiontask)

## Inference-guessing model with relaxed assumptions
s <- mptspec("WST", .replicates = 2)
m1 <- mpt(s, data = selectiontask$exp1, method = "EM")

## Inference-guessing model
m2 <- mpt(update(s, .restr = list(sf1=s1, sb1=s1, sfb1=s1,
                                  sf2=s2, sb2=s2, sfb2=s2)),
          data = m1$y, method = "EM")

## Effect of hint on i parameter (Exp. 1)
m3 <- mpt(update(m2$spec, .restr = list(i2=i1)), data = m1$y,
          method = "EM")

## Independence model
m4 <- mpt(update(m2$spec,
                 .restr = list(a1=0, c1=0, x1=0, d1=0, s1=0, i1=0,
                               a2=0, c2=0, x2=0, d2=0, s2=0, i2=0)),
          data = m1$y, method = "EM")

anova(m4, m3, m2, m1)
plogis(confint(m2))
AIC(m2)
BIC(m2)  # BIC w/number of non-redundant response categories
AIC(m2, k = log(sum(m2$y)))  # BIC w/total number of data points

## Effect of hint on c parameter (Exp. 2)
m5 <- mpt(m2$spec, data = selectiontask$exp2, method = "EM")
m6 <- mpt(update(m5$spec, .restr = list(c2=c1)), data = m5$y,
          method = "EM")
anova(m6, m5)

idx <- c("P", "p", "Q", "q", "a", "c", "x", "d", "s", "i")
par(mfrow = 1:2)
dotchart(coef(m2)[paste0(idx, 1)], xlim=c(0, 1), labels=idx,
         main="Exp. 1", xlab="")
points(coef(m2)[paste0(idx, 2)], 1:10, pch=16)
legend(0, 11, c("standard", "hints"), pch=c(1, 16),
       title="Instruction", bty="n")
dotchart(coef(m5)[paste0(idx, 1)], xlim=c(0, 1), labels=idx,
         main="Exp. 2", xlab="")
points(coef(m5)[paste0(idx, 2)], 1:10, pch=16)
mtext("Parameter estimate (inference-guessing model)", side=1,
      outer=TRUE, line=-2)
mtext("Klauer et al. (2007)", side=3, outer=TRUE, line=-3)
# }
Documentation reproduced from package mpt, version 0.6-2, License: GPL (>= 2)

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