selectiontask: Wason Selection Task (WST) and Helpful Hints
In mpt: Multinomial Processing Tree Models

selectiontask

R Documentation

Wason Selection Task (WST) and Helpful Hints

Description

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.

Bauder (2020) conducted a replication study that was designed to be similar to Experiment 1 in Klauer et al. (2007).

Usage

data(selectiontask)

Format

WSTKlauer2007 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.

WSTreplication A data frame containing 1118 observations of eight variables:

status: factor. Was the participant excluded?
group: factor. The experimental group.
gender: factor. Participant gender.
age: participant age.
education: years of education.
logic: factor. Familiarity with formal logic.
time: seconds spent on the web page.
y: a participant by response pattern indicator matrix.

Note

In the original analyses (Klauer et al., 2007), a constant of one was added to all frequencies.

Source

Klauer, K.C., Stahl, C., & Erdfelder, E. (2007). The abstract selection task: New data and an almost comprehensive model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33(4), 680–703. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/0278-7393.33.4.680")}

Bauder, D. (2020). Die Modellierung der abstrakten Auswahlaufgabe von Wason - eine Replikationsstudie. Bachelor thesis. University of Tuebingen, Germany. https://osf.io/3z7ux/

Examples

data(selectiontask)

## Inference-guessing model with relaxed assumptions
s <- mptspec("WST", .replicates = 2)
m1 <- mpt(s, data = WSTKlauer2007$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$spec, data = WSTKlauer2007$exp2, method = "EM")
m6 <- mpt(update(m5$spec, .restr = list(c2=c1)), data = m5$y,
          method = "EM")
anova(m6, m5)

## Replication of Exp. 1
wst.agg <- aggregate(y ~ group, WSTreplication,
                     subset = status == "select", sum)
y <- as.vector(t(wst.agg[, -1]))

set.seed(1503)
m7 <- mpt(m2$spec, data = y, start = runif(20), method = "EM")

idx <- c("P", "p", "Q", "q", "a", "c", "x", "d", "s", "i")
par(mfrow = 1:2)
dotchart(coef(m2)[paste0(idx, 1)], xlim = 0:1, labels = idx,
         main = "Klauer et al. (2007, 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(m7)[paste0(idx, 1)], xlim = 0:1, labels = idx,
         main = "Replication study", xlab = "")
points(coef(m7)[paste0(idx, 2)], 1:10, pch = 16)
mtext("Parameter estimate (inference-guessing model)", side = 1,
      outer = TRUE, line = -2)

mpt documentation built on Oct. 11, 2024, 5:07 p.m.