citysize: City-Size Paired-Comparison Task and Response Strategy

Description

Thirty-seven participants performed a city-size paired-comparison task. On each trial, participants indicated which of two cities was more populous. After the paired comparisons, participants were asked for each city if they recognized its name, knew in which country it is, and how many inhabitants it has. The study was designed to be similar to Experiment 6 in Hilbig, Erdfelder, and Pohl (2010).

The 17 cities were (in order of population; Wikipedia, 2016): Shanghai, Tianjin, Tokio, Seoul, London, Bangkok, Chongqing, Wuhan, Santiago, Rangun, Ankara, Harbin, Kano, Busan, Durban, Ibadan, Montreal.

Usage

data(citysize)

Arguments

Format

A data frame consisting of six components:

gender: factor. Participant gender.
age: Participant age.
country: Number of cities whose country was correctly identified.
rt: Median response time (in seconds) across paired comparisons.
instr: factor. When none, no additional instructions were given; when recog, participants were instructed to choose the city they recognized whenever possible.
y: a matrix of aggregate response frequencies per participant. The column names indicate each of eight response categories: correct/false responses when both cities were recognized (KC, KF), when both were unrecognized (GC, GF), when only one was recognized and the recognized city was chosen (RC, RF), and when only one was recognized and the unrecognized city was chosen (UF, UC).

References

Hilbig, B.E., Erdfelder, E., & Pohl, R.F. (2010). One-reason decision-making unveiled: A measurement model of the recognition heuristic. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 123--134. 10.1037/a0017518

Wikipedia. (2016). List of cities proper by population. Retrieved Jun 16 from https://en.wikipedia.org/wiki/List_of_cities_proper_by_population.

Examples

Run this code

# NOT RUN {
data(citysize)

## Fit r-model separately for each instruction type
mpt(mptspec("rmodel"), unname(citysize[citysize$instr == "none",  "y"]))
mpt(mptspec("rmodel"), unname(citysize[citysize$instr == "recog", "y"]))

## Test instruction effect on r parameter
city.agg <- aggregate(y ~ instr, citysize, sum)
y <- as.vector(t(city.agg[, -1]))

m1 <- mpt(mptspec("rmodel", .replicates=2), y)
m2 <- mpt(update(m1$spec, .restr=list(r2=r1)), y)
anova(m2, m1)  # more use of RH with recognition instruction

## Plot parameter estimates
dotchart(coef(m1)[c(4, 1:3)], xlim=0:1, labels=c("a", "b", "g", "r"),
         xlab="Parameter estimate (r-model)",
         main="Recognition heuristic use by instruction type")
points(coef(m1)[c(8, 5:7)], 1:4, pch=16)
legend(0, 1, c("none", "recognition"), pch=c(1, 16),
       title="Instruction", bty="n")
# }