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Behavior Research Methods

, Volume 45, Issue 4, pp 1144–1158 | Cite as

A visual object naming task standardized for the Croatian language: A tool for research and clinical practice

  • Maja Rogić
  • Ana Jerončić
  • Marija Bošnjak
  • Ana Sedlar
  • Darko Hren
  • Vedran Deletis
Article
  • 769 Downloads

Abstract

The aim of the present study was to provide normative data for the Croatian language using 346 visually presented objects (Cycowicz, Friedman, Rothstein, & Snodgrass Journal of Experimental Child Psychology 65:171-237, 1997; Roach, Schwartz, Martin, Grewal, & Brecher Clinical Aphasiology 24:121-133, 1996; Snodgrass & Vanderwart Journal of Experimental Psychology: Human Learning and Memory 6:174–215, 1980). Picture naming was standardized according to seven variables: naming latency, name agreement, familiarity, visual complexity, word length, number of syllables, and word frequency. The descriptive statistics and correlation pattern of the variables collected in the present study were consistent with normative studies in other languages. These normative data for pictorial stimuli named by young healthy Croatian native speakers will be useful in studies of perception, language, and memory, as well as for preoperative and intraoperative mapping of speech and language brain areas.

Keywords

Visual object naming Naming latency Name agreement Familiarity Visual complexity Word frequency Word length Number of syllables Visually presented objects Croatian 

Visual object naming (VON) is a fundamental ability that a person uses during language communication. Visually presented object (VPOs) are commonly used as stimulus materials in studies of the development of cognitive functions, memory, and perception, and recently for preoperative and intraoperative mapping of speech and language brain areas (Duffau, Leroy, & Gatignol, 2008; Schuhmann, Schiller, Goebel, & Sack, 2009). VPOs are also used in psycholinguistic research of components of language production, such as lexical and phonological encoding. The pictorial stimuli that are used generally differ in several respects: (a) name agreement (i.e., the degree to which subjects agree with the name of the VPO), (b) age of acquisition (of the specific word) (c) visual complexity of the displayed object (i.e., the amount of lines and details in the drawing), (d) familiarity of the concept depicted, (e) naming latency (the time needed to provide an adequate name for the VPO after stimulus presentation), and (f) frequency of use of target words in speech. In the recent study of Brodeur, Dionne-Dostie, Montreuil, and Lepage (2010), photo stimuli were also normalized for name, category, familiarity, visual complexity, object agreement, viewpoint agreement, and manipulability. Among the most important characteristics of VPOs are name agreement, familiarity, and visual complexity (Pompéia, Miranda, & Amodeo Bueno, 2001). It has been shown that familiarity is an essential predictor of the naming latency (i.e., faster for more familiar VPOs), while visual complexity affects the memory ability for a VPO and the naming latency (Alario & Ferrand, 1999). Naming agreement is a strong predictor of naming difficulties that may arise during the process of VON; therefore, this variable is very important in studies investigating naming latency, recall, recognition, and verbal coding (Pompéia et al., 2001). Moreover, name agreement is specific for each language, despite similarities in familiarity and visual complexity (Sanfeliu & Fernandez, 1996).

In addition, correlations between naming latency, frequency of use of the word, familiarity, age of acquisition, visual complexity, and word length have often been used to conclude which of these variables the key factors when naming VPOs (Cycowicz, Friedman, Rothstein, & Snodgrass, 1997). Most studies have reported standards for familiarity and visual complexity (Martein, 1995; Pompéia et al., 2001; Sanfeliu, & Fernandez, 1996), but only a few have determined norms for naming latencies (Bates et al., 2003). It has been shown that the names for VPOs learned early in life are pronounced faster than names learned in later years, and that the age of acquisition, rather than the frequency of use of a specific word in speech, has a stronger effect on naming latencies (Carroll & White, 1973). It has also been shown that the length of words associated with VPOs influences naming latencies (Morrison, Ellis, & Quinlan, 1992).

To control for variations of these characteristics, it is necessary to have normative data for VPOs. Normative data are values collected from a sample that is representative of the tested population (in this study, young healthy adults). On the basis of these values, it can be seen how the measured values are distributed in the population, and referent values can be obtained. This implies that the particular observation can be easily categorized in the corresponding quintile and compared with observations from other studies.

Normative data for VPOs were not available until Snodgrass and Vanderwart (1980) standardized data for 260 black and white drawings. Normative data are now available for several languages: American English (Cycowicz et al., 1997; Snodgrass & Vanderwart, 1980), British English (Barry, Morrison, & Ellis, 1997; Vitkovich & Tyrrell, 1995), Italian (Dell’Acqua, Lotto, & Job, 2000), Portuguese (Pompéia et al., 2001), French (Alario & Ferrand, 1999; Bonin, Peereman, Malardier, Méot, & Chalard, 2003), Spanish (Cuetos, Ellis, & Alvarez, 1999; Fernandez, Diez, Alonso, & Beato, 2004; Moreno-Martínez, Montoro, & Laws, 2011; Sanfeliu & Fernandez, 1996), Dutch (Martein, 1995), Icelandic (Pind, Jónsdóttir, Gissurardóttir, & Jónsson, 2000), Greek (Dimitropoulou, Duñabeitia, Blitsas, & Carreiras, 2009), and Russian (Tsaparina, Bonin, & Méot, 2011). In addition, normative data for VPOs have been measured for children (Berman, Friedman, Hamberger, & Snodgrass, 1989; Cycowicz et al., 1997; Pompéia et al., 2001) as well as adults (Alario & Ferrand, 1999; Dell’Acqua et al., 2000; Pompéia et al., 2001; Bonin et al., 2003; Snodgrass, & Vanderwart, 1980).

Much attention has focused recently on the errors/disturbances in a VPO task that are produced by patients with various neurological impairments, as compared to healthy volunteers. These errors can provide specific information about the naming process, such as object identification, activation of certain names, and generation of responses (Cycowicz et al., 1997). VON is one of the language tasks commonly used in preoperative testing of anomia in patients with brain lesions. The VON task has also been used during intraoperative mapping of specific speech-related cortical areas (Duffau et al., 2008; Ojemann, 1993). The main function of VON in these settings is preservation of critical language areas, and consequently the reduction of postoperative speech and language deficits.

In the present study, we aimed to obtain normative data for the native Croatian-speaking population using 346 VPOs (Cycowicz et al., 1997; Roach et al., 1996; Snodgrass & Vanderwart, 1980) from young healthy adults, using a methodology that closely follows previously published procedures (Bonin et al., 2003; Cycowicz et al., 1997; Pompéia et al., 2001; Snodgrass & Vanderwart, 1980).

The VPOs were standardized according to seven variables: naming latency, name agreement, familiarity, visual complexity, word length, number of syllables and word frequency.

Method

Subjects

The study included 50 subjects. According to Crawford and Howell (1998), this sample size is adequate for normative studies. The subjects included 30 women, mean age 21.56 years (range 19–30), and 20 men, mean age 21.6 years (range 19–26). All of the subjects were students and healthy Croatian native speakers, recruited from the School of Medicine at the University of Split during 2011. The ethics committee of the School of Medicine approved the protocol, and informed consent was signed by all subjects.

Stimuli and procedure

The VPOs in this study were taken from three normative studies (Cycowicz et al., 1997; Roach et al., 1996; Snodgrass & Vanderwart, 1980). From the total of 409 VPOs, we selected 352 depicted in black on a white background (138 pictures from Snodgrass & Vanderwart, 1980; 112 from Roach et al., 1996; and 102 from Cycowicz et al., 1997). To be included in our database, each picture had to meet three criteria: (a) high frequency of usage in everyday life of the word associated with the VPO; (b) low complexity of the VPO (e.g., in terms of lines), and (c) high familiarity with the VPO among native speakers. Each VPO was rated on a scale from 1 to 3 for each of the above-mentioned characteristics by two raters. For frequency of word usage, each rater rated a specific VPO with a 1 (for low frequency), 2 (for average frequency), or 3 (high frequency); for complexity, VPO were rated 1 (for low), 2 (for medium), or 3 (for high); and for familiarity, speakers rated VPOs with a 1 (poor), 2 (good), or 3 (very good). To be included in our database, the pictures had to be rated 3 for frequency, 1 for complexity, and 3 for familiarity, with both raters differing on at most one criterion by at most one point. If the raters disagreed on the ratings for a picture, a consensus was sought whenever possible. Given that the standards were specific to a particular language, it was not suitable to simply choose a set of VPOs from other languages. For example, pagoda, used frequently in the Chinese language but not in Croatian, was not included in our database. We also included only VPOs whose names in Croatian consisted of only one word. The final set included 346 images, which were divided into six blocks. The order of presentation of the VPOs was randomized within the block, and blocks were randomized across the subjects.

Subjects were tested in a quiet room of the Laboratory for Human and Experimental Neurophysiology, School of Medicine, University of Split. Testing averaged 50 min per subject. For the VON task, the subjects were instructed to give the name that they would most frequently use in their speech, because in the Croatian language some VPOs can be named with more than one synonym (e.g., mlin/vjetrenjača “windmill”). All subjects were informed that if they experienced any fatigue, the examiner might temporarily suspend the testing, thereby optimizing the outcome of the study.

The day after they took the VON task, the subjects also completed a questionnaire in which they rated the familiarity and complexity of the presented pictures. The pictures of all 346 visual objects were printed (in 300 dpi), with one object per page in a booklet. The subjects were given the printed images and separate evaluation tables, one for familiarity and the other for visual complexity. Each evaluation table contained in a row the information on the object name, object ID number, and the rating scale for the variable. Numerical ratings from 1 to 5, with clear notations of which number corresponded to the high and low ratings (see the Familiarity and Visual Complexity sections below), were used with both scales. The order of the printed images corresponded to the order of object names in the evaluation sheets. Subjects were instructed to finish one questionnaire and then to proceed to the other one.

Measurements

The 346 pictures were presented in six randomized blocks using the Presentation program (Neurobehavioral Systems, Albany, CA) on a computer monitor (LG 22-in. LCD screen with 1,920 × 1,080 resolution). Each block contained from 56 to 58 objects in randomized order. The pictures were centered, and the size of each picture corresponded to a quarter of the monitor surface. Each presented VPO was regarded as a unique stimulus, and responses were analyzed separately. The response of a subject was a spoken word recorded with a microphone (Logitech Digital USB) installed on the testing PC. The naming latency and answer were recorded by the sound response device, which was controlled by the Presentation software. A change in the amplitude of the recorded sound above the threshold value of 10 % was detected as a sound event. After the first event was detected for the presented VPO, the device started to record for 3 s. During that period, if the sound was not constant but there was a quiet period in the middle, additional sound responses were detected. This was necessary in order to compensate for background noise or hesitation sounds from the subjects. The total duration of a trial, from the presented VPO until the presentation of the next object, was 5 s. This time included 1 s of blank screen presentation preceding the presentation of the next object. The time from presentation of the picture to the recorded response was registered using the Presentation software and was stored in a separate log file on the testing PC.

The order of presentation of the blocks of pictures was randomly assigned for each subject. Subjects were advised to be calm during the testing and to avoid sudden movements and loud breathing.

Naming latency

The naming latency was defined as the time required to give the name for a VPO, measured from onset of the picture presentation to the beginning of a voice response.

Name agreement

Name agreement refers to the degree to which subjects agreed on the object name of the particular VPO. The data for name agreement were collected simultaneously with the data for naming latency.

The modal name was defined during postacquisition analysis as the name given by the majority of the subjects, and it was further classified into one of four categories, according to the percentage of subjects who named a VPO by the same object name: (a) 0 %–50 %, (b) 51 %–89 %, (c) 90 %–99 %, or (d) 100 %.

Nonmodal names (i.e., object names that differed from the modal name) were classified into the following categories (based on Snodgrass & Vanderwart, 1980) in offline analysis; synonym, superordinate, subordinate, component (part of), coordinate (same category), semantic (failure in meaning), and failure (answer not given, description, or word without meaning).

In assessing the name agreement two measures were used: the percentage of subjects who used the modal name, and the H index, calculated in the following manner:
$$ H=\sum\nolimits_{i=1}^k {{P_i}\mathrm{lo}{{\mathrm{g}}_2}\left( {\frac{1}{{{P_i}}}} \right),} $$
where k is the number of different names given for each picture, and P i is the proportion of subjects who gave each name. The greater the naming agreement between subjects, the closer that H is to 0 (Snodgrass & Vanderwart, 1980).

Familiarity

This variable refers to the familiarity of the concept depicted, or how often subjects come in contact with and/or how often they think about the displayed object. On the questionnaire, subjects were instructed to evaluate the concept for each presented VPO on a scale ranging from 1 to 5 (1 = very unfamiliar, 2 = unfamiliar, 3 = medium, 4 = familiar, 5 = very familiar).

Visual complexity

Visual complexity refers to the number of lines and details in the drawing. On the questionnaire, subjects were instructed to evaluate the concept for each presented VPO on a scale from 1 to 5 (1 = least complex, 5 = most complex).

Frequency of word

Since normative data for frequency of word usage in speech are not available in the Croatian language, in our study we used the frequency of words in written language from Moguš, Bratanić, and Tadić (1999). The calculated frequency of usage was obtained from an analysis of different types of written sources: drama, newspapers, prose, verses, and textbooks. The sum frequency of word usage is presented as the occurrence of the word per million. On the basis of the written sources, the words were divided into five corpuses with 200,000 words in each corpus. The dictionary is divided into three parts:
  1. (a)

    A dictionary with frequency of usage (list of lemmas), including the following data for each lemma: types of word, meaning or determination, mark for nonstandard, ranking, absolute frequency, relative frequency, and representation in the specific corpus;

     
  2. (b)

    Alphabetic dictionary (alphabetic list of lemmas with labels related to frequency of usage), including the data listed in (a), but with the lemmas listed in alphabetic order;

     
  3. (c)

    Alphabetic dictionary with tokens (alphabetic list of lemmas with tokens associated with their frequency of usage), including the data listed in (a).

     

A professor of the Croatian language (one of the authors of this study) assigned values for absolute and relative frequency of usage according to Moguš et al. (1999) for 352 words, representing each of the pictures.

Length of word and number of syllables

For each modal name, we also determined the length of the word, in terms of both the number of sounds in the word and the number of syllables.

Data analysis

Naming latencies of incorrect responses (wrong names) were not included in the analyses. In addition, naming latencies in trials in which we experienced technical difficulties with the voice key were also excluded (a total of 0.8 % of the data). Trials with correct responses but with hesitation sounds were included in the analysis, as we were able to correctly mark the naming latencies for each object in such cases.

For description of the data, the usual descriptors were used: mean and standard deviation, median, total, and interquartile range. We also calculated 95 % confidence intervals (CIs) for the means and medians to include in the data description. To estimate the CI, we used the bias-corrected and accelerated bootstrapping method with 2,000 replications. This method improves the precision of asymptotic approximations in small samples and is effective in cases in which normality is violated.

The item difficulty (percentage of the sample who answered each item correctly) and discrimination (corrected item–total correlation) indices were used in the individual item analyses.

Correlations between variables were tested using Spearman’s rho (r) coefficient. Significance was inferred from the 95 % CI. If a 95 % CI for a correlation coefficient did not include the value of 0, then significance was inferred. Likewise, the 95 % CI for the median was used in comparisons of naming latencies across the modal categories. The corresponding level of significance was .05. The analysis was performed using the statistical package SPSS Statistics, version 19.

Results

From the total of 346 pictures presented, all subjects used the same object name for 61 of the VPOs (see Supplementary Fig. 1). Original pictures can be found in Supplementary Fig. 2 (d). Descriptive statistics for each of these 61 objects are available in Table 1. Croatian to English translation of modal responses for these objects can be found in Supplementary Table 2.
Table 1

Descriptive statistics for the 61 visually presented objects (VPOs) for which the same object name was given by all subjects, together with the number of syllables in the particular name and its word length and frequency of usage in the Croatian language (per million words)

Modal Name

Naming Latency (ms)

Visual Complexity

Familiarity

      

M

SD

MD

Q1

Q3

95 % CI

M

SD

MD

Q1

Q3

95%CI

M

SD

MD

Q1

Q3

95 % CI

NS*

LW

AF

lav

903

305

810

715

967

813–992

2.8

1.61

3

1

4

2.4–3.3

2.4

1.61

2

1

4

1.9–2.9

1

3

24

stol

1,003

673

861

756

943

805–1,200

1.8

1.07

1

1

2

1.5–2.1

4.8

0.53

5

5

5

4.7–5.0

1

4

436

banana

907

606

778

698

876

729–1,085

1.8

0.95

1.5

1

2

1.5–2.1

4.2

0.97

4.5

4

5

3.9–4.5

3

6

5

prsten

1,145

587

1,009

854

1,165

973–1,317

1.9

1.07

2

1

2

1.6–2.2

3.8

1.44

4

3

5

3.4–4.2

2

6

39

kalendar

1,012

475

883

805

1,080

872–1,151

2.8

1.59

3

1

4

2.4–3.3

4.0

1.18

4

3

5

3.6–4.3

3

8

5

nož

1,054

723

901

727

1,072

842–1,267

1.4

0.57

1

1

2

1.2–1.5

4.7

0.66

5

4

5

4.5–4.9

1

3

160

ananas

921

275

865

784

1,038

840–1,002

2.7

1.56

2

1

4

2.3–3.1

3.1

1.18

3

2

4

2.8–3.5

3

6

1

most

1,064

356

954

840

1,265

960–1,169

3.2

1.62

3

2

5

2.7–3.7

3.0

1.40

3

2

4

2.6–3.4

1

4

81

pas

999

165

1,003

898

1,091

951–1,048

2.5

1.34

2

1

3

2.1–2.8

4.3

1.19

5

4

5

4.0–4.7

1

3

163

zmija

1,154

1,122

821

704

933

824–1,483

2.1

1.21

2

1

3

1.8–2.5

2.4

1.41

2

1

3

1.9–2.8

2

5

57

knjiga

828

160

805

724

871

781–875

1.7

1.00

1

1

2

1.4–2.0

4.3

1.15

5

4

5

4.0–4.6

2

5

248

boca

1,002

462

851

751

1,109

867–1,138

1.4

0.66

1

1

2

1.2–1.5

4.3

1.06

5

4

5

3.9–4.6

2

4

87

žaba

1,081

483

942

850

1,084

940–1,223

2.5

1.27

2

1

3

2.1–2.9

2.4

1.40

2

1

4

2.1–2.9

2

4

28

rukavica

1,039

306

967

843

1,131

949–1,129

1.8

0.85

2

1

2

1.5–2.0

3.6

1.31

4

3

5

3.3–4.0

4

8

21

sova

1,140

756

966

854

1,123

918–1,362

2.7

1.50

3

1

4

2.3–3.2

2.3

1.41

2

1

3

1.9–2.7

2

4

11

šator

881

248

834

716

966

808–954

1.7

0.97

1

1

2

1.4–2.0

2.5

1.52

2

1

4

2.1–3.0

2

5

31

deva

950

404

851

791

961

832–1,069

2.2

1.21

2

1

3

1.9–2.5

2.0

1.44

1

1

3

1.6–2.5

2

4

7

sendvič

997

499

916

774

1,086

850–1,143

2.3

1.21

2

1

3

2.0–2.7

4.7

0.68

5

5

5

4.5–4.9

2

7

3

traktor

1,139

757

958

838

1,125

917–1,362

2.7

1.59

3

1

4

2.3–3.2

2.7

1.52

3

1

4

2.2–3.1

2

7

14

sidro

1,322

723

1,104

982

1,435

1,110–1,534

2.2

1.24

2

1

3

1.9–2.6

2.8

1.47

3

1

4

2.4–3.2

2

5

9

nos

1,131

1,271

869

774

997

758–1,504

1.4

0.64

1

1

2

1.2–1.6

4.6

0.89

5

5

5

4.3–4.9

1

3

107

konj

899

232

829

754

970

831–967

2.4

1.31

2

1

4

2.0–2.8

2.6

1.39

2

1

4

2.2–3.0

1

3

170

ključ

1,007

229

1,001

847

1,089

939–1,074

2.0

1.23

2

1

3

1.7–2.4

4.6

0.84

5

5

5

4.4–4.9

1

4

44

križ

844

203

809

704

926

785–904

1.5

0.81

1

1

2

1.2–1.7

4.3

1.05

5

4

5

4.0–4.6

1

4

99

mačka

1,039

508

898

759

1,105

890–1,188

2.1

1.13

2

1

3

1.8–2.5

4.1

1.15

5

3

5

3.8–4.4

2

5

64

puž

979

369

863

788

1,018

871–1,087

2.2

1.14

2

1

3

1.9–2.5

2.7

1.32

3

2

4

2.4–3.2

1

3

33

sat

1,117

678

931

820

1,115

918–1,316

2.3

1.26

2

1

3

1.9–2.6

4.6

0.94

5

5

5

4.3–4.9

1

3

464

kosa

1,100

322

1,025

902

1,222

1,005–1,194

2.4

1.46

2

1

4

2.0–2.8

4.7

0.76

5

5

5

4.5–5.0

2

4

214

sunce

1,392

1,538

952

799

1,076

940–1,843

1.6

0.75

1

1

2

1.4–1.8

4.6

0.87

5

4

5

4.3–4.8

2

5

661

vrata

980

786

782

674

893

750–1,211

2.1

1.21

2

1

3

1.8–2.5

4.7

0.83

5

5

5

4.4–4.9

2

5

648

slon

1,090

1,098

877

809

1,018

767–1,412

2.4

1.37

2

1

4

2.0–2.8

2.2

1.44

2

1

3

1.8–2.6

1

4

20

čekić

1,423

838

1,184

922

1,559

1,177–1,669

2.1

1.01

2

1

3

1.8–2.4

3.1

1.34

3

2

4

2.7–3.4

2

5

27

kruna

909

250

873

782

992

836–983

2.7

1.42

3

1

4

2.3–3.1

2.0

1.26

2

1

3

1.7–2.4

2

5

52

leptir

871

269

794

700

931

792–950

2.5

1.49

2

1

4

2.0–2.9

3.2

1.25

3

3

4

2.9–3.6

2

6

35

kruška

982

275

930

848

1,031

901–1,063

1.3

0.65

1

1

1

1.1–1.5

3.8

1.07

4

3

5

3.5–4.1

2

6

21

metla

1,061

954

798

730

971

781–1,341

2.0

1.12

2

1

3

1.7–2.3

3.9

1.22

4

3

5

3.6–4.3

2

5

7

oko

889

494

778

689

933

744–1,034

2.4

1.41

2

1

3

2.0–2.8

4.7

0.82

5

5

5

4.5–5.0

2

3

297

akvarij

1,328

381

1,274

1028

1,524

1,216–1,440

3.0

1.71

3

1

5

2.5–3.5

3.1

1.35

3

2

4

2.6–3.4

3

7

1

kaktus

1,135

382

1,040

870

1,195

1,023–1,247

2.8

1.42

3

2

4

2.4–3.2

2.6

1.44

2.5

1

4

2.2–3.0

2

6

6

jaje

1,136

1,231

817

730

999

774–1,497

1.7

0.94

1

1

2

1.4–1.9

3.9

1.25

4

3

5

3.5–4.2

2

4

34

kaciga

1,123

1,115

918

833

1,033

796–1,450

1.9

0.87

2

1

3

1.7–2.2

3.3

1.38

3

2

4

2.9–3.6

3

6

11

bazen

1,097

582

987

868

1,127

927–1,268

2.5

1.34

2

1

4

2.2–2.9

3.3

1.32

4

2

4

2.9–3.7

2

5

20

duga

1,057

299

996

875

1,167

969–1,145

2.5

1.42

2

1

3

2.1–2.9

3.6

1.24

4

3

5

3.3–4.0

2

4

49

puška

1,446

1,766

943

808

1,184

928–1,965

2.1

1.06

2

1

3

1.8–2.4

2.5

1.46

2

1

3

2.0–2.9

2

5

85

robot

892

160

855

800

953

845–939

2.3

1.14

2

1

3

1.9–2.6

2.1

1.30

2

1

3

1.8–2.5

2

5

9

krov

1,056

294

977

890

1,168

970–1,143

2.1

0.98

2

1

3

1.8–2.3

3.9

1.20

4

3

5

3.6–4.3

1

4

153

lopata

1,099

731

939

800

1,084

885–1,314

1.9

0.97

2

1

2

1.6–2.1

2.8

1.14

3

2

4

2.4–3.1

3

6

18

kravata

1,001

566

926

787

1,038

835–1,167

2.2

1.17

2

1

3

1.8–2.5

3.5

1.27

4

3

4

3.1–3.8

3

7

19

kapa

1,125

718

945

765

1,130

915–1,336

1.8

1.00

1

1

2

1.5–2.0

3.6

1.37

4

3

5

3.2–4.0

2

4

58

češalj

990

557

866

773

1,010

826–1,153

1.9

1.07

1.5

1

3

1.6–2.2

4.2

1.29

5

4

5

3.8–4.5

2

5

10

ograda

980

279

918

822

1,059

898–1,062

1.8

0.92

1.5

1

2

1.5–2.0

3.3

1.23

3

3

4

2.9–3.6

3

5

56

grožđe

1,167

476

1,039

903

1,243

1,027–1,307

2.4

1.29

2

1

3

2.0–2.8

3.6

1.14

4

3

4

3.3–4.0

2

6

23

gitara

938

231

932

733

1,063

870–1,005

2.4

1.24

2

1

3

2.0–2.7

3.5

1.29

4

3

4

3.2–3.9

3

6

23

olovka

1,067

910

880

750

1,035

800–1,334

1.6

0.88

1

1

2

1.3–1.9

4.3

0.94

5

4

5

4.2–4.7

3

6

97

telefon

887

179

856

780

974

834–939

2.5

1.34

3

1

3

2.1–2.9

4.6

0.87

5

5

5

4.4–4.9

3

7

47

kamion

1,431

1,021

1,119

879

1,467

1,131–1,731

2.2

1.11

2

1

3

1.9–2.5

3.5

1.32

4

3

5

3.1–3.9

3

6

18

kornjača

1,213

1,127

906

758

1,176

882–1,544

2.2

1.04

2

1

3

1.9–2.5

2.8

1.29

3

2

4

2.5–3.3

3

7

11

kišobran

987

613

814

708

987

807–1,167

2.2

1.11

2

1

3

1.8–2.5

3.9

1.22

4

3

5

3.6–4.3

3

8

20

vaga

1,058

387

972

844

1,085

944–1,171

2.1

1.13

2

1

3

1.8–2.5

3.0

1.25

3

2

4

2.6–3.4

2

4

16

sir

1,064

239

1,005

906

1,183

994–1,134

1.8

0.93

1.5

1

3

1.5–2.1

4.2

1.13

5

4

5

3.9–4.5

1

3

15

piramida

1,133

698

957

810

1,197

928–1,338

1.7

0.80

2

1

2

1.5–2.0

2.5

1.41

2

1

4

2.2–3.0

4

8

13

*NS = number of syllables; LW = length of word; AF = absolute frequency of word usage; M = mean; SD = standard deviation; MD = median; interquartiles (Q1, Q3); 95 % CI = 95 % confidence interval.

After awarding each VPO its modal name, we found that four modal names (skale “ladder,” kokoš “chicken,” miš “mouse,” and pismo “letter”) appeared were modal for two different VPOs (Supplementary Table 1A). We also found that eight of the modal names belonged to the local dialect (Supplementary Table 1B).

Descriptive statistics for all of the VPOs are shown in Table 2, including the following variables: H index, name agreement, naming latency, familiarity, visual complexity, and average word length and number of syllables for the 346 modal names, as well as their absolute and relative (per million words) frequencies of usage in the Croatian language.
Table 2

Descriptive statistics for all VPOs, included the following variables: H index, percentage of subjects who used the modal name, naming latency, familiarity, visual complexity, word length, number of syllables, and relative and absolute frequency

Variables

Mean

SD

Median

Min

Max

Q1

Q3

95 % CI

H index

.20

.18

.14

.00

.53

.03

.36

.18–.22

Percent name agreement (%)

82

20

90

22

100

70

98

80–84

Naming latency (ms)

1,146

265

1,092

740

2,460

943

1,093

1,113–1,255

Familiarity

3.29

0.80

3.20

1.62

4.96

2.64

3.91

3.20–3.37

Visual complexity

2.26

0.50

2.26

1.12

3.54

1.90

2.60

2.21–2.32

No. syllables

2

1

2

1

5

2

3

2.2–2.4

Length of word

6

2

5

3

10

5

7

5.5–5.8

Absolute frequency

75

172

21

1

1,599

8

59

56.63–93.24

Relative frequency

.008

.017

.002

<.001

.160

.001

.006

.006–.009

SD = Standard deviation; interquartiles (Q1, Q3); 95 % CI = 95 % confidence interval

Table 2 shows that the H index is low (.20) and positively oriented, indicating a very high percentage of name agreement (82 %). Indexes from the individual-item analysis, including the measure of item difficulty and the index of item discrimination based on the item–test correlations, are included in Supplementary Table 1C.

Table 3 shows the correlations between the different independent variables and naming latency, as well as their intercorrelations. All independent variables correlated with naming latency. The variables that had the highest correlation with naming latency were the H index and the percentage of agreement (strong correlations), followed by visual complexity (moderate), absolute frequency and familiarity (weak), and word length and number of syllables (very weak).
Table 3

Correlation matrix of all variables included in the study

Variables

Spearman's Rho Coefficient (95 % CI)

AF

LW

NS

VC

FA

%

H

Absolute frequency (AF)

      

Length of word (LW)

–.344* (−.436, –.260)

     

No. syllables (NS)

–.317* (−.413, –.226)

.793* (.741, .839)

    

Visual complexity (VC)

–.165* (−.284, –.066)

.131* (.020, .229)

.086 (−.022, .186)

   

Familiarity (FA)

.329* (.227, .432)

–.111* (−.222,–.003)

–.019 (−.132, .087)

–.413* (−.497, –.328)

  

Percent name agreement (%)

.231* (.125, .325)

–.116* (−.218, –.013)

–.082 (−.188, .023)

–.101 (−.205; .009)

.118* (.007, .231)

 

H index (H)

–.230* (−.324, –.125)

.116* (.013, .218)

.084 (−.021, .190)

.094 (−.015, .200)

–.112 (−.225, .001)

–.998* (−.999, –.996)

Naming latency

–.288* (−.386, –.178)

.153* (.040, .260)

.106* (.003, .203)

.342* (.203, .395)

–.252* (−.348, –.141)

–.710* (−.759, –.646)

.710* (.647, .759)

*Significant at .05 level.

With regard to intercorrelations of the independent variables, as expected, the measures of H index and percentage of name agreement highly correlated with each other. A similar relation was found between the two measures of word length: length of word and number of syllables. In addition, consistent with other normative studies using line-drawn pictures, a negative correlation between familiarity and visual complexity, with r around –.4, was observed.

In general, the relationships between phonological and lexical variables in the Croatian language did not notably deviate from those in other languages. The two measures of word length moderately correlated with the absolute frequency of word usage, indicating that in Croatian, as in other languages, longer words are less frequent. Overall, the number of syllables was shown to be a better predictor of naming latencies as, in contrast to length of word, it correlated only with naming latency and absolute frequency. Absolute frequency correlated moderately and positively with familiarity, and weakly and negatively with visual complexity, while modal name agreement and the H value were also both weakly correlated with absolute frequency.

The significant correlations among the other measures collected in the study were all relatively small in magnitude (absolute value of r from .11 to .13). Generally, the correlational pattern among the present data is very similar to the ones observed for normative data sets based on line-drawn pictures that were developed for other languages (Alario & Ferrand, 1999; Morrison et al., 1992).

In order to directly compare our data with previously published norms, we correlated data provided by Székely et al. (2003) with the data obtained in the present study for the same variables (using 123 drawings that we could visually identify as being common to both studies; see Table 4). Fairly high and significant correlations were observed for all of the variables, and the highest correlation was observed with naming latency. Strong correlations were also determined for visual complexity and absolute frequency, while a moderate correlation was found for name agreement. The weakest correlation, as anticipated, was for the number of syllables.
Table 4

Cross-language correlation matrix of variables collected by Székely et al. (2003) and in the present study

  

Spearman's Rho Coefficient (95 % CI), English Sample

NL

H

VC

NS

LW

AF

Croatian Sample

Naming latency (NL)

.647* (.530, .759)

     

H index (H)

 

.482* (.319, .613)

    

Visual complexity (VC)

  

.508* (.329, .648)

   

No. syllables (NS)

   

.395* (.210, .554)

  

Length of word (LW)

    

.464* (.250, .630)

 

Absolute frequency (AF)

     

.592* (.420, .724)

*Significant at .05 level.

In a further analysis, we compared the naming latencies between modal and nonmodal responses (Table 5). Naming latencies were significantly shorter for the modal response than for nonmodal responses: median = 916 ms (95 % CI 875–942) versus 1,164 ms (95 % CI 1,115–1,200). In addition, median naming latencies in the different modal categories decreased with increasing category ranks (Table 6). In Tables 7, 8 and 9, the modal names are classified into categories according to the percentages of subjects who named each VPO by the same object name. Translations of modal responses from Croatian to English for these categories can be found in Supplementary Table 2. Original pictures can be found in Supplementary Fig. 2 (a,b,c).
Table 5

Descriptive statistics of naming latencies for modal and nonmodal responses

Variables

Median

Min

Max

Q1

Q3

95 % CI*

Nonmodal

1,164

740

12,067

1,014

1,326

1,115–1,200

Modal

916

772

1,296

855

975

875–942

Naming latencies are measured in milliseconds (ms); *95 % CI for median.

Table 6

Naming latency descriptive statistics for modal responses classified into four categories, according to the percentages of subjects who named each VPO by the same object name

Categories

Median

Min

Max

Q1

Q3

95 % CI*

0 %–50 %

1,400

1,016

12,067

1,261

1,676

1,308–1,635

51 %–89 %

1,227

742

1,926

1,098

1,352

1,186–1,254

90 %–99 %

1,019

740

2,460

903

1,122

974–1,047

100 %

916

772

1,296

854

975

874–941

Naming latencies are measured in milliseconds (ms); *95 % CI for median.

Table 7

Statistics for modal names classified into the 0 %–50 % category, according to the percentage of subjects who named each VPO by the same object name

CN

Intended Name

Modal Name

Freq.

%

CN

Intended Name

Modal Name

Freq.

%

10

vilica

pinjur

18

36 %

275

magarac

magarac

25

50 %

15

vodopad

vodopad

25

50 %

287

gorila

majmun

24

48 %

34

bubanj

bubnjevi

25

50 %

298

jastog

jastog

21

42 %

118

zvrk

zvrk

12

24 %

303

vijak

matica

13

26 %

125

mlada

mlada

20

40 %

305

kist

kist

25

50 %

140

kvadrat

kvadrat

24

48 %

311

pijetao

kokoš

20

40 %

145

bočica

bočica

25

50 %

330

paprat

paprat

20

40 %

146

ruksak

ruksak

23

46 %

336

guma

guma

24

48 %

162

keks

keks

18

36 %

337

kavez

kavez

22

44 %

167

pelena

pelene

16

32 %

341

lijevak

lijevak

24

48 %

180

hidrant

hidrant

21

42 %

344

propeler

propeler

23

46 %

183

čvor

konop

23

46 %

347

saksofon

saksofon

23

46 %

184

ljestve

skale

24

48 %

350

totem

totem

16

32 %

197

papir

papir

19

38 %

351

puran

puran

15

30 %

199

grašak

grašak

11

22 %

     

201

tanjur

tanjur

24

48 %

     

208

britvica

žilet

21

42 %

     

215

tepih

tepih

24

48 %

     

216

jedrilica

jedrilica

25

50 %

     

226

štednjak

štednjak

19

38 %

     

236

tronožac

stalak

13

26 %

     

245

pšenica

pšenica

17

34 %

     

263

dugme

dugme

24

48 %

     

266

gusjenica

gusjenica

24

48 %

     

270

štipaljka

štipalica

23

46 %

     

CN = corresponding picture number, Freq. = frequency (number of subjects who gave the modal name), % = percentage of subjects who gave the modal name

Table 8

Statistics for modal names classified into the 51 %–89 % category, according to the percentage of subjects who named each VPO by the same object name

CN

Intended Name

Modal Name

Freq.

%

CN

Intended Name

Modal Name

Freq.

%

2

toplomjer

toplomjer

38

76 %

135

harfa

harfa

37

74 %

4

svinja

svinja

33

66 %

139

krug

krug

43

86 %

6

ruka

ruka

40

80 %

142

iks

iks

43

86 %

7

ručnik

ručnik

37

74 %

144

strijela

strijela

38

76 %

11

biljka

cvijeće

30

60 %

148

ptica

ptica

39

78 %

12

garaža

garaža

39

78 %

149

daska

daska

37

74 %

13

konzerva

konzerva

41

82 %

151

bomba

bomba

44

88 %

18

stopalo

stopalo

29

58 %

157

dvorac

dvorac

44

88 %

22

kocke

kocke

32

64 %

159

prsa

prsa

37

74 %

25

prsluk

prsluk

32

64 %

160

grad

grad

43

86 %

27

balon

balon

29

58 %

163

rak

rak

42

84 %

29

kamin

kamin

31

62 %

164

kocka

kocka

41

82 %

31

ventilator

ventilator

44

88 %

165

zubar

zubar

42

84 %

33

lampa

lampa

27

54 %

166

pustinja

pustinja

37

74 %

35

lubanja

lubanja

35

70 %

168

haljina

haljina

43

86 %

37

eskim

eskim

41

82 %

169

bušilica

bušilica

43

86 %

39

pegla

pegla

44

88 %

173

pod

pločice

26

52 %

45

mikroskop

mikroskop

44

88 %

174

smeće

smeće

37

74 %

46

zdjela

zdjela

30

60 %

176

globus

globus

40

80 %

47

kombi

kombi

28

56 %

177

četka

četka

44

88 %

51

lopta

lopta

32

64 %

179

kopito

kopito

44

88 %

60

jarac

koza

29

58 %

182

jakna

jaketa

32

64 %

66

remen

kaiš

36

72 %

185

bubamara

bubamara

26

52 %

67

prikolica

prikolica

26

52 %

189

mikrofon

mikrofon

43

86 %

68

redalo

ravnalo

28

56 %

190

ogledalo

ogledalo

43

86 %

75

uže

konop

39

78 %

192

igla

igla

39

78 %

77

pismo

pismo

41

82 %

193

gnijezdo

gnijezdo

33

66 %

89

gusar

gusar

41

82 %

194

paket

paket

40

80 %

91

tikva

bundeva

28

56 %

195

vjedro

kanta

29

58 %

94

dijete

beba

35

70 %

196

hlače

hlače

37

74 %

103

čavao

brokva

28

56 %

198

papiga

papiga

43

86 %

104

dalekozor

dalekozor

36

72 %

203

lonac

lonac

26

52 %

105

vaza

vaza

40

80 %

204

dar

poklon

39

78 %

107

klupa

klupa

44

88 %

214

ruža

ruža

43

86 %

108

zebra

zebra

43

86 %

218

košulja

košulja

32

64 %

109

čovjek

čovjek

34

68 %

223

vojnik

vojnik

39

78 %

110

tuljan

tuljan

38

76 %

224

stepenište

skale

26

52 %

111

perika

perika

43

86 %

225

kip

kip

36

72 %

112

ogrlica

ogrlica

35

70 %

227

podmornica

podmornica

43

86 %

116

jastuk

jastuk

42

84 %

230

suza

suza

41

82 %

124

klavir

klavir

42

84 %

231

zubalo

zubalo

27

54 %

232

teleskop

teleskop

29

58 %

282

lisica

lisica

34

68 %

233

palac

palac

35

70 %

286

koza

koza

40

80 %

237

trofej

pehar

33

66 %

294

noga

noga

44

88 %

240

violina

violina

39

78 %

296

salata

kupus

29

58 %

242

opeka

zid

43

86 %

299

lokot

katanac

35

70 %

243

novčanik

novčanik

42

84 %

304

naranča

naranča

36

72 %

244

orah

orah

42

84 %

308

kliješta

kliješta

42

84 %

246

mlin

vjetrenjača

44

88 %

310

frižider

frižider

38

76 %

248

vuk

vuk

34

68 %

312

škare

škare

40

80 %

249

žena

žena

31

62 %

313

ovca

ovca

33

66 %

250

crv

glista

29

58 %

314

suknja

suknja

36

72 %

254

mrav

mrav

35

70 %

318

reket

reket

43

86 %

255

strelica

strelica

42

84 %

324

bunar

bunar

41

82 %

256

pepeljara

pepeljara

41

82 %

325

kolo

kotač

26

52 %

257

sjekira

sjekira

44

88 %

326

žir

žir

40

80 %

259

medvjed

medvjed

42

84 %

329

slavina

špina

26

52 %

260

pčela

pčela

27

54 %

331

udica

udica

42

84 %

261

bicikl

bicikla

30

60 %

332

slušalica

slušalica

43

86 %

264

svijeća

svijeća

44

88 %

333

padobran

padobran

27

54 %

267

trešnja

trešnja

35

70 %

340

rep

rep

34

68 %

269

cigareta

cigareta

28

56 %

342

janje

janje

26

52 %

271

kaput

kaput

29

58 %

343

palma

palma

44

88 %

273

šalica

šalica

30

50 %

346

štakor

miš

34

68 %

276

orao

orao

35

70 %

349

termometar

termometar

27

54 %

277

koverta

pismo

38

76 %

352

bič

bič

34

68 %

279

kažiprst

prst

34

68 %

     

280

zastava

zastava

39

78 %

     

281

muha

muha

37

74 %

     

CN = corresponding picture number, Freq. = frequency (number of subjects who gave the modal name), % = percentage of subjects who gave the modal name

Table 9

Statistics for modal names classified into the 90 %–99 % category, according to the percentage of subjects who named each VPO by the same object name

CN

Intended Name

Modal Name

Freq.

%

CN

Intended Name

Modal Name

Freq.

%

3

kruh

kruh

49

98 %

122

šešir

šešir

46

92 %

5

jabuka

jabuka

48

96 %

123

kruna

kruna

49

98 %

9

čaša

čaša

48

96 %

127

srce

srce

49

98 %

16

kralj

kralj

49

98 %

128

klaun

klaun

49

98 %

17

čizma

čizma

47

94 %

129

vulkan

vulkan

49

98 %

19

stolica

stolica

49

98 %

131

hobotnica

hobotnica

49

98 %

26

grablje

grablje

48

96 %

132

pila

pila

48

96 %

28

patka

patka

49

98 %

133

fotoaparat

fotoaparat

45

90 %

32

prozor

prozor

49

98 %

134

krevet

krevet

49

98 %

36

most

most

49

98 %

138

majmun

majmun

49

98 %

41

mrkva

mrkva

48

96 %

141

trokut

trokut

49

98 %

42

mornar

mornar

47

94 %

147

kada

kada

46

92 %

44

vjeverica

vjeverica

49

98 %

150

brod

brod

49

98 %

48

helikopter

helikopter

48

96 %

152

kutija

kutija

47

94 %

50

šal

šal

49

98 %

153

dječak

dječak

49

98 %

53

krava

krava

46

92 %

155

štap

štap

47

94 %

54

brada

brada

49

98 %

156

auto

auto

46

92 %

57

lula

lula

49

98 %

158

lanac

lanac

49

98 %

62

top

top

46

92 %

161

stup

stup

47

94 %

63

cipela

cipela

45

90 %

171

pero

pero

48

96 %

65

pauk

pauk

49

98 %

172

vatra

vatra

48

96 %

70

kraljica

kraljica

46

92 %

175

djevojčica

djevojčica

49

98 %

71

crkva

crkva

49

98 %

186

kosilica

kosilica

47

94 %

73

zviždaljka

zviždaljka

46

92 %

187

svjetionik

svjetionik

45

90 %

74

kukuruz

kukuruz

49

98 %

188

magnet

magnet

46

92 %

76

košara

košara

45

90 %

191

miš

miš

49

98 %

79

dimnjak

dimnjak

48

96 %

200

pingvin

pingvin

48

96 %

82

vatrogasac

vatrogasac

49

98 %

205

torbica

torba

45

90 %

84

kost

kost

49

98 %

206

kiša

kiša

47

94 %

86

kuhinja

kuhinja

46

92 %

210

cesta

cesta

48

96 %

87

sedlo

sedlo

48

96 %

212

raketa

raketa

49

98 %

92

čarapa

čarapa

48

96 %

217

školjka

školjka

49

98 %

95

šišmiš

šišmiš

49

98 %

220

kostur

kostur

49

98

96

list

list

48

96 %

221

tobogan

tobogan

47

94 %

97

planina

planina

46

92 %

222

praćka

praćka

48

96 %

99

brk

brkovi

46

92 %

228

ljuljačka

ljuljačka

49

98 %

100

uho

uho

47

94 %

229

tenk

tenk

49

98 %

102

kuća

kuća

48

96 %

235

tigar

tigar

48

96 %

113

zvono

zvono

47

94 %

238

pinceta

pinceta

47

94 %

114

zvijezda

zvijezda

49

98 %

239

usisavač

usisavač

45

90 %

117

žlica

žlica

46

92 %

247

vještica

vještica

47

94 %

119

cvijet

cvijet

46

92 %

251

harmonika

harmonika

48

96 %

120

odijelo

odijelo

47

94 %

252

avion

avion

48

96 %

253

krokodil

krokodil

48

96 %

302

gljiva

gljiva

49

98 %

258

bačva

bačva

49

98 %

307

paprika

paprika

46

92 %

262

autobus

autobus

48

96 %

309

zec

zec

47

94 %

268

koka

kokoš

47

94 %

315

jagoda

jagoda

47

94 %

274

jelen

jelen

47

94 %

316

labud

labud

46

92 %

283

tava

tava

48

96 %

319

vlak

vlak

45

90 %

284

žirafa

žirafa

47

94 %

320

stablo

stablo

45

90 %

285

naočale

naočale

45

90 %

327

dupin

dupin

46

92 %

289

skakavac

skakavac

45

90 %

328

lepeza

lepeza

47

94 %

291

pištolj

pištolj

48

96 %

334

slika

slika

49

98 %

292

vješalica

vješalica

49

98 %

338

mozak

mozak

49

98 %

293

klokan

klokan

49

98 %

348

škorpion

škorpion

46

92 %

295

limun

limun

48

96 %

     

297

žarulja

žarulja

45

90 %

     

300

mjesec

mjesec

46

92 %

     

CN = corresponding picture number, Freq. = frequency (number of subjects who gave the modal name), % = percentage of subjects who gave the modal name

Naming latencies were faster when all subjects gave the identical modal name to a specific VPO (category with 100 % of agreement), while the longest naming latency (1,400 ms) was observed for the modal 0 %–50 % category. A comparison of the median naming latencies across the modal categories revealed significant differences between all four categories (Table 6, note the nonoverlapping 95 % CIs for the medians).

Finally, we examined whether a change in the variables’ correlation patterns could be attributed to the category of modal responses (Table 10). As opposed to the strong correlations of naming latency with H index and percentage agreement reported in Table 3, after data were stratified by categories of modal responses, we observed moderate and comparable correlations in the 51 %–89 % and 90 %–99 % categories. These associations, however, vanished in the 0 %–50 % category. Overall, in the 90 %–99 % category, all variables correlated with naming latency, whereas no association was determined in the 0 %–50 % and 100 % categories. The variables that correlated with naming latency in the 51 %–89 % category were the name agreement measures, familiarity, and visual complexity.
Table 10

Correlation coefficients among all variables, tested separately for each modal category of the percentage of subjects who gave the same name to each specific VPO

Variables

Spearman's Rho Coefficient (95 % CI)

Categories

AF

LW

NS

VC

FA

%

H

Length of word (LW)

100 %

–.513* (−.669, –.311)

      

90 %–99 %

–.369* (−.523, –.209)

      

51 %–89 %

–.281* (−.438, –.101)

      

0 %–50 %

.006 (−.311, .363)

      

No. syllables (NS)

100 %

–.461* (−.630, –.259)

.836* (.741, .899)

     

90 %–99 %

–.358* (−.498, –.213)

.806* (.713, .873)

     

51 %–89 %

–.335* (−.475, –.161)

.770* (.662, .854)

     

0 %–50 %

.208 (−.125, .538)

.762* (.576, .891)

     

Visual complexity (VC)

100 %

–.333* (−.560, –.095)

.09 (−.176, .371)

.045 (−.215, .336)

    

90 %–99 %

–.138 (−.311, .060)

.237* (.049, .421)

.162 (−.037, .342)

    

51 %–89 %

–.094 (−.271, .083)

.012 (−.153, .176)

.038 (−.137, .201)

    

0 %–50 %

–.172 (−.456, .172)

.016 (−.277, .329)

–.07 (−.369,.260)

    

Familiarity (FA)

100 %

.484* (.207, .683)

–.123 (−.392, .155)

–.151 (−.419, .125)

–.346* (−.561, –.107)

   

90 %–99 %

.422*** (.249, .578)

–.11 (−.281, .074)

.074 (−.098, .242)

–.431*** (−.574, –.266)

   

51 %–89 %

.211* (.039, .380)

–.048 (−.218, .123)

–.012 (−.169, .162)

–.367* (−.518, –.196)

   

0 %–50 %

.07 (−.284, .390)

–.191 (−.486, .136)

–.108 (−.398, .197)

–.471* (−.708, –.166)

   

Percentage agreement (%)

100 %

n.a.

n.a.

n.a.

n.a.

n.a.

  

90 %–99 %

.137 (−.046, .297)

–.151 (−.341, .049)

–.258* (−.452, –.066)

.045 (−.145, .223)

–.086 (−.276, .104)

  

51 %–89 %

.007 (−.180, .175)

.011 (−.170, .192)

–.001 (−.193, .190)

.054 (−.108, .219)

–.063 (−.232, .106)

  

0 %–50 %

.334* (.033, .609)

.284 (−.011, .559)

.307 (−.033,.637)

–.33 (−.631, .007)

.163 (−.173, .461)

  

H index (H)

100 %

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

 

90 %–99 %

–.141 (−.298, .044)

.16 (−.032, .352)

.266* (.078, .458)

–.058 (−.240, .134)

.095 (−.097, .283)

–.997* (−1.000, –.990)

 

51 %–89 %

.007 (−.167, .200)

–.026 (−.206, .162)

–.01 (−.199, .176)

–.055 (−.219, .105)

.056 (−.120, .227)

–.994* (−1.000, –.979)

 

0 %–50 %

%–.251 (−.578, .104)

–.195 (−.503, .130)

–.217 (−.548,.143)

.192 (−.149, .510)

.135 (−.195, .419)

–.511* (−.872, –.103)

 

Naming latency

100 %

–.157 (−.410, .109)

.089 (−.161, .320)

.05 (−.190, .273)

.194 (−.055, .424)

–.117 (−.361, .129)

n.a.

n.a.

90 %–99 %

–.341* (−.495, –.160)

.302* (.109, .482)

.193* (.014, .369)

.350* (.182, .507)

–.229* (−.402,–.046)

–.348* (−.520, –.180)

.352* (.183, .520)

51 %–89 %

–.031 (−.204, .159)

–.099 (−.282, .082)

–.019 (−.193, .147)

.341* (.189, .487)

–.235* (−.388,–.055)

–.341* (−.501, –.175)

.350** (.179, .507)

0 %–50 %

–.192 (−.483, .136)

–.06 (−.365, .262)

–.176 (−.462,.136)

.324 (−.019, .619)

–.257 (−.570, .108)

–.152 (−.465, .202)

–.066 (−.378, .263)

Naming latency for modal names classified into four categories, according to the percentage of subjects who gave the same name to each specific VPO: 0 %–50 %, 51 %–89 %, 90 %–99 %, or 100 %. AF = absolute frequency; n.a. = not applicable, as one of the variables is constant. *Significant at the .05 level.

In addition to the obvious correlation between the two measures of word length, the most stable relationship that was not influenced by the category of the modal response was the correlation between familiarity and visual complexity (r from –.346 to –.471).

We also observed a decrease in the magnitude of correlations with a decrease in modal category rank (i.e., the correlation coefficient for the 100 % category was highest) for the correlations of absolute word frequency with length of word, number of syllables, visual complexity, and familiarity. Furthermore, associations that completely vanished after the data stratification were the associations of the agreement measures with word frequency and length of word, as well as the associations of word length with familiarity and visual complexity. Similar to the correlations for naming latency, some of the correlations remained significant only in the 90 %–99 % modal category—that is, the correlations of length of word and visual complexity, as well as that of the name agreement measures and number of syllables.

With regard to classification of misnomers (i.e., nonmodal names), we observed 211 synonyms all together (150 synonyms and 61 synonyms in dialects); 14 dialectal forms based on semantic mistakes, and 4 others based on coordinate and semantic mistakes; 138 failures; 259 coordinate and semantic errors (178 semantic and 81 coordinate); 55 components; 37 superordinates; and 20 subordinates.

Furthermore, seven of the modal names given to VPOs differed semantically from the intended name: those for Picture 60 (intended jarac, modal koza); Picture 173 (intended pod, modal pločice); Picture 183 (intended čvor, modal konop); Picture 242 (intended opeka, modal zid); Picture 287 (intended gorilla, modal majmun); Picture 346 (intended štakor, modal miš); and Picture 311 (intended pijetao, modal kokoš).

Discussion

In the present study, we obtained normative data for 346 VPOs from young and healthy Croatian native speakers.

We believe that these normative data will be of significance to researchers investigating various theoretical issues in which VPO is an important stimulus material. Each of the measured variables plays a very important role in the performance of various cognitive tasks (Cycowicz et al., 1997). A question that often arises is which of these variables have a direct impact on the performance of certain tasks. Before we can answer this question, it is important to know the relations between the variables.

The mean naming latency ± SD of 1,146 ± 265 ms that we observed in this study is comparable to latencies obtained in other languages, which have ranged from 1,019 to 1,217 ms (Bates et al., 2003). In addition, the correlation pattern of the variables that were included in the study was consistent with the patterns obtained with English (Morrison et al., 1992) and French (Alario & Ferrand, 1999) samples. The cross-language comparison of the VPOs common between our and Székely et al.’s (2003) data sets revealed that the two sets of measures were consistent.

We found that naming latency correlated with all of the tested variables. Liu, Hao, Li, and Shu (2011) also showed correlations of naming latency with word length, familiarity, word frequency, visual complexity, and percentage of agreement. In contrast to our study, Liu et al. did not include measures of frequency of usage and number of syllables. Correlations between naming latency and number of syllables had been observed, however, by Cuetos et al. (1999), Dell’Acqua et al. (2000), and Severens, Van Lommel, Ratinckx, and Hartsuiker (2005). Unlike in our study, Dell’Acqua et al. did not find a significant correlation between naming latency and word length.

Naming latencies were longer for visually more complex VPOs in the overall data set, as well as in the intermediate modal response categories. Previous studies have shown that more complex stimuli require longer processing time, and thus elicit longer naming latencies (Alario et al., 2004). The absence of associations of naming latency with either visual complexity or familiarity in the 100 % modal response class is interesting: It indicates that in this category, with minimal visual complexity and maximal familiarity, a threshold in naming latencies was reached, and therefore a ceiling effect was observed.

A correlation that is consistently observed in normative studies is a negative correlation between familiarity and visual complexity of a size around .4 (Brodeur et al., 2010). This correlation was also the most stable one across different modal categories in our study. Why familiar items are perceived as being less visually complex is not completely known, but an explanation may lie in the fact that each concept has different characteristics with regard to the number of lines and details used to display a particular VPO, the way that the picture is drawn, and so forth (Berman et al., 1989).

As there are no published data on the frequency of words in speech in the Croatian language, in this study we used word frequencies collected from written sources. Even though the results showed correlations with all of the measures, after data stratification correlations only remained for four of the measures: length of word, number of syllables, familiarity, and naming latency. Future studies should take into account the frequency of words in speech, if available.

Considering the types of misnomers (i.e., nonmodal names; Snodgrass & Vanderwart, 1980) employed by our subjects, we found that most of them were coordinate and semantic errors, followed by synonyms, failures, components, superordinates and subordinates.

We hope that the normative data presented in this study will provide a guide for the selection of VPOs to be used in studies or in clinical settings. We suggest that investigators be especially careful if deciding to use VPOs, as we have found (a) four names that were modal for two different VPOs (Pictures 268 and 311, 184 and 224, 191 and 346, and 78 and 277); (b) eight modal names belonging to local dialect; (c) seven modal names given to VPOs that differed semantically from the intended name (for Pictures 60, 173, 183, 242, 287, 346, and 311), and (d) many modal names that belonged to the 0 %–50 % agreement category.

Conclusion

The present study has provided normative data for the Croatian language for 346 visually presented objects. The pictures were standardized in terms of seven variables: naming latency, name agreement, familiarity, visual complexity, length of the word, number of syllables, and frequency of word. These normative data for pictorial stimuli will be very useful in studies of perception, language, and memory, as well as during preoperative and intraoperative mapping of speech-related cortical areas.

Notes

Author note

We thank the third-year undergraduate students in physiotherapy (academic year 2010–2011), School of Medicine, University of Split, for participating in the study as subjects. The contributions of each author were as follows: M.R. and A.J. contributed equally to the creation of this study. M.R. created the general conception, idea, and design of the study, as well as contributing to the analysis and writing of the manuscript. A.J. contributed to the design of the study, the data analysis, writing of the manuscript, and critical review of the manuscript draft. M.R. and M.B. performed testing of the subjects and a literature search. M.B. also contributed to the writing of the manuscript, and A.S. contributed to the design of the study, providing valuable corrections regarding Croatian language rules. D.H. contributed to the statistical analysis and critical review of the manuscript draft. The authors did not have financial support for this study and declare that no conflicts exist between authors.

Supplementary material

13428_2012_308_MOESM1_ESM.docx (631 kb)
Supplement 1 (DOCX 630 KB)
13428_2012_308_MOESM2_ESM.zip (4.1 mb)
Supplement 2 Figure 2. Original pictures classified into four categories according to the percentage of subjects who named a VPO by the same object name: a) 0%–50%, b) 51%–89%, c) 90%–99%, d) 100% (ZIP 4.07 MB)

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Copyright information

© Psychonomic Society, Inc. 2013

Authors and Affiliations

  • Maja Rogić
    • 1
    • 2
  • Ana Jerončić
    • 1
  • Marija Bošnjak
    • 1
  • Ana Sedlar
    • 1
  • Darko Hren
    • 1
  • Vedran Deletis
    • 1
  1. 1.University of SplitSplitCroatia
  2. 2.Laboratory for Human and Experimental Neurophysiology (LAHEN)School of Medicine, University of SplitSplitCroatia

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