=Paper=
{{Paper
|id=Vol-1419/paper0117
|storemode=property
|title=Geographical Stability of Generation Frequency Norms for Russian Language
|pdfUrl=https://ceur-ws.org/Vol-1419/paper0117.pdf
|volume=Vol-1419
|dblpUrl=https://dblp.org/rec/conf/eapcogsci/MarchenkoPB15
}}
==Geographical Stability of Generation Frequency Norms for Russian Language==
https://ceur-ws.org/Vol-1419/paper0117.pdf
Geographical Stability of Generation Frequency Norms for Russian Language
Olga P. Marchenko (olga.marchenko@psyexp.ru)
Center of Experimental Psychology, MSUPE, 2A Shelepihinskaya Quay
Moscow, 123390 Russia
Yury G. Pavlov (yury.pavlov@urfu.ru)
Laboratory of Psychophysiology and Psychophysics, Ural Federal University, Mira str., 19
Ekaterinburg, 620002 Russia
Tatyana N. Bandurka (bandurka@list.ru)
Pedagogical Institute of Irkutsk State University (ISU), Nijnyaya Naberejnaya, 6
Irkutsk, 664013 Russia
Abstract ratings for those categories may also vary with cultural
milieu (Medin and Atran, 2004). Thus using a database, that
This study was aimed to examine geographical stability of
generation frequency norms for semantic categories in was collected from subjects of another culture, is not always
Russian language. Participants from three different regions of acceptable. That is why similar studies were conducted in
Russia carried out a standard procedure for generating other countries as well, for example in Belgium (Storms,
exemplars of 45 semantic categories. For each exemplar, 2001, Ruts et al., 2004), France (Bueno & Megherbi, 2009),
overall generation frequency was calculated in each of three New Zealand (Marshall, Parr, 1996), Canada (Kantner,
regions. Correlations of generation frequency data between all Lindsay, 2014), Israel (Henik & Kaplan, 1988), China
three regions were high providing evidence of the
geographical stability of these norms in Russia.
(Yoon et al., 2004), Great Britain (Hampton, Gardiner,
1983), Spain (Marful et al., 2014), etc.
Keywords: Category norms; exemplar generation frequency; It is claimed that a number of new objects have been
geographical stability. created since 1969 in some categories such as vehicles etc.
Furthermore, a decline in knowledge about biological
Introduction categories during the 20th century has been observed, while
It was shown that there are a number of variables, that affect non-biological categories have experienced evolution
performance on different cognitive tasks with words. Such (Wolff et al., 1999). Thus, Battig and Montague’s database
variables include generation frequency ratings (Battig and was updated in 2004 (Van Overschelde et al., 2004).
Montague, 1969), typicality (Rosh, 1975), imageability In order to study categorization in Russia as well it was
(Chiarello et al., 1999), familiarity (Stadthagen-Gonzalez important to create generation frequency norms for the
and Davis, 2006), Age-of-Acquisition (Johnston and Barry, Russian language. Some data has been published regarding
2006, Tainturier et al., 2005, Hernandez, Fiebach, 2006), 13 categories for Russian language in year 1997 (Vysokov
etc. It has been shown that when these variables are not & Lyusin, 1997), serving as a starting point for this line of
controlled results of studies might not be valid (Stewart, research. Considering the ongoing changes, evolution of
1992). language content, it was important to enlarge the quantity of
In order to study categorization it is necessary first to categories documented. Generation frequency database for
identify which words are used by native speakers in specific 45 semantic categories was collected for Russian language
semantic categories (like “A Bird” or “A Tree”), and to later (Marchenko, 2011). This database was collected in
determine generation frequency of these words within Moscow. Many of selected categories were the same as in
categories. This variable was also named instance the study by Battig and Montague. However, some new
dominance by some researches (Mervis et al., 1976, Neely, categories were included (for example "A Domestic
1977). First attempts to create category norms of generation Appliance", "An Organ of the Human Body").
frequency were made by Cohen at al. (1957) in USA. Their It has been shown that some categorization phenomena
work was continued by Battig and Montague during the depend on human experience and can vary between urban
next decade. Battig and Montague’s (1969) database, which citizens and people who live in close contact with nature
contains 56 categories of English language is the most (Medin and Atran, 2004). Thus, it is important to take into
frequently cited database of generation frequency. The account not only cultural but also experiential factors
citation search made by Van Overschelde et al. (2004) on (Winkler-Rhoades et al., 2010, Taverna et al., 2014).
2002 demonstrated that it was cited over 1600 times in Task that which is used to gather generation frequency
papers published in more than 220 different journals. norms can be quite sensitive not only to language and to
Cross-cultural and linguistic research has revealed that the culture aspect but to experiential factors as well (Winkler-
content of categories varies across different cultures (Yoon Rhoades et al., 2010). It gives an impression about concept
et al., 2004) and that patterns of phenomena and variable structure in population. Along with universality of concepts,
704
it can reveal some differences between subjects, who speak could be stronger than between samples from Moscow and
the same language but live in different countries and have Ekaterinburg and Moscow and Irkutsk as it was suggested
different environment (Marshall, Parr, 1996), or who lives that culture in Moscow is quite different from cultures of
in the same environment but belongs to different cultural other regions.
groups in the same country (Winkler-Rhoades et al., 2010).
Category norms collected previously in Moscow were Method
shown to be reliable (Marchenko, 2011). Nevertheless, Participants 312 students of different universities of
taking into account that Russia covers more than one-eighth Moscow aged 18-23 years participated in the study as
of the Earth`s inhabited land, some differences could be volunteers (258 females and 54 males, m=19, SD=1.19).
suggested between distant regions. Thus, before making One hundred seven students from Ekaterinburg aged 18-
inferences and generalizing generation frequency norms 23 years (51 females and 18 males, m=19, SD=.94) and one
collected in Moscow to the Russian language and the whole hundred six students from Irkutsk aged 18-24 years (94
country, geographical stability of these results needs to be females and 12 males, m=19, SD=1.26) participated in this
tested. Thus, it is important to test how similar generation study as well. According to Kruskal-Wallis test there were
frequency data from distant regions will be. Moscow, no significant age differences between samples of these
Irkutsk and Ekaterinburg regions were chosen for this aim three regions (Chi-square=3.779, df=2, p=.151). There were
(Figure 1). no significant difference in proportion of male and female
participants in samples (Pearson Chi-square=2.178, df=2,
two-sided p=0.337).
All of participants were native Russian speakers.
Procedure The procedure used to gather the Russian
category norms was similar to the procedure of Battig and
Montague (1969). Participants were provided with a small
notebook. The following instructions, were copied verbatim
from Battig and Montague (1969), but were translated into
Russian.
“The purpose of this experiment is to find out what items or
objects people commonly give as belonging to various
categories or classes. The procedure will be as follows:
First, you will be given the name or description of a
Figure 1: Schematic map of Russia. category. Then you will be given 30 sec. to write down in
the notebook as many items included in that category as you
Moscow is located on a central part of Russia. The city can, in whatever order they happen to occur to you. For
playing role of political, economic and cultural center in example, if you were given the category "seafood", you
Russia. Ekaterinburg is located on a borderline between might respond with such items as lobster, shrimp, clam,
Europe and Asia on the eastern side of the Ural Mountains. oyster, herring, and so on. The words are to be written in the
Wooded hills and small lakes surround it. Irkutsk is one of notebook, using a different page for every category. When
the biggest cities of Eastern Siberia. The city lies on the you hear the word "Stop", you are to stop writing and go to
Angara River not far away from Lake Baikal and the beginning of the next page. You will then be given the
surrounded by rolling hills within the taiga. name of another category, and again you are to write the
Geographical stability of psychometric data is names of as many members of that category as you can
traditionally tested through correlations between data think of.” The full version of the instruction can be found in
collected in different regions. the paper by Battig and Montague of 1969.
The following suggestions can be made. Generation The category names were read aloud by the experimenter.
frequency data can be accepted as geographically stable and The participants were tested in small groups to be sure that
reliable when there are high correlations between samples they could work in a proper way and will not be distracted
of different regions. The same level of correlations between by each other. The presentation order of the categories was
regions provide additional evidence for geographical randomized and was different in different groups of
stability of generation frequency norms. Strength of participants. The category set for this study consisted of 45
correlations can be related to distance. As cities are closer to different categories such as various natural kinds ("A Fish",
each other, stronger correlation levels can be observed. "An Insect", "A Flower"), artificial kinds ("A Type of
Correlations between the Moscow sample and samples of Vehicle", "An Article of Furniture", "A Musical
other cities could be greater than correlation between these Instrument"), names ("A Male`s First Name"), activity kinds
cities as Moscow, being a melting pot due to constant ("A Profession", "A Sport"), abstract kinds ("A Unit of
migration processes, is more similar to other cities culturally Time", "A Unit of Distance"), etc.
than these cities to each other. On the other hand,
correlation between samples from Irkutsk and Ekaterinburg
705
SPSS and syntax file for Fisher’s r-to-z transformation the list. For each exemplar, overall generation frequency
and for comparing Pearson correlations in SPSS (Weaver, was calculated.
Wuensch, 2013) were used for analyses. Correlation between cities were calculated for further
comparison. All words (even these, which were named only
Results and Discussion one time) were used for this analysis. All Pearson`s
correlations were significant, p<.001. Correlations are
The same procedure of data analysis as in previous works presented in Table 1.
was used (Battig and Montague, 1969, Storms, 2001). No Data can be accepted as geographically stable as
distinction was made between singular and plural or correlations between the three regions were very strong.
masculine and feminine versions of exemplars. Legible
responses that were nonmembers were not removed from
A Reptile 0.98 0.99 0.98
Table 1: Correlation of generation frequency between A Science 0.98 0.96 0.95
three regions for each category. 1 A Sport 0.97 0.97 0.96
A Toy 0.89 0.94 0.85
category MI ME IE A Tree 0.96 0.98 0.97
An Alcoholic Beverage 0.97 0.98 0.97 A Type of Fabric 0.98 0.97 0.97
An Amphibian 0.97 0.99 0.96 A Type of Music 0.97 0.97 0.98
An Article of Clothing 0.95 0.97 0.94 A Type of Vehicle 0.95 0.97 0.96
An Article of Furniture 0.99 0.98 0.99 A Unit of Distance 0.99 0.99 0.99
A Bird 0.97 0.96 0.96 A Unit of Time 0.99 0.99 0.99
A Carpenter`s Tool 0.96 0.98 0.98 A Vegetable 0.98 0.97 0.98
A Color 0.99 1.00 0.99 A Weapon 0.97 0.98 0.96
A Country 0.96 0.97 0.96 A Wild Animal 0.97 0.99 0.97
A Crime 0.97 0.99 0.97
A Disease 0.94 0.94 0.88
A Domestic Animal 0.98 0.98 0.98 Pearson correlations were chosen in order to apply
A Domestic Appliance 0.93 0.85 0.80 Fisher’s r-to-z transformation to compare correlations.
A Family Member 0.98 0.99 0.99 Correlations were compared later using Fisher method for
A Farm Animal 0.99 0.99 0.99 independent samples (Steiger, 1980. Meng et al., 1992.
0.81 0.94 0.81 Weaver, Wuensch. 2013). Results of that comparison is
A Fish
presented in Table 2. Correlations which were significantly
A Flower 0.95 0.97 0.96
(p<.05) and insignificantly different were coded and Chi-
A Four-footed Animal 0.98 0.98 0.97
square was applied.
A Fruit 0.98 0.98 0.96
A Girl`s first name 0.94 0.88 0.85 There were more correlations between Irkutsk and
Ekaterinburg data which did not differ significantly from
An Insect 0.98 0.98 0.96
correlations between Moscow-Ekaterinburg and Moscow-
A Kind of Food 0.85 0.84 0.88
Irkutsk data (Pearson Chi-square=8.022, df=1, p<.01 - IE
A Kitchen Utensil 0.94 0.97 0.97 and ME; Pearson Chi-square=11.756, df=1, p<.001 - IE and
A Male`s First Name 0.93 0.92 0.89 MI).
A Mammal 0.97 0.97 0.94 Thus correlations between the Moscow sample and
A Metal 0.98 0.97 0.97 samples of other cities are not stronger in general than
A Musical Instrument 0.97 0.99 0.97 correlation between these cities and it can`t be suggested
A Nonalcoholic 0.96 0.97 0.97 that Moscow is more similar to other cities culturally than
Beverage these cities to each other. According to these data,
A Part of the Human 0.99 0.98 0.98 correlations between samples from Irkutsk and Ekaterinburg
Body were no stronger, than correlations between samples from
A Plant 0.89 0.93 0.92 Moscow and Ekaterinburg and Moscow and Irkutsk, thus
A Precious Stone 0.97 0.98 0.98 culture in Moscow is not quite different from cultures of
A Profession 0.92 0.92 0.91 other regions.
An Organ of the 0.99 0.98 0.97 In order to test if there are connection between distance
Human Body and strength of consistency for generation frequency norms
correlations between data from cities, which are closer to
each other (like Moscow and Ekaterinburg, Ekaterinburg
1 MI –Correlations between Moscow and Irkutsk samples.
and Irkutsk) were compared to correlations between cities,
ME - Correlations between Moscow and Ekaterinburg samples. which are located on a greater distance from each other (like
IE - Correlations between Irkutsk and Ekaterinburg samples.
706
Moscow and Irkutsk). Frequency of correlations between p=.180). There were no stronger correlations between
Moscow and Ekaterinburg data which did not differ Ekaterinburg and Irkutsk data in comparison to correlations
significantly from correlations between Moscow and Irkutsk between Moscow and Irkutsk data (Pearson Chi-
was almost the same as frequency of correlations which square=11.756, df=1, p<.001). Frequency of greater
were significantly different (Pearson Chi-square=.556, df=1, correlation between Irkutsk and Ekaterinburg data in
p=.456). Correlations, which were significantly different, comparison to correlations of Moscow and Irkutsk data
analyzed separately from insignificant correlations. were equal to frequency of lower correlations (Pearson Chi-
Frequency of stronger correlations between Moscow and square=.818, df=1, p<.366) Thus, the strength of
Ekaterinburg in comparison to correlations between correlations is not related to distance. There were no
Moscow and Irkutsk data did not differ from frequency of significantly stronger correlation levels for cities which are
weaker correlations (Pearson Chi-square=1.80, df=1, closer to each other.
.107 .314 .620
Table 2: Comparison of correlation coefficients between A Four-footed .000 -1.428 -1.416
three regions for each category.2 Animal 1.000 .153 .157
A Fruit -.605 -1.792 -1.212
category MI-ME IE-ME IE-MI .545 .073 .225
Z, p Z, p Z, p A Girl`s first 3.843 -1.328 -4.836
An Alcoholic -1.181 -1.253 -.210 name ˂.001 .184 .001
Beverage .237 .210 .834 An Insect -.630 -2.298 -1.700
An Amphibian -3.565 -3.670 -.355 .529 ˂.05 .089
˂.001 ˂.001 .722 A Kind of Food .205 1.676 1.489
An Article of -2.570 -2.731 -.346 .838 .094 .136
Clothing ˂.010 ˂.01 .729 A Kitchen -2.906 .000 2.719
An Article of 2.820 2.257 -.346 Utensil ˂.01 1.000 ˂.01
Furniture ˂.01 ˂.05 .729 A Male`s First .906 -1.802 -2.633
A Bird .819 .000 -.774 Name .365 .072 ˂.01
.413 1.000 .439 A Mammal -.383 -2.569 -2.252
A Carpenter`s -3.053 .000 2.723 .702 ˂.05 ˂.05
Tool ˂.01 1.000 ˂.01 A Metal 2.153 .000 -1.894
A Color -20.159 -13.585 -1.089 ˂.05 1.000 .058
˂.001 ˂.001 .276 A Musical -4.242 -3.745 .218
A Country -1.317 -1.220 .000 Instrument ˂.001 ˂.001 .827
.188 .223 1.000 A Nonalcoholic -1.216 .000 1.109
A Crime -5.844 -4.560 .399 Beverage .224 1.000 .267
˂.001 ˂.001 .690 A Part of the 3.266 .000 -2.979
A Disease -.191 -3.736 -3.591 Human Body ˂.01 1.000 ˂.01
.849 ˂.001 ˂.001 A Plant -2.597 -.717 1.673
A Domestic .575 .000 -.530 ˂.01 .473 .094
Animal .565 1.000 .596 A Precious -.823 .000 .744
A Domestic 3.076 -1.055 -3.787 Stone .410 1.000 .457
Appliance ˂.01 .291 ˂.001 A Profession -.389 -.689 -.338
A Family -1.443 .000 1.359 .697 .491 .735
Member .149 1.000 .174 An Organ of the 2.629 -1.368 -3.663
A Farm Animal -.263 .000 .245 Human Body ˂.01 .171 ˂.001
.793 1.000 .807 A Reptile -1.355 -1.604 -.378
A Fish -5.462 -5.224 -.049 .175 .109 .705
˂.001 ˂.001 .961 A Science 4.542 -1.000 -5.086
A Flower -1.611 -1.008 .496 ˂.001 .317 ˂.001
A Sport -.315 -1.286 -1.000
.753 .198 .317
2 MI-ME - comparison of correlation coefficients between
A Toy -3.696 -5.106 -1.792
Moscow and Irkutsk sample with correlation coefficients between ˂.001 ˂.001 .073
Moscow and Ekaterinburg sample.
A Tree -2.252 -1.243 .795
IE-ME - comparison of correlation coefficients between Irkutsk
and Ekaterinburg sample with correlation coefficients between ˂.05 .214 .426
Moscow and Ekaterinburg sample. A Type of 1.823 .000 -1.628
IE-MI - comparison of correlation coefficients between Irkutsk and Fabric .068 1.000 .104
Ekaterinburg sample with correlation coefficients between A Type of -.338 1.831 2.111
Moscow and Irkutsk sample.
707
Music .736 .067 ˂.05 contemporary English. Behavior Research Methods,
A Type of -1.666 -1.055 .517 Instruments, & Computers, 31(4), 603-637.
Vehicle .096 .292 .605 Cohen, B. H., Bousfield, W. A., & Whitmarsh, G. A.
A Unit of .540 .000 -.611 (1957). Cultural norms for verbal items in 43 categories
Distance .589 1.000 .542 (Tech. Rep.No. 22). Storrs, CT: University of
A Unit of Time -.964 .000 .895 Connecticut.
.335 1.000 .371 Gutchess, A.H., Yoon, C., Luo, T., Feinberg, F., Hedden,
A Vegetable 1.308 1.048 -.132 T., Jing, Q., Nisbett R.E. (2006). Park D.C. Categorical
.191 .295 .895 organization in free recall across culture and age.
A Weapon -2.498 -3.174 -.875 Gerontology, 52(5), 314-323.
˂.05 ˂.01 .382 Hampton, J. A., Gardiner, M. M. (1983). Measures of
A Wild Animal -5.518 -4.648 .534 internal category structure: a correlation analyses of
˂.001 ˂.001 .593 normative data. British journal of Psychology. 74. 491–
516.
As correlations between the three regions are strong, Henik, A., & Kaplan, L. (1988). Category content: Findings
geographical stability of generation frequency norms for for categories in Hebrew and a comparison to findings in
Russian language can be suggested. Nevertheless, this work the US. Israel Journal of Psychology, 1, 104–112.
was aimed to prove geographical stability and further Hernandez, A. E., Fiebach, Ch. J. (2006). The brain bases of
analyses can be continued in order to study regional reading late learned words: Evidence from functional
specificity of concepts with more sensitive statistic methods. MRI. Visual Cognition, 13(7/8), 1027-1043.
There were no evidence for connection between strength Howard, D.V. (1980). Category Norms: a Comparison of
of correlations and geographical distance. Correlations the Battig and Montague (1969) Norms with the
between Moscow sample with samples from the other two responses of adults between the ages of 20 and 80. The
cities were not greater than between these cities. Correlation Journal of Gerontology, 35 (2), 225-231.
between Ekaterinburg and Irkutsk data were no stronger Johnston, R.A., & Barry, Ch. (2006). Age of acquisition and
than between data from these two cities and Moscow. This lexical processing. Visual cognition, 13 (7/8), 789-845.
fact suggests stability of generation frequency norms in Kantner, J, Lindsay, D. S. (2014). Category exemplars
Russian database and domination of the same culture around normed in Canada. Canadian Journal of Experimental
the whole area of the country. Similar pattern was observed Psychology/Revue canadienne de psychologie
for English language when comparison of category norms expérimentale, 68(3), Sep, 163-165.
collected in different regions of the same country conducted Marchenko, O.P. (2011). Psycholinguistic Database for
(Battig and Montague, 1969). English and Chinese category Russian Language In: Kokinov, B., Karmiloff-Smith, A.,
norms of different age groups within a culture were also Nersessian, N.J. (eds.) European Perspectives on
similar (Howard, 1980, Yoon et al., 2004, Gutchess et al., Cognitive Science. New Bulgarian University Press.
2006). As norms of generation frequency are geographically Marful, A. Díez, E. Fernandez, A. (2014). Normative data
stable, the same generation frequency norms can be used for for the 56 categories of Battig and Montague (1969) in
Russian language around the whole country. Spanish. Behavior Research Methods. Aug 27
Marshall, C. E., Parr, W. V. (1996) New Zealand norms for
a subset of Battig and Montague's (1969) categories. New
Acknowledgments Zealand Journal of Psychology, 25(1), 24-29.
This work was supported by Russian Humanitarian Medin, D.L., & Atran, S. (2004). The native mind:
Foundation, grant “Cultural specificity and universality of biological categorization, reasoning and decision making
normative ratings for words and pictures” 14-36-01309. in development across cultures. Psychological Review,
111(4), 960-983.
Meng, X., Rosenthal, R., Rubin, D. B. (1992). Comparing
correlated coefficients. Psychological bulletin, 111, 172-
References 175.
Battig, W.F., & Montague, W.E. (1969). Category norms Mervis, C.B., Catlin, J., Rosh E. (1976). Relationships
for verbal items in 56 categories: a replication and among goodness-of-example, category norms and word
extension of the Connecticut category norms. Journal of frequency. Bulletin of the Psychonomic Society, 7, 283-
Experimental Psychology Monograph, 80(3), 1-45. 284.
Bueno, S. & Megherbi, H. (2009). French categorization Neely, J.H. (1977). Semantic priming and retrieval from
norms for 70 semantic categories and comparison with lexical memory: Roles of inhibitionless spreading
Van Overschelde et al.'s (2004) English norms. Behavior activation and limited-capacity attention. Journal of
Research Methods, 41, 1018-1028. Experimental Psychology: General, 106, 226-254.
Chiarello, C., Shears, C., Lund, K. (1999). Imageability and
distributional typicality measures of nouns and verbs in
708
Rosh, E. (1975). Cognitive representations of semantic
categories. Journal of Experimental Psychology: General,
104, 192-233.
Ruts, W., De Deyne, S., Ameel, E., Vanpaemel, W.,
Verbeemen, T., Storms, G. (2004). Dutch norm data for
13 semantic categories and 338 exemplars. Behavior
Research Methods, Instruments, and Computers, 36, 506-
515.
Stadthagen-Gonzalez, H., & Davis, C.J. (2006). The Bristol
Norms for Age of Acquisition, Imageability and
Familiarity. Behavior Research Methods, 38, 598-605.
Stewart, F., Parkin, A. J., Hunkin, N. M. (1992). Naming
impairments following recovery from herpes simplex
encephalitis: Category-specific? Quarterly Journal of
Experimental Psychology, 44A, 261-284.
Steiger, J.H. (1980). Tests for comparing elements of a
correlation matrix. Psychological Bulletin, 87,245-251.
Storms, G. (2001). Flemish category norms for exemplars of
39 categories: A replication of Battig and Montague
(1969) category norms. Psychologica Belgica, 41, 145-
168.
Tainturier, M.-J., Tamminen, J., Thierry, G. (2005). Age of
acquisition modulates the amplitude of the P300
component in spoken word recognition. Neuroscience
Letters, 379, 17-22.
Taverna, A. S.; Waxman, S. R.; Medin, D. L., Moscoloni,
N. & Peralta, O. A. (2014). Naming the living things:
linguistic, experiential and cultural factors in Wichí and
Spanish speaking children. Journal of Culture and
Cognition 14, 213-233.
Van Overschelde, J. P., Rawson, K. A., Dunlosky, J. (2004).
Category norms: An updated and expanded version of the
Battig and Montague (1969) norms. Journal of Memory
and Language. 50, 289-335.
Vysokov, I.E., & Lyusin, D.V. (1997). Internal structure of
natural categories: generation frequency.
Psichologichesky Zhurnal, 18(4), 69-77.
Weaver, B., Wuensch, K. L. (2013). SPSS and SAS
programs for comparing Pearson correlations and OLS
regression coefficients Behavior Research Methods
45:880–895.
Winkler-Rhoades, N., Medin, D. L., Waxman, S. R., &
Woodring, J., Ross, N. O. (2010). Naming the animals
that come to mind: Effects of culture and experience on
category fluency. Journal of Cognition and Culture. 10,
205-220.
Wolff, P., Medin, D.L., Pankratz, C. (1999). Evolution and
devolution of folkbiological knowledge. Cognition, 73,
177-204.
Yoon, C., Feinberg, F., Hu, P., Gutchess, A.H, Hedden, T.,
Chen, H., Jing, Q., Cui, Y., Park, D.C. (2004). Category
norms as a function of culture and age: Comparisons of
item responses to 105 categories by American and
Chinese adults. Psychology and Aging, 19, 379-393.
Matlock, T. (2001).
709