mpt (version 1.0-0)

prospecMemory: Prospective Memory and Task Importance

Description

Smith and Bayen (2004) tested the performance of 64 participants in an event-based prospective memory task that was embedded in a color-matching task. On each trial, participants were presented with four colored rectangles followed by a colored word. Their task was to press a key to indicate whether the color of the word matched one of the rectangles. Interspersed among these nontarget words were six target words for which subjects had to remember to press a special key (prospective memory response) regardless of the color. Participants received two different instruction types either stressing the importance of the color-matching (CMI) or of the prospective-memory task (PMI).

In a replication study, the performance of 72 German-speaking participants was tested; this study was designed to be similar to Experiment 1 in Smith and Bayen (2004).

Usage

data(prospecMemory)

Format

PMSmithBayen A data frame consisting of five variables:

instruction

instruction type, either color-matching importance (cmi) or prospective memory importance (pmi).

item

a factor specifying one of four item types: either a target word that did or did not match the color of the rectangles, or a nontarget word that did or did not match.

resp

a factor giving the response categories: match, nonmatch, or the prospective memory response (prospec).

freq

the aggregate response frequencies per condition.

treeid

an identifier for the single trees of the joint multinomial model.

PMreplication A data frame containing 72 observations of five variables:

gender

factor. Participant gender.

age

participant age.

instr

factor. Instruction type.

rtdiff

average response time difference (in milliseconds) between color-matching and prospective-memory task.

y

a matrix of aggregate response frequencies per participant. The column names indicate each of twelve response categories: match, nonmatch, prospective memory response for targets in matching (tmm, tmn, tmp) or in nonmatching condition (tnm, tnn, tnp), and again for nontargets (nmm, nmn, nmp vs. nnm, nnn, nnp).

See Also

mpt.

Examples

## Prospective memory model: identifiability
qr(mptspec("prospec",
   .restr = list(M1=M, M2=M))$par2deriv(runif(6))$deriv)$rank
qr(mptspec("prospec",
   .restr = list(M1=M, M2=M, g=.1, c=.5))$par2deriv(runif(4))$deriv)$rank

## Prospective memory model: goodness of fit
data(prospecMemory)
cmi <- PMSmithBayen[PMSmithBayen$instruction == "cmi", ]
m2 <- mpt(mptspec("prospec", .restr = list(M1=M, M2=M, g=.1, c=.5)), cmi)
m1 <- mpt(update(m2$spec, .restr = list(C2=C1)), cmi)
anova(m1, m2)

pmi <- PMSmithBayen[PMSmithBayen$instruction == "pmi", ]
anova(mpt(m1$spec, pmi), mpt(m2$spec, pmi))

## Testing P_cmi = P_pmi and M_cmi = M_pmi

## Smith and Bayen
m2 <- mpt(mptspec("prospec", .replicates = 2,
                  .restr = list(M11=M1, M21=M1, g1=.1, c1=.5,
                                M12=M2, M22=M2, g2=.1, c2=.5)),
          data = PMSmithBayen)
m1 <- mpt(update(m2$spec, .restr = list(P2=P1)), PMSmithBayen)
m0 <- mpt(update(m2$spec, .restr = list(M2=M1)), PMSmithBayen)
anova(m1, m2)
anova(m0, m2)

## Replication
pm.agg <- aggregate(y ~ instr, PMreplication, sum)
y <- as.vector(t(pm.agg[2:1, -1]))

m3 <- mpt(m2$spec, y)
m1 <- mpt(update(m3$spec, .restr = list(P2=P1)), y)
m0 <- mpt(update(m3$spec, .restr = list(M2=M1)), y)
anova(m1, m3)
anova(m0, m3)

par(mfrow = 1:2)
dotchart(coef(m2)[c("C12", "C22", "P2", "M2")], xlim=0:1, xlab="",
  labels=c("C1", "C2", "P", "M"), main="Smith and Bayen (2004, Exp. 1)")
points(coef(m2)[c("C11", "C21", "P1", "M1")], 1:4, pch=16)
legend("bottomleft", c("CMI", "PMI"), pch=c(1, 16), title="Instruction",
       title.adj=1, bty="n")

dotchart(coef(m3)[c("C12", "C22", "P2", "M2")], xlim=0:1, xlab="",
  labels=c("C1", "C2", "P", "M"), main="Replication study")
points(coef(m3)[c("C11", "C21", "P1", "M1")], 1:4, pch=16)
mtext("Parameter estimate (prospective memory model)", side=1,
      line=-2, outer=TRUE)