Package 'WordPools' reference manual

Package 'WordPools'

Title:	Word Pools Used in Studies of Learning and Memory
Description:	Collects several classical word pools used most often to provide lists of words in psychological studies of learning and memory. It provides a simple function, 'pickList' for selecting random samples of words within given ranges.
Authors:	Michael Friendly [aut, cre] , Matthew Dubins [ctb]
Maintainer:	Michael Friendly <[email protected]>
License:	GPL-2
Version:	1.2.1
Built:	2024-11-12 06:27:06 UTC
Source:	https://github.com/friendly/WordPools

Title:

Word Pools Used in Studies of Learning and Memory

Description:

Collects several classical word pools used most often to provide lists of words in psychological studies of learning and memory. It provides a simple function, 'pickList' for selecting random samples of words within given ranges.

Authors:

Michael Friendly [aut, cre]

, Matthew Dubins [ctb]

Maintainer:

Michael Friendly <[email protected]>

License:

GPL-2

Version:

1.2.1

Built:

2024-11-12 06:27:06 UTC

Source:

https://github.com/friendly/WordPools

Help Index

Word Pools Used in Studies of Learning and Memory

Description

This package collects several classical word pools used most often to provide lists of words in psychological studies of learning and memory.

Each word pool consists of a population of words, together with various descriptive measures (number of letters, number of syllables, word frequency, etc.) and normative measures (imagery, concreteness, etc.) that can be used in experimental designs to vary and control such factors.

Details

At present, the package contains three main word pools:

Paivio - the Paivio etal. (1968) word list of 925 nouns

TWP - the Friendly etal. (1982) Toronto Word Pool of 1080 words in various grammatical classes

Battig - the Battig & Montague (1969) Categorized Word Norms, containing 5231 words listed in 56 taxonomic categories. Various measures on these categories are given in CatProp.

In addition, the function pickList provides the ability to select items from such lists with restrictions on the ranges of the measured variables.

Author(s)

Michael Friendly

Maintainer: Michael Friendly <[email protected]>

References

Paivio, A., Yuille, J.C. & Madigan S. Concreteness, imagery and meaningfulness for 925 nouns. Journal of Experimental Psychology, Monograph Supplement, 1968, 76, No.1, pt.2.

Battig, W.F. & Montague, W.E. (1969). Category norms for verbal items in 56 categories: A replication and extension of the Connecticut norms. Journal of Experimental Psychology, 80 (1969), pp. 1-46

Friendly, M., Franklin, P., Hoffman, D. & Rubin, D. The Toronto Word Pool, Behavior Research Methods and Instrumentation, 1982, 14(4), 375-399. http://datavis.ca/papers/twp.pdf.

Friendly, M. (2006) Word list generator. http://datavis.ca/online/paivio/

Rubin, D. C. & Friendly, M. (1986). Predicting which words get recalled: Measures of free recall, availability, goodness, emotionality, and pronunciability for 925 nouns. Memory and Cognition, 14, 79-94.

See also http://memory.psych.upenn.edu/Word_Pools for other related word pools

Battig - Montague Categorized Word Norms

Description

This dataset comprises a ranked list of 5231 words listed in 56 taxonomic categories by people who were asked to list as many exemplars of a given category ("a precious stone", "a unit of time", "a fruit", "a color", etc.). Participants had 30s to generate as many responses to each category as possible, after which time the next category name was presented.

Included in this dataset are all words from the Battig and Montague (1969) norms listed with freq > 1.

Usage

data(Battig)data(Battig)

Format

A data frame with 5231 observations on the following 9 variables.

word: a character vector
catnum: category number, a factor
catname: category name, a factor
syl: number of syllables
letters: number of letters
freq: Frequency of response
frequency: Kucera-Francis word frequency
rank: rank of freq within the category
rfreq: rated frequency

Details

In our original dataset, words were truncated at 18 characters, so some are incomplete.

Source

Battig, W.F. & Montague, W.E. (1968). Category norms for verbal items in 56 categories: A replication and extension of the Connecticut norms using University of Maryland and Illinois students (Tech. Rep.) University of Colorado, Boulder, CO (1968)

Battig, W.F. & Montague, W.E. (1969). Category norms for verbal items in 56 categories: A replication and extension of the Connecticut norms. Journal of Experimental Psychology, 80 (1969), pp. 1-46

References

Joelson, J. M. & Hermann, D. J., Properties of categories in semantic | memory, Amer Journal of Psychology, 1978, 91, 101-114.

Examples

data(Battig)
## maybe str(Battig) ; plot(Battig) ...

# select items from several categories
cats <- c("fish", "bird", "flower", "tree")
for (c in cats) {
	cat("\nCategory:", c, "\n")	
	print(pickList(subset(Battig, catname==c), nitems=5))
}

# or, using sapply():
sapply(cats, function(c) pickList(subset(Battig, catname==c), nitems=5), simplify=FALSE)
data(Battig)
## maybe str(Battig) ; plot(Battig) ...

# select items from several categories
cats <- c("fish", "bird", "flower", "tree")
for (c in cats) {
	cat("\nCategory:", c, "\n")	
	print(pickList(subset(Battig, catname==c), nitems=5))
}

# or, using sapply():
sapply(cats, function(c) pickList(subset(Battig, catname==c), nitems=5), simplify=FALSE)

Joelson-Hermann Category Properties

Description

Properties of the 56 taxonomic categories from the Battig-Montague category norms published by Joelson and Hermann (1978).

Usage

data(CatProp)data(CatProp)

Format

A data frame with 56 observations on the following 24 variables.

catnum: Category number, a numeric variable
catname: Category name, a character variable
rnatrl: Rated naturalness 1..7, a numeric variable
rfamil: Rated familiarity 1..7, a numeric variable
rmeang: Rated meaningfulness 1..7 (Hunt & Hodge, 1971), a numeric variable
rfreq: Rated frequency 1..7 B&M, a numeric variable
genfreq: Generated category label frequency, a numeric variable
rageoaq: Rated age of acquisition 1..10, a numeric variable
rsize: Estimated category size, a numeric variable
ts_30: Mean # types produced in 30 seconds, a numeric variable
rclasm: Recall asymptote, a numeric variable
rclrate: Recall rate parameter, a numeric variable
tas: Types across subjects, a numeric variable
cortas: Corrected types across subjects, a numeric variable
ntf: # of types produced first, a numeric variable
nmngox: # of dictionary meanings (Oxford), a numeric variable
nmngam: # of dictionary meanings (Am. Heritage), a numeric variable
catfreqp: category label K-F frequency, a numeric variable
rabcon: Rated abstract-concreteness 1..7, a numeric variable
rvagprc: Rated vague-precise 1..7, a numeric variable
exfreqp: Avg exemplar log K-F frequency, a numeric variable
intsam: Intersample correlation, a numeric variable
maxfreq: Maximum response frequency, a numeric variable
pagmt: Percent agreement on category membership, a numeric variable

Details

Includes data for all 56 of the Battig-Montague categories from a preprint of the Joelson-Hermann paper Values for catfreqp were added for categories 3, 4, 8, 15, 24, 27, 32, 46, 47 & 56 from the Kucera-Francis norms, ignoring "part of", "unit of", and taking max of labels connected by "or".

Source

Joelson, J. M. & Hermann, D. J. , Properties of categories in semantic memory, American Journal of Psychology, 1978, 91, 101-114.

Examples

data(CatProp)
summary(CatProp)
plot(CatProp[,3:10])

# try a biplot
CP <- CatProp
rownames(CP) <- CP$catname
biplot(prcomp(na.omit(CP[,3:12]), scale=TRUE))

# select some categories where the rated age of acquisition is between 2-4
cats <- pickList(CatProp, list(rageoaq=c(2,4)))
cats[,2:9]

# pick some fruit
pickList(subset(Battig, catname=="fruit"))
data(CatProp)
summary(CatProp)
plot(CatProp[,3:10])

# try a biplot
CP <- CatProp
rownames(CP) <- CP$catname
biplot(prcomp(na.omit(CP[,3:12]), scale=TRUE))

# select some categories where the rated age of acquisition is between 2-4
cats <- pickList(CatProp, list(rageoaq=c(2,4)))
cats[,2:9]

# pick some fruit
pickList(subset(Battig, catname=="fruit"))

Paivio, Yuille & Madigan Word Pool

Description

The Paivio, Yuille & Madigan (1968) word pool contains 925 nouns, together with average ratings of these words on imagery, concreteness and meaningfulness, along with other variables.

Usage

data(Paivio)data(Paivio)

Format

A data frame with 925 observations on the following 9 variables.

itmno: item number
word: the word
imagery: imagery rating
concreteness: concreteness rating
meaningfulness: meaningfulness rating
frequency: word frequency, from the Kucera-Francis norms
syl: number of syllables
letters: number of letters
freerecall: Free recall proportion, added from Christian et al (1978)

Details

The freerecall variable has 27 NAs.

Source

Paivio, A., Yuille, J.C. & Madigan S. Concreteness, imagery and meaningfulness for 925 nouns. Journal of Experimental Psychology, Monograph Supplement, 1968, 76, No.1, pt.2.

Christian, J., Bickley, W., Tarka, M., & Clayton, K. (1978). Measures of free recall of 900 English nouns: Correlations with imagery, concreteness, meaningfulness, and frequency. Memory & Cognition, 6, 379-390.

References

Kucera and Francis, W.N. (1967). Computational Analysis of Present-Day American English. Providence: Brown University Press.

Examples

data(Paivio)
summary(Paivio)
plot(Paivio[,c(3:5,9)])

# density plots

plotDensity(Paivio, "imagery")
plotDensity(Paivio, "concreteness")
plotDensity(Paivio, "meaningfulness")
plotDensity(Paivio, "frequency")
plotDensity(Paivio, "syl")
plotDensity(Paivio, "letters")
plotDensity(Paivio, "freerecall")



# find ranges & 5 num summaries
ranges <- as.data.frame(apply(Paivio[,-(1:2)], 2, function(x) range(na.omit(x))))
rownames(ranges) <- c("min", "max")
ranges

P5num <- as.data.frame(apply(Paivio[,3:5], 2, fivenum))
rownames(P5num) <- c("min", "Q1", "med", "Q3", "max")
P5num




data(Paivio)
summary(Paivio)
plot(Paivio[,c(3:5,9)])

# density plots

plotDensity(Paivio, "imagery")
plotDensity(Paivio, "concreteness")
plotDensity(Paivio, "meaningfulness")
plotDensity(Paivio, "frequency")
plotDensity(Paivio, "syl")
plotDensity(Paivio, "letters")
plotDensity(Paivio, "freerecall")



# find ranges & 5 num summaries
ranges <- as.data.frame(apply(Paivio[,-(1:2)], 2, function(x) range(na.omit(x))))
rownames(ranges) <- c("min", "max")
ranges

P5num <- as.data.frame(apply(Paivio[,3:5], 2, fivenum))
rownames(P5num) <- c("min", "Q1", "med", "Q3", "max")
P5num

Select Items from a Word Pool in Given Ranges

Description

This is a convenience function to provide the capability to select items from a given word pool, with restrictions on the range of any numeric variables.

Usage

pickList(data, ranges, nitems = 10, nlists = 1, replace = FALSE)
pickList(data, ranges, nitems = 10, nlists = 1, replace = FALSE)

Arguments

`data`	A data.frame, typically a word list like `Paivio` or `TWP`
`ranges`	A data.frame of two rows, and with column names corresponding to a subset of the column names in `data`. The two rows give the minimum and maximum values, respectively, of variables in `data`. Alternatively, you can supply a named list containing the minimum and maximum values for one or more variables in `data`.
`nitems`	Number of items per list
`nlists`	Number of lists
`replace`	A logical value, indicating whether the sampling of items (rows) of `data` is to allow sampling with replacement.

Details

sample will generate an error if fewer than nitems * nlists items are within the specified ranges and replace=FALSE.

Value

A data frame of the same shape as data containing the selected items prefixed by the list number.

Author(s)

Michael Friendly

References

A related word list generator: Friendly, M. Word list generator. http://datavis.ca/online/paivio/

Examples

data(Paivio)
# 2 lists, no selection on any variables
pickList(Paivio, nlists=2)

# Define ranges for low and high on imagery, concreteness, meaningfulness
# These go from low - median, and median-high on each variable
vars <- 3:5
(low <- as.data.frame(apply(Paivio[,vars], 2, fivenum))[c(1,3),])
(high <- as.data.frame(apply(Paivio[,vars], 2, fivenum))[c(3,5),])

# select two lists of 10 low/high imagery items
lowI <- pickList(Paivio, low[,"imagery", drop=FALSE], nitems=10, nl=2)
highI <- pickList(Paivio, high[,"imagery", drop=FALSE], nitems=10, nl=2)

# compare means
colMeans(lowI[,c(4:8)])
colMeans(highI[,c(4:8)])

# using a list of ranges
L <- list(imagery=c(1,5), concreteness=c(1,4))
pickList(Paivio, L)
data(Paivio)
# 2 lists, no selection on any variables
pickList(Paivio, nlists=2)

# Define ranges for low and high on imagery, concreteness, meaningfulness
# These go from low - median, and median-high on each variable
vars <- 3:5
(low <- as.data.frame(apply(Paivio[,vars], 2, fivenum))[c(1,3),])
(high <- as.data.frame(apply(Paivio[,vars], 2, fivenum))[c(3,5),])

# select two lists of 10 low/high imagery items
lowI <- pickList(Paivio, low[,"imagery", drop=FALSE], nitems=10, nl=2)
highI <- pickList(Paivio, high[,"imagery", drop=FALSE], nitems=10, nl=2)

# compare means
colMeans(lowI[,c(4:8)])
colMeans(highI[,c(4:8)])

# using a list of ranges
L <- list(imagery=c(1,5), concreteness=c(1,4))
pickList(Paivio, L)

Enhanced density plot for WordPools

Description

Plots the distribution of a variable with a density estimate and a rug plot

Usage

plotDensity(
  data,
  var,
  adjust = 1,
  lwd = 2,
  fill = rgb(1, 0, 0, 0.2),
  xlab = NULL,
  main = NULL,
  anno = FALSE,
  ...
)
plotDensity(
  data,
  var,
  adjust = 1,
  lwd = 2,
  fill = rgb(1, 0, 0, 0.2),
  xlab = NULL,
  main = NULL,
  anno = FALSE,
  ...
)

Arguments

`data`	A data.frame
`var`	Name of the variable to be plotted
`adjust`	Adjustment factor for the bandwidth of the density estimate
`lwd`	line width
`fill`	Color to fill the area under the density estimate
`xlab`	Label for the variable
`main`	Title for plot
`anno`	If `TRUE`
`...`	Other arguments passed to `plot.density`

Value

Returns the result of density

Examples

plotDensity(Paivio, "imagery", anno=TRUE)
plotDensity(Paivio, "imagery", anno=TRUE, adjust=1.5)
plotDensity(Paivio, "syl")

plotDensity(TWP, "imagery", anno=TRUE)
plotDensity(Paivio, "imagery", anno=TRUE)
plotDensity(Paivio, "imagery", anno=TRUE, adjust=1.5)
plotDensity(Paivio, "syl")

plotDensity(TWP, "imagery", anno=TRUE)

The Toronto Word Pool

Description

The Toronto Word Pool consists of 1080 words in various grammatical classes together with a variety of normative variables.

The TWP contains high frequency nouns, adjectives, and verbs taken originally from the Thorndike-Lorge (1944) norms. This word pool has been used in hundreds of studies at Toronto and elsewhere.

Usage

data(TWP)data(TWP)

Format

A data frame with 1093 observations on the following 12 variables.

itmno: item number
word: the word
imagery: imagery rating
concreteness: concreteness rating
letters: number of letters
frequency: word frequency, from the Kucera-Francis norms
foa: a measure of first order approximation to English. In a first-order approximation, the probability of generating any string of letters is based on the frequencies of occurrence of individual letters in the language.
soa: a measure of second order approximation to English, based on bigram frequencies.
onr: Orthographic neighbor ratio, taken from Landauer and Streeter (1973). It is the ratio of the frequency of the word in Kucera and Francis (1967) count divided by the sum of the frequencies of all its orthographic neighbors.
dictcode: dictionary codes, a factor indicating the collection of grammatical classes, 1-5, for a given word form

. In the code, "1" in any position means the item had a dictionary definition as a noun; similarly, a "2" means a verb, "3" means an adjective, "4" means an adverb, and "5" was used to cover all other grammatical categories (but in practice was chiefly a preposition). Thus an entry "2130" indicates an item defined as a verb, noun, and an adjective in that order of historical precedence.

noun: percent noun usage. Words considered unambiguous based on dictcode are listed as 0 or 100; other items were rated in a judgment task.
canadian: a factor indicating an alternative Canadian spelling of a given word

Details

The last 13 words in the list are alternative Canadian spellings of words listed earlier, and have duplicate itmno values.

Source

Friendly, M., Franklin, P., Hoffman, D. & Rubin, D. The Toronto Word Pool, Behavior Research Methods and Instrumentation, 1982, 14(4), 375-399. http://datavis.ca/papers/twp.pdf.

References

Kucera and Francis, W.N. (1967). Computational Analysis of Present-Day American English. Providence: Brown University Press.

Landauer, T. K., & Streeter, L. A. Structural differences between common and rare words: Failure of equivalent assumptions for theories of word recognition. Journal of Verbal Learning and Verbal Behavior, 1973, 11, 119-131.

Examples

data(TWP)
str(TWP)
summary(TWP)
# quick view of distributions
boxplot(scale(TWP[, 3:9]))

plotDensity(TWP, "imagery")
plotDensity(TWP, "concreteness")
plotDensity(TWP, "frequency")

# select low imagery, concreteness and frequency words
R <- list(imagery=c(1,5), concreteness=c(1,4), frequency=c(0,30))
pickList(TWP, R)

# dplyr now makes this much more flexible
if (require(dplyr)) {
  # select items within given ranges
  selected <- TWP |>	
  	filter( canadian == 0) |>              # remove Canadian spellings
  	filter( imagery <= 5, concreteness <= 4, frequency <= 30) |>
  	select(word, imagery:frequency )
  	
  str(selected)
  
  # get random samples of selected items
  nitems <- 5
  nlists <- 2
  lists <- selected |>
  	sample_n( nitems*nlists, replace=FALSE) |>
  	mutate(list = rep(1:nlists, each=nitems))
  
  str(lists)
  lists
}

data(TWP)
str(TWP)
summary(TWP)
# quick view of distributions
boxplot(scale(TWP[, 3:9]))

plotDensity(TWP, "imagery")
plotDensity(TWP, "concreteness")
plotDensity(TWP, "frequency")

# select low imagery, concreteness and frequency words
R <- list(imagery=c(1,5), concreteness=c(1,4), frequency=c(0,30))
pickList(TWP, R)

# dplyr now makes this much more flexible
if (require(dplyr)) {
  # select items within given ranges
  selected <- TWP |>	
  	filter( canadian == 0) |>              # remove Canadian spellings
  	filter( imagery <= 5, concreteness <= 4, frequency <= 30) |>
  	select(word, imagery:frequency )
  	
  str(selected)
  
  # get random samples of selected items
  nitems <- 5
  nlists <- 2
  lists <- selected |>
  	sample_n( nitems*nlists, replace=FALSE) |>
  	mutate(list = rep(1:nlists, each=nitems))
  
  str(lists)
  lists
}

Select observations within a given range

Description

This function masks 'base::within' and so is no longer exported. Eventually it will be removed.

Usage

within(x, a, b)
within(x, a, b)

Arguments

`x`	A vector
`a`	Lower limit
`b`	Upper limit

Value

A logical vector of the same length as x

Examples

WordPools:::within(1:10, 2, 5)

WordPools:::within(1:10, 2, 5)

Package 'WordPools'

Help Index

Word Pools Used in Studies of Learning and Memory

Description

Details

Author(s)

References

Battig - Montague Categorized Word Norms

Description

Usage

Format

Details

Source

References

Examples

Joelson-Hermann Category Properties

Description

Usage

Format

Details

Source

Examples

Paivio, Yuille & Madigan Word Pool

Description

Usage

Format

Details

Source

References

Examples

Select Items from a Word Pool in Given Ranges

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Enhanced density plot for WordPools

Description

Usage

Arguments

Value

Examples

The Toronto Word Pool

Description

Usage

Format

Details

Source

References

Examples

Select observations within a given range

Description

Usage

Arguments

Value

Examples