Skip to main content
SpringerLink
Log in

Revised and extended norms for a picture naming test sensitive to category dissociations

  • Original Article
  • Published:
Neurological Sciences Aims and scope Submit manuscript

Abstract

This study presents revised and extended norms for a picture naming test [Laiacona et al. (Arch Neurol Psicol Psichiatr 54:209–248, 1993)], based on 80 Snodgrass and Vanderwart (J Exp Psychol Human Learn Mem 6:174–215, 1980) pictures, devised to detect a categorical dissociation in the naming of items between biological and man-made categories. This survey is based on data from 215 healthy Italian participants. Since males are more frequently reported to have a disproportionate impairment of biological categories, norms have also been separately calculated for males and females and for the two categories of man-made objects and biological entities. Besides providing new normative values based on the Equivalent Scores approach, this study reappraises the interaction between categorical dissociations and sex in the normal population, and discusses some methodological aspects concerning the use of statistical norms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Laiacona M, Barbarotto R, Trivelli C, Capitani E (1993) Dissociazioni semantiche intercategoriali: descrizione di una batteria standardizzata e dati normativi. Arch Neurol Psicol Psichiatr 54:209–248

    Google Scholar 

  2. Snodgrass JG, Vanderwart M (1980) Standardized set of 260 pictures: norms for name agreement, inage agreement, familiarity and visual complexity. J Exp Psychol Human Learn Mem 6:174–215

    Article  CAS  Google Scholar 

  3. Novelli G, Papagno C, Capitani E, Laiacona M, Vallar G, Cappa SF (1986) Tre test di produzione e ricerca lessicale. Arch Psicol Neurol Psichiatr 47:477–506

    Google Scholar 

  4. Miceli G, Laudanna A, Burani C, Capasso R (1994) Batteria per l’Analisi dei deficit Afasici. CEPSAG, Roma

    Google Scholar 

  5. Luzzatti C, Willmes K, De Bleser R (1996) Aachener Aphasie Test: versione italiana. Organizzazioni Speciali, Firenze

    Google Scholar 

  6. Ciurli P, Basso A, Marangolo P (1996) Esame del linguaggio—2: manuale. Organizzazioni Speciali, Firenze

    Google Scholar 

  7. Goodglass H, Klein B, Carey P, Jones K (1966) Specific semantic word categories in aphasia. Cortex 2:74–89

    Article  Google Scholar 

  8. Warrington EK, Shallice T (1984) Category-specific semantic impairments. Brain 107:829–854

    Article  PubMed  Google Scholar 

  9. Jones GV (1985) Deep dyslexia, imageability and ease of predication. Brain Lang 24:1–19

    Article  CAS  PubMed  Google Scholar 

  10. Barbarotto R, Laiacona M, Capitani E (2001) Living musical instruments and inanimate body parts? Neuropsychologia 39:406–414

    Article  CAS  PubMed  Google Scholar 

  11. Barbarotto R, Laiacona M, Macchi V, Capitani E (2002) Picture reality decision, semantic categories and gender. A new set of pictures, with norms and an experimental study. Neuropsychologia 40:1637–1653

    Article  PubMed  Google Scholar 

  12. Nisi M, Longoni AM, Snodgrass JG (2000) Misure italiane per l’accordo sul nome, familiarità ed età di acquisizione per le 266 figure di Snodgrass. G Ital Psicol 27:205–218

    Google Scholar 

  13. Barbarotto R, Laiacona M, Capitani E (2005) Objective versus estimated age of word acquisition: a study of 202 Italian children. Behav Res Meth 37:644–650

    Article  Google Scholar 

  14. Lotto L, Dell’Acqua R, Job R (2001) Le figure PD/DPSS. Misure di accord0 sul nome, tipicità, familiarità, età di acquisizione e tempi di denominazione per 266 figure. G Ital Psicol 28:193–207

    Google Scholar 

  15. Nickels LA, Howard D (1995) Phonological errors in aphasic naming: comprehension, monitoring and lexicality. Cortex 31:209–237

    Article  CAS  PubMed  Google Scholar 

  16. Riddoch MJ, Humphreys GW (1987) Visual object processing in optic aphasia: a case of semantic access agnosia. Cogn Neuropsychol 4:131–185

    Article  Google Scholar 

  17. Tyler LK, Moss HE (1997) Functional properties of concepts: studies of normal and brain-damaged patients. Cogn Neuropsychol 14:511–545

    Article  Google Scholar 

  18. Capitani E, Laiacona M, Barbarotto R (1993) Dissociazioni semantiche intercategoriali. Parte II: procedura automatica di analisi di una batteria standardizzata. Arch Neurol Psicol Psichiatr 54:457–476

    Google Scholar 

  19. Aitkin M, Anderson D, Francis B, Hinde J (1989) Statistical modelling in GLIM. Clarendon Press, Oxford

    Google Scholar 

  20. Laiacona M, Barbarotto R, Capitani E (1993) Perceptual and associative knowledge in category specific impairment of semantic memory: a study of two cases. Cortex 29:727–740

    Article  CAS  PubMed  Google Scholar 

  21. Laiacona M, Capitani E (2001) A case of prevailing deficit of non-living categories or a case of prevailing sparing of living categories? Cogn Neuropsychol 18:39–70

    Article  CAS  PubMed  Google Scholar 

  22. Capitani E, Chieppa F, Laiacona M (2010) Associated impairment of the categories of conspecifics and biological entities. Cognitive and neuroanatomical aspects of a new case. Cogn Neuropsychol 27(3):207–229

    Article  PubMed  Google Scholar 

  23. Laiacona M, Barbarotto R, Capitani E (2006) Human evolution and the brain representation of semantic knowledge. Is there a role for sex differences? Evol Hum Behav 27:158–168

    Article  Google Scholar 

  24. Capitani E (1997) Normative values and neuropsychological assessment. Common problems in clinical practice and research. Neuropsychol Rehabil 7:295–309

    Article  Google Scholar 

  25. Capitani E, Laiacona M, Italian Group for the Neuropsychological Study of Ageing (1997) Composite neuropsychological batteries and demographic correction: standardization based on equivalent scores, with a review of published data. J Clin Exp Neuropsychol 19:795–809

    Article  CAS  PubMed  Google Scholar 

  26. Capitani E, Laiacona M (1988) Aging and psychometric diagnosis of intellective impairment: some considerations on test scores and their use. Dev Neuropsychol 4:325–330

    Article  Google Scholar 

  27. Bortolini U, Tagliavini C, Zampolli A (1972) Lessico di frequenza della lingua italiana contemporanea. Garzanti, Milano

    Google Scholar 

  28. Bertinetto PM, Burani C, Laudanna A, Marconi L, Ratti D, Rolando C, Thornton AM (2005) Corpus e lessico di frequenza dell’ italiano scritto (CoLFIS). http://linguistica.sns.it/ColFIS/Home.htm. Accessed 4 Feb 2016

  29. Barbarotto R, Capitani E, Laiacona M (2008) Does sex influence the age of acquisition of common names? A contrast of different semantic categories. Cortex 44:1161–1170

    Article  PubMed  Google Scholar 

  30. Capitani E, Laiacona M, Pagani R, Capasso R, Zampetti P, Miceli G (2009) Posterior cerebral artery infarcts and semantic category dissociations. A study of 28 patients. Brain 132:965–981

    Article  PubMed  Google Scholar 

  31. Gainotti G (2010) The influence of anatomical locus of lesion and of gender-related familiarity factors in category-specific semantic disorders for animals, fruits and vegetables: a review of single-case studies. Cortex 46:1072–1087

    Article  PubMed  Google Scholar 

  32. Capitani E, Laiacona M (2011) Facts and hypotheses relevant for contrasting animal and plant life semantics. A comment to Gainotti. Cortex 47:259–264

    Article  PubMed  Google Scholar 

  33. Capitani E, Laiacona M, Barbarotto R (1999) Gender affects retrieval of certain categories in semantic fluency tasks. Cortex 35:273–278

    Article  CAS  PubMed  Google Scholar 

  34. Shallice T (1988) From neuropsychology to mental structure. Cambridge University Press, Cambridge

    Book  Google Scholar 

Download references

Acknowledgments

We are grateful to Dr. Rosemary Allpress for her revision of the English text.

Author information

Authors and Affiliations

  1. Neurology Unit, Veruno Scientific Institute IRRCS, Salvatore Maugeri Foundation, Veruno, Italy

    Marcella Laiacona

  2. Neuropsychology Unit, Centro S. Ambrogio, Fatebenefratelli, Cernusco sul Naviglio, Milan, Italy

    Riccardo Barbarotto

  3. Neurology Unit, ASST-Rhodense, Azienda Ospedaliera “G. Salvini”, Garbagnate Milanese, Milan, Italy

    Elena Baratelli

  4. Health Sciences Department, Milan University, via Di Rudinì 8, 20142, Milan, Italy

    Erminio Capitani

Authors
  1. Marcella Laiacona
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Riccardo Barbarotto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elena Baratelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Erminio Capitani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erminio Capitani.

Ethics declarations

Conflict of interest

No conflict of interest is declared.

Appendices

Appendix 1

Snodgrass and Vandervart stimulus

Names accepted, with their occurrence

Names rejected (only when observed in 10 subjects or more)

Number and Percentage of accepted responses (overall, males, females)

Nail

Chiodo (204)

Vite (10)

204 (94.9 %), M = 96 (96.0 %), F = 108 (93.9 %)

Lamp

Lampada (157), Abat-jour (52), Paralume (3), Lume (1)

–

213 (99.1 %), M = 99 (99.0 %), F = 114 (99.1 %)

Hammer

Martello (211)

–

211 (98.1 %), M = 100 (100 %), F = 111 (96.5 %)

Caterpillar

Bruco (135), Millepiedi (26), Verme (12), Centopiedi o Centogambe (4), Baco (3), Gatta o Gatta pelosa (3)

Lombrico (20)

183 (85.1 %), M = 89 (89.0 %), F = 94 (81.7 %)

Trumpet

Tromba (203), Trombetta (10), Cornetta (1)

–

214 (99.5 %), M = 99 (99.0 %), F = 115 (100 %)

Hair

Capelli (102), Testa (27), Capigliatura (10), Parrucca (5), Acconciatura (1)

Casco (14), Conchiglia (11)

145 (67.4 %), M = 63 (63.0 %), F = 82 (71.3 %)

Rooster

Gallo (201), Galletto (1)

Gallina (13)

202 (93.9 %), M = 95 (95.0 %), F = 107 (93.0 %)

Harp

Arpa (196), Cetra (5), Lira (3)

–

204 (94.9 %), M = 97 (97.0 %), F = 107 (93.0 %)

Swan

Cigno (167)

Oca (38)

167 (77.7 %), M = 81 (81.0 %), F = 86 (74.8 %)

Saw

Sega (210), Seghetto (3), Gattuccio (1)

–

214 (99.5 %), M = 100 (100 %), F = 114 (99.1 %)

Pineapple

Ananas (208)

–

208 (96.7 %), M = 96 (96.0 %), F = 112 (97.4 %)

Drum

Tamburo (210), Tamburello (2)

–

212 (98.6 %), M = 100 (100 %), F = 112 (97.4 %)

Vase

Vaso (210), Anfora (3), Orcio (1)

–

214 (99.5 %), M = 99 (99.0 %), F = 115 (100 %)

Lemon

Limone (213)

–

213 (99.1 %), M = 98 (98.0 %), F = 115 (100 %)

Violin

Violino (178)

Chitarra (32)

178 (82.8 %), M = 88 (88.0 %), F = 90 (78.3 %)

Tomato

Pomodoro (195)

Mela (16)

195 (90.7 %), M = 90 (90.0 %), F = 105 (91.3 %)

Leg

Gamba (206), Arto inferiore (1), arto dal ginocchio al piede (1)

 

208 (96.7 %), M = 97 (97.0 %), F = 111 (96.5 %)

Helicopter

Elicottero (213)

–

213 (99.1 %), M = 99 (99.0 %), F = 114 (99.1 %)

French Horn

Corno (50), Tromba (105), Flicorno (5), Corno francese (2) Bombardino (2)

Trombone (34)

164 (76.3 %), M = 76 (76.0 %), F = 88 (76.5 %)

Celery

Sedano (197)

–

197 (91.6 %), M = 91 (91.0 %), F = 106 (92.2 %)

Flute

Flauto (138), Flauto Traverso (3), Piffero (29)

Clarino (13), Clarinetto (11)

170 (79.1 %), M = 82 (82.0 %), F = 88 (76.5 %)

Stool

Sgabello (200), Seggiolino (7)

–

207 (96.3 %), M = 98 (98.0 %), F = 109 (94.8 %)

Axe

Scure (92), Ascia (77), Accetta (33), Mannaia (1)

–

203 (94.4 %), M = 99 (99.0 %), F = 104 (90.4 %)

Sledge

Slitta (176), Slittino (19)

–

195 (90.7 %), M = 94 (94.0 %), F = 101 (87.8 %)

Arm

Braccio (211), Arto superiore (2), Avambraccio (1)

–

214 (99.5 %), M = 100 (100 %), F = 114 (99.1 %)

Dresser

Cassettone (75), Comò (87), Cassettiera (36), Trumeau (1), Settimanale (1)

–

200 (93.0 %), M = 92 (92.0 %), F = 108 (93.9 %)

Bus

Autobus (147), Pullman (47), Bus (14), Corriera (5)

Filobus (2)*

213 (99.1 %), M = 100 (100 %), F = 113 (98.3 %)

Ostrich

Struzzo (174)

Cigno (10)

174 (80.9 %), M = 88 (88.0 %), F = 86 (74.8 %)

Cherry

Ciliegie (196)

Mela (14)

196 (91.2 %), M = 93 (93.0 %), F = 103 (89.6 %)

Nose

Naso (206)

–

206 (95.8 %), M = 97 (97.0 %), F = 109 (94.8 %)

Train

Treno (195), Locomotiva (10), Elettrotreno (3), Littorina (2), Locomotore (1)

–

211 (98.1 %), M = 99 (99.0 %), F = 112 (97.4 %)

Bell

Campana (193), Campanella (15), Campanello (5)

–

213 (99.1 %), M = 100 (100 %), F = 113 (98.3 %)

Sailboat

Barca a vela (183), Barca (20), Vela (9)

–

212 (98.6 %), M = 99 (99.0 %), F = 113 (98.3 %)

Foot

Piede (215)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Truck

Camion (168), TIR (25), Autotreno (5), Autocarro (5), Autoarticolato (3), Camion e rimorchio (3)

–

209 (97.2 %), M = 99 (99.0 %), F = 110 (95.6 %)

Strawberry

Fragola (213)

–

213 (99.1 %), M = 99 (99.0 %), F = 114 (99.1 %)

Screwdriver

Cacciavite (205), Giravite (4)

–

209 (97.2 %), M = 100 (100 %), F = 109 (94.8 %)

Onion

Cipolla (200)

–

200 (93.0 %), M = 89 (89.0 %), F = 111 (96.5 %)

Camel

Cammello (147), Dromedario (67)

–

214 (99.5 %), M = 99 (99.0 %), F = 115 (100 %)

Orange

Arancia (131), Pompelmo (3)

Limone (17)

134 (62.3 %), M = 61 (61.0 %), F = 73 (63.5 %)

Carrot

Carota (215)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Hand

Mano (215)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Pliers

Pinza (163), Tronchese/tronchesino (4)

Tenaglia (39)

167 (77.7 %), M = 88 (88.0 %), F = 79 (68.7 %)

Pear

Pera (214)

–

214 (99.5 %), M = 100 (100 %), F = 114 (99.1 %)

Accordion

Fisarmonica (210), Organetto (3)

–

213 (99.1 %), M = 99 (99.0 %), F = 114 (99.1 %)

Car

Automobile (124), Macchina (73), Auto (15), Cadillac (1), Limousine (1)

–

214 (99.5 %), M = 100 (100 %), F = 114 (99.1 %)

Apple

Mela (214)

–

214 (99.5 %), M = 99 (99.0 %), F = 115 (100 %)

Corn

Pannocchia (133), Granoturco (42), Mais (36)

–

211 (98.1 %), M = 99 (99.0 %), F = 112 (97.4 %)

Giraffe

Giraffa (211)

–

211 (98.1 %), M = 98 (98.0 %), F = 113 (98.3 %)

Asparagus

Asparago (197)

–

197 (91.6 %), M = 93 (93.0 %), F = 104 (90.4 %)

Chair

Sedia (215)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Eye

Occhio (215)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Screw

Vite (209)

–

209 (97.2 %), M = 99 (99.0 %), F = 110 (95.6 %)

Rocking Chair

Sedia a dondolo (203)

Dondolo (10)

203 (94.4 %), M = 94 (94.0 %), F = 109 (94.8 %)

Mouse

Topo (209), Ratto (6)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Scissors

Forbice o Forbici (215)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Watermelon

Anguria (193), Cocomero (3)

–

196 (91.2 %), M = 90 (90.0 %), F = 106 (92.2 %)

Lips

Labbra (125), Bocca (89)

–

214 (99.5 %), M = 99 (99.0 %), F = 115 (100 %)

Cow

Mucca (208), Vacca (2)

–

210 (97.7 %), M = 99 (99.0 %), F = 111 (96.5 %)

Guitar

Chitarra (211)

–

211 (98.1 %), M = 99 (99.0 %), F = 112 (97.4 %)

Frog

Rana (205), Rospo (10)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Grapes

Uva (214)

–

214 (99.5 %), M = 100 (100 %), F = 114 (99.1 %)

Bicycle

Bicicletta (211), Bici (4)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Ear

Orecchio (212), Padiglione auricolare (2)

–

214 (99.5 %), M = 100 (100 %), F = 114 (99.1 %)

Mushroom

Fungo (214), Porcino (1)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Bed

Letto (215)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Piano

Pianoforte (200), Piano (8), Pianoforte a coda (7)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Aeroplane

Aeroplano (135), Aereo (77), Apparecchio (3)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Banana

Banana (215)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Pepper

Peperone (209)

–

209 (97.2 %), M = 95 (95.0 %), F = 114 (99.1 %)

Desk

Scrivania (208), Cattedra (2)

–

210 (97.7 %), M = 98 (98.0 %), F = 112 (97.4 %)

Finger

Dito (199), Indice (10)

–

209 (97.2 %), M = 95 (95.0 %), F = 114 (99.1 %)

Motorbike

Motocicletta (162), Moto (52)

–

214 (99.5 %), M = 100 (100 %), F = 114 (99.1 %)

Butterfly

Farfalla (215)

–

215 (100 %), M = 100 (100 %), F = 115 (100 %)

Table

Tavolo (203), Tavolino (9)

–

212 (98.6 %), M = 97 (97.0 %), F = 115 (100 %)

Lettuce

Insalata (80), Cavolo (50), Verza (38), Lattuga (14),

Cavolfiore (12)

182 (84.6 %), M = 81 (81.0 %), F = 101 (87.8 %)

Chisel

Scalpello (106), Scalpellino (2), Sgorbia (3)

Cacciavite (36), Lima (25)

111 (51.6 %), M = 70 (70.0 %), F = 41 (35.6 %)

Sofa

Divano (204), Sofà (5), Ottomana (1)

–

210 (97.7 %), M = 96 (96.0 %), F = 114 (99.1 %)

Spanner

Chiave inglese (156), Chiave (27), C. meccanica o da meccanico, C. a stella, C. esagonale, C. fissa, C. per avvitare (9)

 

192 (89.3 %), M = 98 (98.0 %), F = 94 (81.7 %)

Artichoke

Carciofo (207)

–

207 (96.3 %), M = 97 (97.0 %), F = 110 (95.6 %)

Appendix 2

Correction grids of the original scores for the relevant combinations of age, education and sex. Note that if a subject obtains the top score, no corrections are needed.

Age

35

40

45

50

55

60

65

70

75

80

85

School

 (a) Overall responses (top score 80)

  Correction for males

   3

−0.03

+0.15

+0.34

+0.56

+0.80

+1.09

+1.42

+1.83

+2.36

+3.11

+4.38

   5

−0.69

−0.52

−0.33

−0.11

+0.14

+0.42

+0.76

+1.17

+1.70

+2.44

+3.72

   8

−1.48

−1.30

−1.11

−0.89

−0.65

−0.36

−0.03

+0.38

+0.91

+1.66

+2.94

   13

−2.50

−2.33

−2.13

−1.92

−1.67

−1.39

−1.05

−0.64

−0.11

+0.63

+1.91

   17

−3.19

−3.01

−2.82

−2.60

−2.35

−2.07

−1.73

−1.32

−0.79

−0.05

+1.23

  Correction for females

   3

+0.97

+1.15

+1.34

+1.56

+1.80

+2.09

+2.42

+2.83

+3.36

+4.11

+5.38

   5

+0.30

+0.48

+0.67

+0.69

+1.14

+1.42

+1.76

+2.17

+2.70

+3.44

+4.72

   8

−0.48

−0.30

−0.11

+0.11

+0.35

+0.64

+0.97

+1.38

+1.92

+2.66

+3.93

   13

−1.50

−1.33

−1.13

−0.92

−0.67

−0.39

−0.05

+0.36

+0.89

+1.63

+2.91

   17

−2.19

−2.01

−1.82

−1.60

−1.35

−1.07

−0.73

−0.32

+0.21

+0.95

+2.23

Age

35

40

45

50

55

60

65

70

75

80

85

Sex

 (b) Man-made stimuli (top score 30)

  Males

−0.97

−0.91

−0.85

−0.78

−0.70

−0.61

−0.50

−0.37

−0.20

+0.04

+0.44

  Females

+0.03

+0.09

+0.15

+0.22

+0.30

+0.39

+0.49

+0.63

+0.80

+1.04

+1.44

Age

35

40

45

50

55

60

65

70

75

80

85

School

 (c) Biological stimuli (top score 30)

  3

+0.50

+0.58

+0.67

+0.77

+0.88

+1.01

+1.17

+1.35

+1.60

+1.94

+2.52

  5

+0.10

+0.18

+0.27

+0.37

+0.48

+0.61

+0.76

+0.95

+1.19

+1.53

+2.12

  8

−0.37

−0.29

−0.20

−0.10

+0.01

+0.14

+0.29

+0.48

+0.72

+1.06

+1.64

  13

−0.99

−0.91

−0.83

−0.73

−0.61

−0.49

−0.33

−0.14

+0.10

+0.44

+1.02

  17

−1.41

−1.33

−1.24

−1.14

−1.03

−0.90

−0.75

−0.56

−0.32

+0.02

+0.61

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Laiacona, M., Barbarotto, R., Baratelli, E. et al. Revised and extended norms for a picture naming test sensitive to category dissociations. Neurol Sci 37, 1499–1510 (2016). https://doi.org/10.1007/s10072-016-2611-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10072-016-2611-0

Keywords

Search

Navigation