Behavior Research Methods, Instruments, & Computers1996,28 (4),537-555
A set of 254 Snodgrass-Vanderwart picturesstandardized for Spanish:
Norms for name agreement, image agreement,familiarity, and visual complexity
M.CARMEN SANFELIU and ANGEL FERNANDEZUniversidad de Salamanca, Salamanca, Spain
The Snodgrass and Vanderwart (1980)picture set was standardized for a Spanish sample (N = 261).The present article shows the main results, but more explicitly, it shows the differences between En- .glish and Spanish data. This evidence justifies the statement that normative data of cognitive stimulicannot be taken into another language directly, because object names that are very common in one language may not be so in another, or objects that have a specific name in one language may have a genericname in another, and so on. Finally, because of the potential usefulness of the data for bilingualism studies, the Spanish data are presented jointly with the English data.
Often, cognitive research requires that the material tobe processed be selected according to very specific,experimenter-defined characteristics. One way in whichthat selection can be effectively achieved is through theuse ofnormative databases. Norms for verbal material havebeen in use for many years (see Bradshaw, 1984, andBrown, 1976, for reviews of early work) and continue tobe used (see, e.g., Benjafield & Muckenheim, 1989; Erickson, Gaffney, & Heath, 1987; Ferraro & Kellas, 1990;Gibson & Watkins, 1988; Graff & Williams, 1987; Wilson, 1988).
Fewer normative data, however, have been collectedfor pictorial stimuli. Until the last decade it was difficultto establish comparisons among studies in which pictorial stimuli were used due to the fact that different picturesor drawings were utilized in each study. This lack ofstimulus homogeneity often resulted in a real difficulty forcomparing and interpreting results. A turning point was thepublication of Snodgrass and Vanderwart's (1980) wellknown paper, which provided a thorough report of normative data for a set of260 line drawings. Snodgrass andVanderwart attended to dimensions such as name, familiarity, visual complexity, and agreement between mentalimages and pictures.
This standardized set of drawings was soon used in experiments in which processing differences between words
The authors would like to thank Joan Gay Snodgrass for sending theset of pictures, as well as for her permission to publish the data fromSnodgrass and Vanderwart (1980). We would also like to thank Emiliano Diez for his work in the design and implementation of the computer program, and Olga Fernandez, Ildefonso Mendez, Emilio 1.Martin, and Marcos Perez for their help in the data collection. Correspondence should be addressed to M. C. Sanfeliu, Departamento dePsicologia Basica, Psicobiologia y Metodologia, Facultad de Psicologia,Universidad de Salamanca, Avda. de la Merced, 109-113,37005, Salamanca, Spain (e-mail: [email protected]).
and pictures were investigated, both in semantic and episodic memory tasks. The topic ofword-picture differencesis central in modern cognitive research and has led psychologists to the discovery of important facts and to theelaboration of sophisticated theories. For example, it hasbeen found that naming time is shorter for words than forpictures, although categorization is faster for pictures thanfor words (Potter & Faulconer, 1975; Snodgrass & McCullough, 1986, Vanderwart, 1984). Another important problem concerning word-picture differences is the picture superiority effect in memory tasks. The discovery of thisphenomenon stimulated the development oftheoretical accounts of intervening memory structures and processes,such as the dual coding hypothesis (Paivio, 1971, 1983)and the sensory-semantic model (Nelson, Reed, &Walling, 1976). Both approaches have shown considerablemerit in the attempt to account for recall and recognitiondata (Mantyla, 1986; see Bajo & Canas, 1988a, 1988b, forreviews). However, several discrepancies-like those arising from the utilization of different materials-need to beresolved. The use of standardized materials can contributeto the reduction ofthe disparities among experimental procedures and, in that way,provide a firmer basis for the comparison ofboth results and theoretical accounts.
Another area in which the use of standardized pictorialmaterials has proven to be important is perceptual identification and recognition. Examples ofprogress in this areaare found in work on recognition thresholds (Kroll & Potter, 1984; Snodgrass, 1984; Snodgrass & Corwin, 1988;Snodgrass & Poster, 1992) and on recognition of objectand non-object drawings (Mou, Anderson, Vaughan, &Rouse, 1989). Finally, drawings have been used increasingly in experiments aimed at the study of implicit memory. Recent work with pictures has demonstrated that theeffects found with verbal materials are readily replicablewhen drawings are used. Snodgrass and Vanderwart's
537 Copyright 1996 Psychonomic Society, Inc.
538 SANFELIU AND FERNANDEZ
(1980) original set of standardized drawings has beenmodified to build series with eight levels of increasingfragmentation (Snodgrass, Smith, Feenan, & Corwin,1987). The series have been used in experiments in whichprior exposure can prime both word and picture fragments (Feenan & Snodgrass, 1990; Hirshman, Snodgrass,Mindes, & Feenan, 1990; Snodgrass & Feenan, 1990;Snodgrass & Surprenant, 1989). This line ofresearch hasrecently been extended by the construction ofa series ofgradually degraded verbal material consisting ofeight levels of increasing fragmentation ofthe names of the Snodgrass and Vanderwartpictures (Snodgrass & Poster, 1992).
This review is far from exhaustive, but it clearly supports the view that the standardization of pictorial stimuli, such as the set of Snodgrass and Vanderwart (1980)drawings, has had an unquestionably positive effect onresearch devoted to the study ofpicture processing. At thesame time, further work is needed to expand the originalwork on picture standardization to other contexts becauseofnorm generalization difficulties. One case in which thatwork is necessary is the collection of normative data in different linguistic and sociocultural contexts. Objects thatare common in a given culture might be less common inanother culture; pictures that evoke a very specific andconcrete name in English might evoke a more generalterm in another language; or images that show a high levelof name agreement for English speakers might showhigher variability in a different community of speakers.Because of these and related problems, a serious effortshould be devoted to the empirical validation ofall kindsof normative data with appropriate population samples.The research reported in this paper is part ofan effort tocollect adequate normative data for pictorial stimuli inthe Spanish-speaking population.
Several studies have focused on the task of obtainingnormative data for verbal stimuli in Spanish. The firstquantitative report was made available by Juilland andChang-Rodriguez (1964), who developed a frequencyindex for Spanish words. In more recent years, other researchers have presented additional normative data forverbal material (see Dasi, 1986, for a listing and review ofavailable norms). Special mention has to be made to thework of Algarabel and his colleagues at the Universidadde Valencia (Algarabel, Ruiz, & Sanmartin, 1988; Algarabel, Sanmartin, Garcia, & Espert, 1986; Pascual, Gotor,Miralles, & Algarabel, 1979). They have made a very systematic attempt to collect norms for words, attending toseveral dimensions (category membership, free association, number ofsyllables, etc.). Furthermore, they have assembled the data in a single, 16,000-word computerizeddatabase, the University ofValencia's WordPool (UVWP),that allows for easy consultation (Algarabel et aI., 1988).
No comparable approach has been applied to pictorialstimuli. In order to provide researchers with normative dataon pictures that are appropriate for Spanish-speakingsubjects, a study was conducted in which the stimuli were254 line drawings from the set first used by Snodgrassand Vanderwart (1980). Closely following their procedure,norms for name agreement, familiarity, visual complex-
ity,image agreement, picture-name agreement, and imagevariability were collected.
METHOD
SubjectsA total of261 students from introductory courses in psychology
from the Universidad de Salamanca participated in the study. Different subjects participated in each of the six tasks. There were 62in the name agreement task, 55 in the image agreement task, 51 inthe familiarity task, 59 in the complexity task, and 19 and 15 in thesubsidiary tasks of picture-name agreement and image variabilityagreement, respectively. All the subjects were native Spanishspeakers and participated voluntarily as a course activity. Each session was run in small groups of up to 5 people.
MaterialsThe stimuli were a set of 254 pictures from Snodgrass and Van
derwart (1980). A digitized version ofthe drawings was used to present them on a Macintosh SE/30computer screen. Three ofthe tasksrequired auditory presentation ofnames. These auditory stimuli weredigitized, stored in the computer, and presented through a loudspeaker. A custom-made program controlled presentation ofvisualand auditory stimuli in all the tasks.
ProcedureThe procedure closely followed the steps described by Snodgrass
and Vanderwart (1980), both in terms ofthe tasks performed and inthe way the tasks were done. The only difference was that in our studyall the stimulus presentation processes were controlled by a microcomputer. Drawings were randomly presented in the center of thescreen inside a 95 X 130 rom frame over a white background. Soundswere amplified to a perfectly audible level. Subjects sat in a quiet roomin front ofthe computer screen at a distance ofapproximately 1.5 m.
The six different tasks were run in a similar way. At the beginningof every session in each task, subjects were read the instructionsand encouraged to answer carefully and consistently. They weregiven individual answer sheets and instructed to respond to everydrawing. The total amount of time was between 40 min for thequicker task and I h for the slower one, with one or two 3-min breaksdepending on the length of the session. Each picture was presentedfor a period of 4 sec, and there was a 4-sec interstimulus delay during which subjects had to rate the stimulus according to the instructions for each task.
In the name agreement task, subjects had to identify the drawingwith the first name that came to mind and write the name on the answer sheet. If that was not possible, they had to indicate whether thereason was "don't know object" (DKO), "don't know name" (DKN),or "tip of the tongue" (TOT).
In the familiarity task, subjects had to rate the degree to whichthe object represented in the drawing was familiar to them, basingtheir rating on the frequency with which they came across the object in everyday life. Their answer to each item was a whole number from a 5-point rating scale (I = a very unfamiliar object, 5 =a very familiar object).
The visual complexity task required the subjects to rate the complexity of each drawing, rather than the complexity of the object itrepresented. They also had to provide ratings from a 5-point scale(I = drawing very simple, 5 = drawing very complex).
In the image agreement task, subjects were asked to estimate howsimilar each picture was to a mental image of an object they hadpreviously been asked to form. First, while the screen remainedblack, they heard the name of the object through the loudspeaker,and had 3 sec to form a mental image of it. After that, a picture ofthe object was presented on the screen. From that moment they had4 sec to rate the degree of agreement between their mental imageand the picture (I = low agreement, 5 = high agreement). If theycould form no image, they were instructed to write the letters NI,
and if the formed image corresponded to a different object, theyhad to write the letters DO. The name used with each picture wasthe most common name given to the object in the name agreementtask. Nine ofthe drawings were excluded from this and the next twotasks because they failed to reach a 30% level of agreement in thename agreement task. The level of agreement was low for severaldifferent reasons, such as the use of a category label instead of theconcrete object name, confusion with similar-looking objects, object not known, and so on.
In the picture-name agreement task, subjects listened to the nameand saw the picture at the same time. After each picture-name presentation, subjects had 4 sec to rate, on a 5-point scale, how closelythe picture matched the way they expected the named object tolook. In the last task, image variability, subjects listened to the namealone and then they had to indicate, using a 5-point scale (1 = fewimages, 5 = many images), whether the name evoked few or manydifferent images.
RESULTS AND DISCUSSION
A summary of the rating data obtained from our samples ofSpanish-speaking subjects is presented in Appendix A. Blank responses were not taken into account inthe computation ofthe ratings. Toallow for easy referenceand data comparison, entries are listed according to theidentifying numbers originally assigned to each drawingby Snodgrass and Vanderwart (1980). For each picture,the following information is presented: (1) most frequentname given in Spanish; (2) most frequent name in English; (3) two measures ofname agreement, the statistic Hand the percentage of subjects producing the most common name; and (4) the means and standard deviations forimage agreement, familiarity, visual complexity, picturename agreement, and image variability. Along with theratings obtained with Spanish-speaking subjects, Appendix A also presents the data obtained by Snodgrass andVanderwart with their English-speaking subjects. Thisgrouping and presentation of the data allows for easycomparison ofratings given by the two samples to the samedrawing in each task. Appendix B lists the alternate namesgivento each drawing, with an indicationoftheir frequency.
Table I presents summary statistics for the followingindices: H (reflecting name agreement), percentage, imageagreement, familiarity, and complexity. The table con-
PICTURES STANDARDIZED FOR SPANISH 539
tains the summary for both the Spanish-speaking samplesand the English-speaking samples reported by Snodgrassand Vanderwart (1980). Although H and percentage aretwo measures of name agreement, following Snodgrassand Vanderwart's arguments, 1 we will use H as the measure ofname agreement. Separate t tests were conductedto compare the ratings given in each of these dimensionsby Spanish speakers and English speakers in response tothe 254 drawings that were common to both studies. Therewere small but significant differences for familiarity andcomplexity. In the English-speaking sample the pictureswere judged more familiar than those in the Spanish one[t(253) = 4.32]. The reason seems quite obvious: Pictures were selected in the American context (things likea baseball bat, a football helmet, or a kettle are more familiar to English speakers in America than to Spanishspeakers in Spain). With regard to complexity, the Spanish sample judged the pictures as slightly more simplethan did the English-speaking one (t(253) = 6.60]. Perhaps the different size of the pictures reproduced by theslide projector or by the computer can account for the difference. There is a larger significant difference when the Hvalue is considered [t(253) = 7.97]. The English-speakingsample had bigger H values than did the Spanish one. Because H is an index of name agreement, it seems that thesample in our group showed less variability in the numberofnames applied to objects than did the English-speakingsample. Although the comparisons are rather global, thedifferences found are evidence that normative data forthis kind ofstimuli should be collected for different populations, because the responses that stimuli evoke in different tasks can vary across cultures and/or languages.
Given this result, we performed a qualitative comparison ofthose Spanish-English pairs whose difference wasexaggerated (we selected those scores 2 standard deviations above or below the mean). Following this criterionwe found 39 pairs for which the Spanish word surpassedthe English one in such a difference, but only 8 pairs forwhich the English word exceeded the Spanish one in sucha difference. The analysis of all these pairs helped us tofind some reasons for the discrepancy. One is that thereis a larger proportion of compound common words in
Table 1Summary Statistics for All Variables: Spanish and U.S.A. Samples
Picture-NameH* % Image Agreement Familiarity Complexity Agreement Variability
U.S.A. Spain U.S.A. Spain U.S.A. Spain U.S.A. Spain U.S.A. Spain (Spain) (Spain)
M 0.56 0.27t 86.59 82.30 3.69 3.71 3.29 3.12t 2.96 2.67t 4.06 2.61SD 0.53 0.41 14.34 21.68 0.58 0.60 0.96 1.11 0.89 0.93 0.49 0.59Median 0.42 0.12 3.72 3.84 3.32 3.06 2.93 2.52 4.11 2.6Mode 0 4.11 1.53 2.05 4.47 2.53Range 2.55 2.19 67 91.9 2.68 3.03 3.72 3.67 3.78 3.68 2.74 3.13Min 0 0 33 8 2.05 1.74 1.18 1.27 I 1.05 2.26 1.05Max 2.55 2.19 100 100 4.73 4.77 4.9 4.94 4.78 4.73 5 4.13QI 0.12 0.04 3.27 3.29 2.49 .2.16 2.28 1.98 3.74 2.16Q3 0.87 0.28 4.15 4.16 4.09 4.08 3.59 3.39 4.42 3.13Skew 1.5 2.39 -1.38 -1.42 0.96 -0.71 0.93 0.01 1.02 0.28 -0.57 -0.06Kurtosis 5.79 1.49 1.06 0.01 -1.32 -0.89 0.19 -0.49Validcases 260 254 260 254 260 245 260 254 260 254 245 245
*Increasing H values indicate decreasing name agreement. "Significanr t test differences at p < .0 I.
540 SANFELIU AND FERNANDEZ
English than in Spanish: In 14 ofthe 39 pairs, the Englishword was a compound (e.g., avian-airplane, sartimfrying pan, collar-necklace, pincel-paintbrush, bolsapocketbook,patin-roller skate, rnaleta-suitcase, regaderawatering can). Another reason is that the number ofalternative names was always superior for the member withless agreement (8/8 for the pairs superior in English and31/39 for the pairs superior in Spanish). We also thoughtabout two other possible reasons: (I) the possibility thata higher H in one ofthe languages was due to the existenceofa very frequentsynonym (e.g., barril/tonel-barrel, cuenco/tazon-bowl, clavo/punta-nail, etc.) and (2) the use ofgeneric words instead ofspecific ones (e.g., shirt instead ofblouse, pan instead offrying pan, skate instead of rollerskate, glass instead of wineglass, etc.).
In conclusion, a detailed consultation of the ratingsobtained for each drawing with a particular sample is theoptimal strategy for selecting stimuli to be used withsubjects from that population.
Correlations Among the MeasuresThree correlational analyses were performed on the
data. The first one correlated the data provided by Snodgrass and Vanderwart (1980) with the data obtained inthe present study for H, image agreement, familiarity,and visual complexity. As shown in Table 2, there werefairly high and significant correlations for all the variables. Correlations were higher for complexity and familiarity than for name agreement (H and %) and imageagreement. This is probably because complexity and familiarity werejudgments that were produced directly aboutthe picture or the object it represented, but not about theword used to name it. On the other hand, name agreement and image agreement were directly dependent onwords and the particular structure oflanguage. This pattern ofcorrelations is similar to that found for a Japanesesample (Matsukawa, 1983), for whom the lower correlations were also H values, percentage, and image agreement, demonstrating that naming pictures is not equallydirect in all languages (Table 2).
The second correlational analysis involved the dataobtained in the present study. Each measured variable
Table 2Significant Correlations Among the Measured Variables
in the Spanish and U.S.A. Samples(for Snodgrass and Vanderwart Pictures)
U.S.A. Sample*
Variable NA (H) % IA Fam Comp
Spanish sampleName agreement (H) .268Name agreement (%) .427Image agreement .557Familiarity .739Visual complexity .740
Japanese samplet .258 .185 .409 .858 .941
Note-v-Nx, name agreement; lA, image agreement; Fam, familiarity;Comp, visual complexity. All listed correlation coefficients for the current study and the Snodgrass and Vanderwart (1980) study are significant at p < .0 I. *Results from Snodgrass and Vanderwart (1980)."Results from Matsukawa (1983).
was correlated with the rest. Table 3 shows the significant correlations, which overall are not too different fromthe ones obtained by Snodgrass and Vanderwart (1980).Moreover, they are also similar to the ones obtained withchildren (Berman, Friedman, Hamberger, & Snodgrass,1989), Japanese (Matsukawa, 1983), and Dutch (vanSchagen, Tamsma, Bruggemann, Jackson, & Michon,1983) samples.
The third correlational analysis involved a comparison between the variables measured in the present studyand the 11 variables for which the UVWP (Algarabel et aI.,1988) contains data. Table 4 shows the significant correlations obtained for the data from drawings that had theirmost frequently given name included in the word database. There is a high correlation (.52) between familiarity of the object depicted in the drawing and familiarityof the word. Also high were the correlations of imagevariability with two word variables: number ofattributes(.43) and meaningfulness (.46).
Factor AnalysisAccording to Snodgrass and Vanderwart (1980), the
interrelations among name agreement, familiarity, visualcomplexity, and image agreement were quite low, sug-
Table 3Significant Correlations Among the Measured Variables in a Spanish Sample,
for Snodgrass and Vanderwart (1980) Pictures, and Between Themand the Juilland and Chang-Rodriguez (1964) Frequencies
Variable NA (H) % IA Fam Comp Freq PicName 1-Var
Name agreement (H) 1.000Name agreement (%) - .740 1.000Image agreement .218 1.000Familiarity - .155 1.000Visual complexity -.136 -.4591.000Frequency .214 1.000Picture-name agreement .723 - .206 1.000Image variability - .276 .30 - .286 .327 - .349 1.000
Note-i-All listed correlation coefficients for the current study are significant at p < .01. NA, nameagreement; lA, image agreement; Fam, familiarity; Comp, visual complexity; Freq, frequency; PicName, picture-name agreement; l-Var, image variability.
PICTURES STANDARDIZED FOR SPANISH 541
Table 4Significant Correlations Among Picture Variables and (UVWP)
Variables for the Concepts Available in the UVWP
scriptive ratings for a number ofpicture attributes of thedrawings previously presented by Snodgrass and Vanderwart (1980) with English-speaking subjects are nowavailable for Spanish-speaking subjects. With these data,pictures can be selected in a more accurate way becausetheir indices are specific for Spanish-speaking subjects.The different comparisons between the English and Spanish ratings (and also Japanese; see Table 2) show that,despite the pictures being judged to be of similar complexity and familiarity, name agreement and image agreement are specific to the particular language.
Twoalternative explanations can be offered to explainthe differences found between our study and the originalone: The differences might be due to either language orcultural context. If differences in name agreement andimage agreement were due exclusively to cultural context, the familiarity correlations would not be so high;correlations of.74 in our sample and .86 in the Japanesesample suggest that the cultural contexts are comparableand very similar (i.e., objects that are familiar in one context are familiar in the other). However, even if this argument were correct, the question remains open. Perhapsif another experiment were conducted with Spanishspeaking people living in the United States, the problemcould be solved. The finding ofhigh correlations betweensuch a sample and our sample for name agreement andimage agreement would indicate that the present differences are due to language. Higher correlations betweensuch a sample and the Snodgrass and Vanderwart (1980)sample, by contrast, would indicate that the present differences were due to culture.
Inthe absence of relevant data, the question of language versus culture remains open and clearly in need offurther investigation. In the interim, attention should bepaid to the issue ofgeneralization of the results obtainedwith Spanish speakers from Spain to other groups ofSpanish speakers. Although Spanish speakers share a basiccorpus oflexical and grammatical knowledge, it might bethe case that different groups of Spanish speakers wouldshow different patterns ofresults because ofdialectal andcultural influences.
REFERENCES
.437
.462-.194
.251
Fam Comp PicName I-Var
.262
.173 -.128 .269
.214 .183-.214
Picture Norms
IANA(H)Word Norms
Letter meanings"Letter frequency]Letters"ImageryMeaningfulnessAttribute - .231Concreteness .217Categorizability .229 .218 .193Familiarity - .206 .526 - .259 .192Pleasantness .180 .269Syllables· - .158
Note-All listed correlation coefficients for the current results are significant atp < .05. All valid cases equal 85, except in • (211 valid cases)and t (89 valid cases). NA, name agreement; lA, image agreement;Fam, familiarity; Comp, visual complexity; PicName, picture-nameagreement; I-var, image variability.
gesting that the four measures represented largely independent attributes of the pictures. They also assumed thatpicture-name agreement and image variability were twovariations ofthe image agreement task, and so the ratingsin those two tasks related to the same attribute ofpictures.To examine these assumptions and to obtain further dataon the structure of attributes, we conducted a principalcomponents factor analysis and a varimax rotation.
The analysis showed that only four factors explainedaltogether 88% of the variance. Table 5 shows that Factor 1 loads on image agreement and picture-name agreement. This fact is congruent with the assumption thatboth variables refer to the same underlying attribute ofpictures. However, Factor 2 loads positively on complexity and negatively on familiarity, implying that visuallycomplex pictures tend to be unfamiliar, or, in other words,familiar objects are usually simple. Factor 3 shows thatimage variability is independent of image agreement. Itseems that variability of images is closer to the richnessof the concept (familiarity might be implied) than to thevividness of the image the concept activates. Finally,Factor 4 loads on name agreement, a result that reflectsthe independence of this attribute.
CONCLUSION
The main goal of the present research was to collectnormative data for pictorial stimuli that could be used inresearch with Spanish-speaking samples. As a result, de-
TableSFactor Analysis (Varimax Rotation)
Variable I 2 3 4
Name agreement (H) - .083 .035 - .152 .981Image agreement .915 -.061 -.135 -.094Familiarity -.128 -.757 .419 .001Visual complexity -.065 .915 .121 .044Picture-name agreement .919 .089 -.138 -.010Image variability -.214 -.066 .917 -.175
ALGARABEL, S., RUIZ, J. c. & SANMARTIN, J. (1988). The Universityof Valencia's computerized word pool. Behavior Research Methods,Instruments. & Computers, 20, 398-403.
ALGARABEL, S., SANMARTIN, J., GARciA, J., & ESPERT, R. (1986). Normas de asociacion libre para investigacion experimental. Unpublished manuscript, Universidad de Valencia, Departamento de Psicologia Experimental.
BAJO, M. T., & CANAS, J. J. (1988a). Dibujos y palabras: Diferencias enprocesamiento. Psicologica, 9, 209-224.
BAJO, M. T., & CANAS, J. J. (I 988b). Dibujos y palabras: Diferencias enestructuras. Psicologica, 9, 225-240.
BENJAFIELD, J., & MUCKENHEIM, R. (1989). Dates of entry and measures of imagery, concreteness, goodness, and familiarity for 1,046words sampled from the Oxford English Dictionary. Behavior Research Methods. Instruments, & Computers, 21, 31-52.
BERMAN, S., FRIEDMAN, D., HAMBERGER, M., & SNODGRASS, J. G.(1989). Developmental picture norms: Relationships between name
542 SANFELIU AND FERNANDEZ
agreement, familiarity, and visual complexity for child and adult ratings of two sets of line drawings. Behavior Research Methods, Instruments, & Computers, 21, 371-382.
BRADSHAW, J. L. (1984). A guide to norms, ratings and lists. Memory &Cognition, 12, 202-206.
BROWN, A. S. (1976). Catalog of scaled verbal material. Memory &Cognition,4,1-45.
DASf, C. (1986). Guia de indices y datos normativos sobre material verbal. Psicologica, 7, 99-102.
ERICKSON, J. R., GAFFNEY, C. R., & HEATH, W. P.(1987). Difficulty andfamiliarity norms for 192 single-solution word fragments. BehaviorResearch Methods, Instruments, & Computers, 19,370-376.
FEENAN, K., & SNODGRASS, J. G. (1990). The effect of context on discrimination and bias in recognition memory for pictures and words.Memory & Cognition, 18,515-527.
FERRARO, E R., & KELLAS, G. (1990). Normative data for number ofword meanings. Behavior Research Methods, Instruments, & Computers, 22, 491-498.
GIBSON, J. M., & WATKINS, M. J. (1988). A pool of 1,086 words withunique two-letter fragments. Behavior Research Methods, Instruments, & Computers, 22, 390-397.
GRAFF, P., & WILLIAMS, D. (1987). Completion norms for 40 threeletter word stems. Behavior Research Methods, Instruments, & Computers, 19, 422-445.
HIRSHMAN, E., SNODGRASS, J. G., MINDES, J., & FEENAN, K. (1990).Conceptual priming in fragment completion. Journal ofExperimental Psychology: Learning, Memory, & Cognition, 16,634-647.
JUILLAND, A., & CHANG-RoDRIGUEZ, E. (1964). Frequency dictionaryofSpanish words. London: Mouton.
KROLL, J. E, & POTTER, M. C. (1984). Recognizing words, pictures, andconcepts: A comparison of lexical, object, and reality decisions. Journal of VerbalLearning & Verbal Behavior, 23, 39-66.
MANTYLA,1. (1986). Optimizing cue effectiveness: Recall of 500 and600 incidentally learned words. Journal ofExperimental Psychology:Learning, Memory, & Cognition, 12, 66-71.
MATSUKAWA, J. (1983). A study ofcharacteristics ofpictorial material(Memoirs of the Faculty of Law and Literature). Shimane University,Shirnane-ken, Japan.
Mou, r..c., ANDERSON, N. S., VAUGHAN, W. S., JR., & ROUSE, R. 0.,JR. (1989). Recognition memory for nonobject drawings. Bulletin ofthe Psychonomic Society, 27, 399-401.
NELSON, D. L., REED, V. S., & WALLING, J. R. (1976). Pictorical superiority effect. Journal of Experimental Psychology: Human Learning & Memory, 2, 523-528.
PAIVIO, A. (1971). Imagery and verbal processes. New York: Holt,Rinehart & Winston.
PAIVIO, A. (1983). The empirical case for dual coding. In 1. C. Yuille(Ed.), Imagery, memory and cognition: Essays in honor ofAllanPaivio (pp. 307-332). Hillsdale, NJ: Erlbaum.
PASCUAL, J., GOTOR, A., MIRALLES, J. L., & ALGARABEL, S. (1979).Normas categoriales para el estudio de la memoria humana (Aetasdel Congreso Nacional de Psicologia). Valencia, Spain.
POTTER, M. c., & FAULCONER, B. A. (1975). Time to understand pictures and words. Nature, 253,437-438.
SNODGRASS, J. G. (1984). Concepts and their surface representations.Journal of VerbalLearning & VerbalBehavior, 23, 3-22.
SNODGRASS, J. G., & CORWIN, J. (1988). Perceptual identificationthresholds for 150 fragmented pictures from the Snodgrass and Vanderwart picture set. Perceptual & Motor Skills, 67, 3-36.
SNODGRASS, J. G., & FEENAN, K. (1990). Priming effects in picturefragment completion: Support for the perceptual closure hypothesis.Journal ofExperimental Psychology: General, 119, 276-298.
SNODGRASS, J. G., & MCCULLOUGH, B. (1986). The role of visual similarity in picture categorization. Journal ofExperimental Psychology:Learning, Memory, & Cognition, 12,147-154.
SNODGRASS, J. G., & POSTER, M. (1992). Visual-word recognitionthresholds for screen-fragmented names of the Snodgrass and Vanderwart pictures. Behavior Research Methods, Instruments, & Computers, 24, 1-15.
SNODGRASS, J. G., SMITH, B., FEENAN, K., & CORWIN, J. (1987). Fragmenting pictures on the Apple Macintosh computer for experimentaland clinical applications. Behavior Research Methods, Instruments,& Computers, 19,270-274.
SNODGRASS, J. G., & SURPRENANT, A. (1989). Effect of retention interval on implicit and explicit memory for pictures. Bulletin ofthe Psychonomic Society, 27, 395-398.
SNODGRASS, J. G., & VANDERWART, M. (1980). A standardized set of260 pictures: Norms for name agreement, image agreement, familiarity, and visual complexity. Journal ofExperimental Psychology:Human Learning & Memory, 6, 174-215.
VANDERWART, M. (1984). Priming by pictures in lexical decision. Journal of VerbalLearning & VerbalBehavior, 23, 67-83.
VAN SCHAGEN, 1.,TAMSMA, N., BRUGGEMANN, E, JACKSON, L. L., & MICHON, J. A. (1983). Namen en normen voor plaatjes. Nederlands Tijdschrift voor de Psychologie, 38, 236-241.
WILSON, M. (1988). MRC psycholinguistic database: Machine-usabledictionary, version 2.00. Behavior Research Methods, Instruments,& Computers, 20, 6-10.
NOTE
I. 'The H value captures more information about the distribution ofnames across subjects than does the percentage agreement measure. Forexample, if two concepts both are given their dominant name by 60%of the subjects, but one is given a single other name and the second isgiven four other names, both concepts will have equal percentage agreement scores, but the first will have a lower H value" (Snodgrass & Vanderwart, 1980, p. 184).
AP
PE
ND
lXA
Th
efo
lIo
win
gin
form
atio
nis
sho
wn
bel
ow
:th
eid
enti
fyin
gn
um
ber
ofe
ach
pic
ture
(fro
mth
eo
rig
inal
set)
;its
mo
stco
mm
on
Sp
anis
hn
ame;
itsE
ng
lish
nam
efr
om
Sn
od
gra
ssan
dV
and
erw
art'
s(1
98
0)
set;
two
mea
sure
so
fn
ame
agre
emen
t,H
and
per
cen
tag
eag
reem
ent;
the
mea
ns
and
stan
dar
dd
evia
tio
ns
for
rati
ng
so
fim
age
agre
emen
t,fa
mil
iari
ty,
com
ple
x-
ity,
pic
ture
-nam
eag
reem
ent,
and
vari
abil
ity.
Wh
ere
avai
lab
le,
dat
afr
om
the
En
gli
shsa
mp
lear
ep
rese
nte
d.
Vis
ual
Com
plex
ity
Pic
-Nam
eN
ame
Agr
eem
ent
Imag
eA
gree
men
tF
amil
iari
tyA
gree
men
tV
aria
bilit
ySp
anis
hE
nglis
hSp
anis
hE
nglis
hSp
anis
hE
nglis
hS
pani
shE
nglis
h(S
pani
sh)
(Spa
nish
)
Span
ish
Eng
lish
H%
H%
MSD
MSD
MSD
MSD
MSD
MSD
MSD
MSD
1A
cord
eon
Acc
ordi
on0.
1690
0.18
883.
651.
13.
401.
041.
550.
762.
151.
204.
250.
844.
680.
613.
681.
382
12
Avi
anA
irpl
ane
0.00
100
1.77
603.
960.
793.
231.
122.
431.
193.
780.
993.
151.
053.
500.
103.
891.
333.
131.
463
Coc
odri
loA
lliga
tor
0.13
890.
5488
4.11
0.92
3.98
0.85
1.27
0.75
1.65
0.82
3.8
0.96
4.08
0.88
4.42
0.96
1.8
0.94
4A
ncla
Anc
hor
0.03
970.
1793
4.18
0.72
4.32
0.85
1.43
0.9
1.60
0.83
2.07
0.85
2.58
0.70
4.58
0.61
2.33
1.18
5H
orm
iga
Ant
0.18
650.
6481
31.
332.
921.
242.
21.
252.
621.
113.
690.
813.
920.
823.
631.
422.
61.
186
Man
zana
App
le0.
0298
0.16
984.
40.
784.
050.
874.
330.
823.
981.
081.
540.
771.
820.
674.
420.
772.
070.
887
Bra
zoA
rm0.
D7
950.
5390
4.11
0.92
3.95
0.89
4.67
0.97
4.75
0.58
2.2
0.69
2.15
0.61
3.89
1.56
2.53
1.13
8Fl
echa
Arr
ow0.
1492
0.16
981.
741
2.27
1.29
3.04
1.18
3.38
1.23
1.08
0.47
1.05
0.31
3.53
1.39
2.53
0.99
9A
lcac
hofa
Art
icho
ke0.
3089
1.54
523.
961.
153.
441.
472.
441.
22.
291.
453.
120.
933.
720.
774.
32I
1.67
1.11
10C
enic
ero
Ash
tray
0.02
980.
0010
03
1.1
3.20
1.05
3.75
1.4
3.56
1.37
2.03
0.87
2.25
0.89
4.16
1.3
2.6
1.5
11E
spar
rago
Asp
arag
us0.
5873
1.27
693.
021.
343.
501.
262.
631.
352.
681.
382.
690.
973.
320.
793.
531.
612.
331.
0512
Hac
haA
xe0.
0498
0.53
904.
110.
744.
500.
631.
860.
982.
281.
101.
680.
682.
480.
744.
470.
92.
331.
3513
Coc
heci
toB
aby
carr
iage
1.82
321.
0052
3.07
1.32
3.65
1.01
2.61
1.25
2.72
1.14
3.27
0.96
3.42
0.10
3.37
1.57
2.67
1.29
14Pe
lota
Bal
l0.
2368
0.44
932.
851.
152.
841.
192.
821.
153.
201.
211.
920.
822.
280.
813.
531.
53.
531.
0615
Glo
boB
allo
on0.
0010
00.
0010
04.
540.
94.
331.
182.
31.
092.
581.
021.
140.
571.
550.
593.
841.
463.
530.
6416
Pla
tano
Ban
ana
0.00
100
0.00
100
4.36
0.82
4.42
0.70
3.84
1.14
3.65
1.04
1.34
0.63
1.32
0.47
4.16
1.21
2.6
1.24
17C
asa
Bar
n2.
0029
1.31
693.
221.
112.
821.
562.
381.
063.
220.
913.
300.
98'1:
:l .....18
Bar
ril
Bar
rel
1.01
580.
0010
04.
770.
514.
311.
142.
161.
192.
021.
132.
830.
953.
320.
934.
890.
321.
930.
59(1
19B
ate
Bas
ebal
l1.
0648
1.00
524.
21.
024.
580.
701.
520.
793.
681.
151.
20.
411.
200.
404.
321
1.6
0.83
.., C20
Ces
taB
aske
t0.
1697
0.60
902.
770.
992.
621.
232.
290.
992.
180.
973.
711.
074.
300.
843.
581.
33.
20.
94:::0
21O
soB
ear
0.04
980.
5388
3.63
1.16
3.62
1.09
1.51
0.83
1.98
1.01
3.53
0.88
3.68
0.90
3.95
1.27
2.13
1.13
m22
Cam
aB
ed0.
0498
0.00
100
3.49
1.03
3.65
0.99
4.78
0.64
4.72
0.77
2.69
0.93
2.85
0.79
3.79
1.4
3.6
1.06
en en23
Abe
jaB
ee0.
1960
1.65
603.
451.
172.
781.
172.
491.
172.
681.
194.
460.
774.
750.
493.
951.
312.
60.
99~
24C
ucar
acha
Bee
tle0.
2127
2.18
502.
051.
131.
981.
071.
881.
003.
610.
873.
650.
8225
Cin
turo
nB
elt
0.13
870.
0010
03.
980.
832.
920.
944.
431.
062.
200.
931.
640.
762.
620.
663.
891.
292.
931.
1Z
26C
ampa
naB
ell
0.04
980.
1698
3.89
1.13
4.05
1.01
2.47
1.14
4.12
1.05
2.31
0.91
2.00
0.59
4.42
0.84
3.2
0.86
~27
Bic
icle
taB
icyc
le0.
1889
0.53
883.
910.
953.
401.
093.
611.
173.
781.
043.
880.
973.
850.
114.
051.
133.
071.
03:::0
28Pa
jaro
Bir
d0.
8281
0.69
883.
751.
083.
331
.l4
3.45
1.3
3.62
1.16
3.25
0.86
3.25
0.73
3.89
1.41
41
0 .....29
Cam
isa
Blo
use
0.26
441.
8943
3.38
1.11
2.80
1.09
4.37
0.98
4.18
0.97
2.85
0.76
3.10
0.66
4.53
0.77
3.33
1.59
N m30
Lib
roB
ook
0.00
100
0.00
100
3.95
0.95
4.33
1.00
4.84
0.61
4.75
0.54
1.98
0.68
2.10
0.66
4.53
0.96
3.6
1.24
031
Bol
aB
oot
0.06
950.
6988
3.29
1.11
2.28
0.96
4.08
1.15
3.38
1.24
2.22
0.79
2.45
0.70
3.68
1.34
3.13
1.06
>-r
j
32B
otel
laB
ottle
0.00
100
0.28
953.
931.
052.
851.
223.
881.
073.
721.
051.
290.
641.
680.
793.
951.
273.
60.
990
33L
azo
Bow
0.04
981.
2574
3.89
1.17
2.67
1.25
2.67
1.31
2.25
1.18
2.39
0.89
2.75
0.86
3.95
1.13
2.47
0.83
:::034
Cue
nco
Bow
l1.
5534
0.17
954.
350.
83.
790.
893.
881.
094.
180.
921.
410.
651.
820.
804.
161.
212.
271.
33en
35C
aja
Box
0.00
100
0.80
883.
371.
232.
901.
183.
491.
172.
881.
311.
190.
511.
380.
763.
371.
073.
671.
4~
36Pa
nde
mol
deB
read
0.17
420.
8483
3.67
1.2
4.02
1.06
4.22
1.22
4.40
0.83
1.58
0.79
1.95
0.67
3.89
1.2
1.6
0.83
Z .....37
Esc
oba
Bro
om0.
0597
0.00
100
3.82
1.42
4.35
0.73
3.69
1.27
3.42
1.14
2.29
0.83
2.42
0.80
3.79
1.47
2.6
0.99
en38
Cep
illo
Bru
sh0.
1889
0.88
833.
211.
633.
201.
273.
981.
333.
801.
082.
510.
82.
820.
743.
581.
433.
071.
03::r:
39A
utob
usB
us0.
1289
0.00
100
3.62
1.08
4.08
1.01
4.22
0.9
4.50
0.74
4.07
1.08
3.95
0.10
4.11
1.15
2.87
1.36
40M
arip
osa
But
terf
ly0.
0010
00.
0010
04.
070.
923.
920.
852.
691.
192,
921.
173.
530.
974.
250.
774.
320.
823.
41.
18V
I41
Bor
onB
utto
n0.
0298
0.16
984.
480.
934.
480.
924.
10.
963.
851.
261.
460.
652.
020.
734.
580.
612.
671.
23~ V
ol
AP
PE
ND
IXA
(Con
tinu
ed)
v..
.j:>
.
Pic
-Nam
e.j:
>.
Nam
eA
gree
men
tIm
age
Agr
eem
ent
Fam
ilia
rity
Vis
ual
Com
plex
ity
Agr
eem
ent
Var
iabi
lity
Spa
nish
Eng
lish
Spa
nish
Eng
lish
Spa
nish
Eng
lish
Spa
nish
Eng
lish
(Spa
nish
)(S
pani
sh)
V1
----
Spa
nish
Eng
lish
H%
H%
MSD
MSD
MSD
MSD
MSD
MSD
MSD
MSD
>-42
Tar
taC
ake
0.22
770.
8483
3.51
\.1
83.
45\.
122.
08\.
I4.
021.
062.
250.
862.
880.
684.
05\.
18
3.13
1.06
Z "Tl
43C
amel
loC
amel
0.47
790.
0095
3.67
1.33
3.92
0.99
1.45
0.88
2.08
1.06
3.29
0.95
3.75
0.73
4.11
1.15
2.67
1.5
tTi
r-'
44V
ela
Can
dle
0.00
100
0.00
100
4.08
0.97
3.85
0.76
4.84
0.54
3.08
\.15
1.53
0.75
2.48
0.90
4.58
0.84
2.8
1.32
245
Can
non
unav
aila
ble
46G
orr
aC
ap0.
3090
0.59
863.
040.
972.
681.
322.
641.
323.
12\.
12
1.76
0.77
2.18
0.74
3.11
1.52
3.27
0.8
~47
Coc
heC
ar0.
0695
1.08
812.
871.
283.
101.
224.
430.
924.
700.
603.
85\.
I4.
050.
953.
581.
574.
13\.
13C
l48
Zan
ahor
iaC
arro
t0.
0298
0.00
100
4.53
0.57
4.5
00.
673.
331.
213.
550.
972.
360.
942.
950.
774.
630.
52.
07\.
16
"Tl
49G
ato
Cat
0.00
100
0.00
100
3.65
1.07
3.78
0.91
3.06
1.36
4.22
0.88
2.71
0.85
3.25
0.94
4.16
0.9
2.93
0.96
tTi
50G
usan
oC
ater
pill
er0.
2642
0.96
792.
651.
422.
381.
231.
90.
961.
720.
813.
421.
043.
580.
103.
161.
342.
471.
06::0 Z
51A
celg
asC
eler
y1.
278
0.83
763.
75\.
14
2.73
1.2
3.40
\.II
3.58
I4.
250.
86>-
52C
aden
aC
hain
0.06
950.
1698
4.18
1.09
4.4
60.
842.
33\.
19
2.82
1.00
2.31
1.05
2.55
0.97
3.89
\.I
3.47
0.99
Z53
Sil
laC
hair
0.00
100
0.00
100
4.47
0.96
3.22
1.28
2.1
\.1
24.
580.
862.
250.
842.
050.
704.
890.
321.
80.
77C
l54
Cer
eza
Ch
erry
0.09
900.
5283
4.33
0.94
4.52
0.81
3.06
\.1
23.
38\.
18
1.31
0.56
1.60
0.62
4.16
1.07
2.13
0.92
tTl
N55
Gal
lina
Chi
cken
0.08
951.
3567
40.
823.
621.
282.
451.
292.
421.
093.
410.
833.
480.
904.
580.
842.
60.
9156
Des
torn
illa
dor
Chi
sel
0.97
152.
3333
3.15
1.22
2.25
1.22
2.46
1.24
2.03
0.74
3.12
0.75
57Ig
lesi
aC
hurc
h0.
0010
00.
4493
2.62
1.24
2.98
1.31
3.49
1.39
3.38
1.34
3.68
\.II
3.28
0.11
3.58
1.39
3.53
1.06
58P
uro
Cig
ar0.
0298
0.00
100
3.22
1.26
2.75
0.92
4.67
0.74
2.35
1.26
2.85
1.08
3.58
0.97
2.26
1.41
2.73
\.1
659
Cig
arro
Cig
aret
te0.
1684
0.16
984.
580.
744.
650.
613.
591.
533.
651.
411.
640.
832.
250.
774.
371.
262.
871.
5160
Rel
ojC
lock
0.10
950.
1698
4.11
1.33
2.20
0.90
3.82
1.01
4.38
0.99
1.63
1.03
2.68
0.88
4.68
0.48
2.93
0.88
61P
inza
Clo
thes
pin
0.16
820.
8381
4.0
90.
823.
721.
363.
980.
952.
801.
472.
150.
872.
820.
924.
420.
841.
81.
0162
Nub
eC
loud
1.21
550.
1795
3.33
1.07
2.85
1.30
3.66
1.47
3.82
\.1
91.
951.
022.
120.
873.
321.
422.
81.
2163
Pay
aso
Clo
wn
0.04
980.
2895
3.11
1.04
3.25
0.89
2.33
\.1
82.
60\.
16
4.22
0.89
4.50
0.81
4.21
\.1
82.
670.
964
Abr
igo
Coa
t1.
0069
0.95
793.
191.
082.
591.
324.
181.
073.
88\.
19
2.54
0.84
2.55
0.67
3.95
1.51
2.87
\.13
65Pe
ine
Co
mb
0.00
100
0.44
934.
290.
983.
780.
854.
510.
814.
520.
872.
140.
922.
380.
834.
370.
962.
130.
8366
Mai
zC
orn
0.21
530.
8881
4.11
0.99
4.08
0.85
2.43
1.2
3.50
1.05
3.47
1.06
3.58
0.86
4.11
1.24
2.07
0.96
67S
ofa
Cou
ch0.
2863
0.92
674.
151.
033.
051.
054.
450.
734.
400.
741.
240.
542.
280.
845
02.
80.
9468
Vac
aC
ow0.
0010
00.
4493
4.15
0.93
3.92
0.90
3.63
\.1
72.
421.
202.
971.
023.
850.
964.
370.
762.
731.
2269
Cor
ona
Cro
wn
0.00
100
0.00
100
3.31
1.08
2.85
0.79
1.49
0.92
1.52
0.81
4.34
0.78
4.25
0.77
3.47
1.43
2.93
0.88
70T
aza
Cup
0.09
940.
4493
3.24
1.2
3.65
1.35
3.75
1.04
4.4
00.
832.
420.
891.
780.
523.
681.
23.
271.
5371
Cie
rvo
Dee
r0.
5679
1.44
764
.24
0.85
3.72
1.05
1.51
0.88
2.22
1.21
3.61
0.87
3.55
0.77
4.47
0.61
1.93
0.8
72E
scri
tori
oD
esk
0.14
450.
3295
2.3
\.1
43.
181.
394.
80.
494.
320.
902.
58I
3.05
0.84
3.74
1.05
2.13
0.74
73P
erro
Dog
0.00
100
0.00
100
3.4
\.I
3.05
1.26
41.
224.
600.
702.
880.
913.
380.
734.
261.
053.
41.
3574
Muf
ieca
Dol
l0.
3365
1.42
712.
371.
212.
281.
072.
781.
352.
92\.
14
3.85
0.98
4.12
0.93
3.11
1.59
3.4
0.83
75B
urro
Don
key
0.18
810.
8786
4.08
0.87
3.48
1.00
1.88
0.97
1.88
0.87
3.51
0.95
3.35
0.69
4.16
1.21
2.33
\.II
76P
uert
aD
oor
0.14
920.
1698
3.4
1.38
3.80
0.87
2.75
1.29
4.68
0.79
2.05
0.88
3.22
0.69
4.11
0.99
3.33
1.23
77Po
rno
Doo
rkno
b1.
9219
0.38
903.
901.
004.
31.
074.
250.
922.
240.
882.
680.
6178
Ves
tido
Dre
ss0.
1192
0.00
100
2.52
1.06
2.30
1.08
3.76
1.35
3.62
1.46
2.14
0.8
2.65
0.65
2.84
1.38
3.6
0.99
79C
orno
daD
ress
er2.
2021
2.55
363.
220.
964.
121.
014.
520.
772.
640.
892.
950.
8980
Tam
bor
Dru
m0.
0597
0.00
983.
781.
053.
711.
054.
041.
062.
60\.
16
2.22
0.87
2.88
0.75
4.68
0.58
2.4
\.1
881
Pato
Duc
k0,
0498
0.28
953.
980.
953.
850.
942.
411.
222.
75\.
II3.
170.
723.
320.
824.
790.
542.
870.
9282
Agu
ila
Eag
le0.
1282
\.1
476
3.4
1.23
3.49
1.26
1.82
1.03
2.42
1.30
4.1
0.8
4.18
0.74
4.26
1.19
2.4
1.24
83O
reja
Ear
0.12
940.
2895
4.42
0.74
4.26
0,93
4.59
0.98
4.5
00.
702.
710.
972.
680.
824.
371.
122.
20.
9484
Ele
fant
eE
leph
ant
0.00
100
0.00
100
4.16
0.83
3.85
0.99
1.43
0.78
2.35
1.04
4.15
0.93
4.12
0.78
4.84
0.37
1.8
0.86
85S
obre
Env
elop
e0.
1492
0.16
983.
931.
074
.70
0.64
4.75
0.69
4.12
0.93
2.19
0.8
1.42
0.59
4.05
1.39
2.73
\.I
86O
joE
ye0.
0010
00.
1698
4.05
0.97
4.15
0.88
4.84
0.54
4.88
0.40
31.
083.
48\.
10
4.47
0.84
3.27
\.1
687
Val
laFe
nce
0.13
660.
9474
3.53
1.15
3.80
1.44
2.76
1.31
3.02
1.06
4.08
0.77
2.55
1.00
3.58
1.22
2.53
1.06
AP
PE
ND
IXA
(Con
tinu
ed)
Nam
eA
gree
men
tIm
age
Agr
eem
ent
Fam
ilia
rity
Vis
ual
Com
plex
ity
Pic
-Nam
eA
gree
men
tV
aria
bili
tyS
pani
shE
ngli
shS
pani
shE
ngli
shS
pani
shE
ngli
shS
pani
shE
ngli
sh(S
pani
sh)
(Spa
nish
)
Spa
nish
Eng
lish
H%
H%
MSD
MSD
MSD
MSD
MSD
MSD
MSD
MSD
88D
edo
Fin
ger
0.18
771.
3771
3.65
1.17
4.60
0.66
4.75
0.8
4.78
0.79
2.39
0.97
2.30
0.95
4.42
1.26
2.67
1.45
89Pe
zFi
sh0.
1192
0.00
100
3.62
0.95
3.58
1.05
3.27
1.27
3.28
1.22
3.34
0.9
3.75
1.02
4.32
1.06
3.53
1.13
90B
ande
raFl
ag0.
1390
0.32
953.
561.
243.
221.
192.
121.
132.
901.
281.
420.
591.
880.
463.
741.
453.
271.
1691
Flo
rF
low
er0.
0010
00.
4893
3.08
1.12
3.25
1.01
3.71
1.04
3.88
1.19
2.71
0.91
3.25
0.94
4.11
1.33
3.67
1.4
92F
laut
aF
lute
0.98
440.
6188
2.27
1.25
3.41
1.30
2.03
I2.
451.
222.
580.
814.
150.
852.
791.
232.
530.
9993
Mos
caFl
y0.
0695
1.15
763.
581.
243.
221.
332.
981.
413.
021.
064
0.93
4.10
0.92
3.58
1.43
2.47
1.06
94Pi
eFo
ot0.
0010
00.
2895
4.16
1.01
4.42
0.86
4.71
0.88
4.78
0.69
2.17
0.97
2.18
0.89
4.26
0.87
2.33
1.23
95B
alon
deru
gby
Foo
tbal
l0.
1242
0.00
100
4.33
0.72
4.18
0.92
1.65
1.04
3.55
1.24
2.25
0.8
2.28
0.71
4.84
0.5
1.2
0.41
96C
asco
Foo
tbal
lhe
lmet
0.16
660.
9562
2.56
1.32
4.38
0.76
2.13
1.33
3.15
1.24
2.86
0.82
2.98
0.69
3.53
1.39
2.87
1.06
97T
ened
orFo
rk0.
0010
00.
0010
03.
641.
084.
150.
854.
351.
054.
780.
473.
370.
872.
620.
943.
741.
633.
131.
4698
Zor
roFo
x0.
0797
1.27
744.
150.
973.
491.
201.
670.
971.
950.
843.
490.
864.
020.
854.
420.
772.
330.
999
Tro
mb6
nF
renc
hho
rn0.
1724
1.67
573.
131.
373.
731.
361.
741.
192.
001.
052.
91.
014.
300.
873.
371.
121.
730.
8810
0R
ana
Frog
0.21
860.
0010
04.
150.
843.
601.
022.
571.
392.
481.
052.
150.
933.
421.
054.
370.
682.
130.
8310
1S
arte
nF
ryin
gpa
n0.
0994
1.18
603.
550.
983.
920.
934.
330.
894.
150.
963.
121.
122.
050.
674.
051.
272.
270.
8810
2C
ubo
deba
sura
Gar
bage
bin
1.21
650.
7688
3.48
1.21
4.52
0.74
3.76
1.11
4.08
1.10
2.93
0.87
2.58
0.74
4.21
1.08
1.73
0.96
103
Jira
faG
iraf
fe0.
0010
00.
3295
4.45
0.94
4.48
0.81
1.53
0.99
1.80
0.95
4.42
0.77
4.65
0.73
4.74
0.56
1.87
1.19
104
Vas
oG
lass
0.00
100
0.16
983.
921.
254.
401.
002.
351.
134.
780.
521.
950.
861.
820.
744
1.05
31.
1310
5G
afas
Gla
sses
0.00
100
1.07
643.
621.
113.
810.
943.
511.
744.
001.
302.
20.
742.
850.
854.
051.
222.
81.
0810
6G
uant
eG
love
0.11
920.
1698
3.62
1.01
3.65
1.20
3.94
1.24
3.38
1.06
2.44
0.93
3.02
0.76
3.79
1.13
2.73
1.03
107
Cab
raG
oat
0.42
810.
7786
3.91
1.03
3.46
1.26
1.65
0.89
1.92
1.06
3.14
0.88
3.18
0.77
3.95
1.39
2.87
0.99
108
Gor
ila
Gor
illa
0.29
550.
7976
3.82
1.06
3.58
1.07
1.59
1.06
2.05
1.18
3.69
0.93
3.62
0.86
4.37
0.9
2.47
1.06
""0
109
Uva
sG
rape
s0.
3045
0.38
904.
081.
264.
310.
792.
311.
143.
651.
042.
140.
963.
000.
924.
580.
611.
930.
7..... ("
J11
0S
alta
mon
tes
Gra
ssho
pper
0.55
821.
4771
3.75
1.0
2.
3.55
1.30
3.59
1.12
2.42
1.07
2.24
0.9
4.40
0.80
3.95
1.27
2.27
0.8
>-l
111
Gui
tarr
aG
uita
r0.
0010
00.
1698
4.67
0.55
4.20
1.21
3.14
1.36
3.58
1.09
2.78
0.95
4.00
0.92
4.16
1.38
3.07
1.22
C11
2P
isto
laG
un0.
1987
1.09
744.
020.
933.
851.
051.
511.
012.
681.
193.
170.
873.
520.
814.
161.
173.
21.
32~ tT
l11
3Pe
loH
air
0.83
710.
6490
2.02
1.18
2.71
1.18
4.31
1.14
-4.5
90.
742.
530.
92.
880.
783.
261.
373.
61.
24ti
l
114
Mar
till
o·
Ham
mer
0.03
970.
0010
03.
041.
144.
101.
022.
651.
323.
481.
162.
360.
912.
600.
703.
581.
432.
271.
03ti
l
115
Man
oH
and
0.00
100
0.44
934.
160.
794.
300.
904.
880.
434.
820.
672.
780.
982.
980.
914.
321
2.53
1.19
~11
6P
erch
aH
ange
r0.
0010
00.
7486
3.89
1.19
4.73
0.55
4.35
0.96
4.52
0.67
1.17
0.59
1.20
0.56
4.53
0.51
2.33
0.9
Z11
7A
rpa
Har
p0.
0987
0.00
934.
280.
744.
281.
061.
631.
041.
881.
084.
080.
884.
050.
814.
740.
451.
871.
25~
118
Som
brer
oH
at0.
1095
0.16
984.
051.
333.
651.
224.
670.
653.
181.
001.
20.
612.
350.
794.
321.
162.
531.
68~
119
Cor
azon
Hea
rt0.
0010
00.
0010
04.
281.
314.
490.
984.
140.
983.
721.
161.
050.
221.
000.
003.
741.
193.
131.
190
120
Hel
icop
tero
Hel
icop
ter
0.Q
498
0.32
953.
950.
913.
420.
971.
670.
912.
551.
123.
810.
943.
800.
954.
321.
162.
271.
33-N
121
Cab
allo
Hor
se0.
0010
00.
0010
04.
160.
924.
200.
812.
571.
33.
551.
143.
630.
933.
820.
704.
470.
72.
871.
19tT
l12
2C
asa
Hou
se0.
1387
0.32
952.
841.
132.
651.
114.
361.
064.
381.
043.
361.
033.
900.
943.
421.
53.
531.
550
123
Pla
ncha
Iron
0.00
100
0.32
953.
091.
064.
080.
783.
611.
133.
651.
083.
150.
893.
250.
893.
741.
242.
130.
92'T
j
012
4T
abla
depl
anch
arIr
onin
gbo
ard
0.17
530.
5683
3.52
1.21
4.40
0.77
2.63
1.37
3.50
1.07
2.25
0.71
2.05
0.63
4.11
0.74
3.33
1.45
~12
5C
haqu
eta
Jack
et1.
3953
0.95
812.
281.
252.
220.
914.
490.
74.
001.
143.
030.
953.
250.
803.
111.
333.
130.
92ti
l12
6C
angu
roK
anga
roo
0.05
970.
0010
04.
40.
814.
300.
751.
310.
761.
921.
153.
860.
863.
980.
884.
840.
372
0.76
~12
7T
eter
aK
ettle
0.18
711.
6640
4.02
1.32
3.31
1.11
4.8
0.4
3.80
1.17
2.2
0.94
2.40
0.74
4.79
0.54
1.8
0.77
Z12
8L
lave
Key
0.00
100
0.00
100
2.96
1.26
4.58
0.74
4.65
0.91
4.85
0.42
2.19
0.86
1.92
0.76
3.84
1.3
3.4
1.35
..... til
129
Com
eta
Kite
0.00
100
0.00
100
4.4
0.66
4.10
1.00
1.92
1.02
2.48
1.14
2.44
0.88
2.85
0.69
4.37
0.76
31.
07::r:
130
Cuc
hill
oK
nife
0.13
940.
6090
3.04
1.15
3.25
1.32
4.69
0.68
4.45
0.84
1.49
0.65
1.92
0.68
3.95
1.31
2.73
0.96
131
Esc
aler
aL
adde
r0.
1095
0.16
982.
841.
573.
751.
143.
041.
093.
351.
152.
080.
932.
320.
613.
321.
252.
80.
8613
2L
ampa
raL
amp
0.00
100
0.44
932.
271.
33.
260.
903.
920.
984.
200.
951.
690.
771.
850.
613.
421.
023.
271.
28V
1~
133
Hoj
aL
eaf
0.09
940.
5390
3.6
1.31
3.88
1.12
3.96
1.15
4.30
0.75
2.42
12.
520.
774
13.
81.
08V
1
AP
PE
ND
IXA
(Con
tin
ued
)V
l.j:
:o.
Pic
-Nam
e0'
1N
ame
Agr
eem
ent
Imag
eA
gree
men
tF
amil
iari
tyV
isua
lC
ompl
exit
yA
gree
men
tV
aria
bili
tyS
pani
shE
ngli
shS
pani
shE
ngli
shS
pani
shE
ngli
shS
pani
shE
ngli
sh(S
pani
sh)
(Spa
nish
)V
JS
pani
shE
ngli
shH
%H
%M
SDM
SDM
SDM
SDM
SDM
SDM
SDM
SD>
134
Pie
rna
Leg
0.38
901.
111.
543.
641.
054.
650.
84.
650.
822.
150.
742.
550.
842.
841.
32.
531.
13Z
813.
54..
"13
5L
imon
Lem
on0.
0010
00.
0010
04.
580.
924.
350:
943.
710.
943.
251.
221.
370.
641.
850.
694.
840.
51.
731.
03rn t'"
'13
6L
eopa
rdo
Leo
pard
1.17
341.
0776
3.87
1.07
3.68
1.03
1.39
0.83
1.92
0.93
4.25
0.88
4.28
0.81
4.42
0.77
1.8
0.94
-13
7L
echu
gaL
ettu
ce1.
3845
1.14
742.
641.
273.
051.
203.
391.
223.
421.
243.
361.
163.
480.
923.
261.
332.
40.
83e
138
Bom
bill
aL
ight
bulb
0.00
100
0.68
864.
650.
624.
420.
834.
430.
854.
180.
802.
310.
952.
750.
944.
471.
022.
21.
01> Z
139
Inte
rrup
tor
Lig
htsw
itch
0.95
660.
9267
31.
394.
620.
624.
560.
844.
580.
632.
140.
882.
520.
773.
681.
452.
331.
050
140
Leo
nL
ion
0.00
100
0.37
933.
951.
033.
881.
031.
531.
012.
001.
073.
80.
944.
300.
874.
370.
762.
20.
94..
"14
1L
abio
sL
ips
0.16
840.
4493
4.25
0.84
4.1
00.
944.
760.
594.
500.
811.
360.
641.
850.
883.
891.
22.
871.
19tT
l14
2C
angr
ejo
Lob
ster
0.19
370.
3890
2.45
1.39
3.62
1.35
2.18
1.26
2.58
1.24
3.85
0.93
4.48
0.81
3.37
1.01
2.73
1.28
:;>;::l Z
143
Can
dado
Loc
k0.
1984
0.53
884.
560.
693.
511.
402.
781.
253.
181.
182
0.81
2.22
0.69
4.42
1.17
2.27
0.7
>14
4M
anop
laM
itte
n0.
3266
0.96
764.
081.
173.
820.
963.
391.
423.
101.
222.
030.
832.
350.
694.
261.
281.
40.
63Z
145
Mon
oM
onke
y0.
1390
0.32
953.
211.
083.
121.
051.
570.
92.
580.
973.
710.
873.
900.
703.
681.
453.
21.
010
146
Lun
aM
oon
0.00
100
1.68
624.
111.
133.
151.
584.
20.
963.
981.
011.
140.
471.
020.
163.
681.
23.
271.
16tT
lN
147
Mot
oM
otor
cycl
e0.
1889
0.32
953.
21.
283.
641.
053.
371.
343.
251.
094.
560.
734.
780.
473.
471.
263.
21.
0814
8M
onta
naM
ount
ain
0.17
690.
6090
3.65
1.02
3.52
1.12
3.12
1.32
2.70
1.19
2.61
1.13
2.80
1.05
3.63
1.07
3.33
1.23
149
Rat
onM
ouse
0.26
790.
7579
4.35
0.73
4.22
0.91
2.86
1.28
2.45
1.02
2.9
0.99
3.28
0.87
4.26
1.28
3.2
1.32
150
Seta
Mus
hroo
m0.
1895
0.00
983.
870.
863.
781.
111.
530.
952.
881.
233.
711.
033.
120.
714.
051.
132.
871.
3615
1C
lavo
Nai
l1.
2145
0.16
984.
241.
054.
730.
622.
551.
213.
281.
201.
220.
491.
800.
684.
580.
612.
531.
0615
2L
ima
Nai
l-fi
le0.
8374
1.04
673.
151.
13.
561.
262.
841.
283.
151.
391.
980.
923.
181.
004
0.94
2.6
1.3
153
Col
lar
Nec
klac
e0.
1190
1.88
604.
410.
843.
321.
493.
121.
232.
701.
311.
590.
811.
780.
883.
581.
263
0.93
154
Agu
jaN
eedl
e0.
3292
0.86
814.
70.
54.
421.
143.
081.
343.
401.
141.
220.
621.
550.
744.
740.
732.
81.
0815
5N
ariz
No
se0.
0010
00.
1698
4.09
0.93
3.62
1.18
4.69
0.84
4.52
0.87
1.73
0.85
1.60
0.92
4.37
1.16
2.67
0.9
156
Tue
rca
Nut
0.21
690.
9764
4.48
0.75
3.62
1.65
1.75
0.98
2.55
1.28
3.64
0.96
2.30
0.56
4.95
0.23
2.33
1.11
157
Ceb
olla
Oni
on0.
0695
0.00
954.
241.
013.
900.
803.
290.
993.
321.
312.
360.
912.
850.
964.
211.
032.
471.
315
8N
aran
jaO
rang
e0.
4984
0.53
814.
330.
984
.00
1.07
3.82
1.21
3.34
1.26
1.58
0.72
2.12
0.71
3.74
1.24
2.13
1.06
159
Ave
stru
zO
stri
ch0.
4971
0.35
864.
20.
923.
321.
031.
410.
831.
520.
673.
390.
933.
700.
814.
530.
611.
730.
8816
0B
uho
Ow
l0.
1987
0.00
100
3.62
0.97
4.1
00.
921.
821.
112.
221.
063.
91.
034.
220.
724.
630.
762.
671.
1816
1P
ince
lP
aint
brus
h0.
0797
1.06
743.
251.
042.
921.
592.
551.
062.
781.
241.
810.
842.
580.
953.
631.
382
0.65
162
Pan
talo
nPa
nts
0.21
840.
5388
3.33
1.16
3.60
0.92
4.88
0.33
4.55
0.86
1.98
0.82
2.22
0.70
3.68
1.49
3.33
1.11
163
Pavo
real
Pea
cock
0.07
921.
1974
4.05
0.97
3.28
1.28
1.73
0.85
2.90
1.02
4.47
0.92
2.55
0.81
4.47
0.84
2.07
1.03
164
Mel
ocot
onPe
ach
0.68
76I
0.81
793.
980.
853.
640.
973.
651.
142.
051.
051.
490.
654.
750.
434.
320.
892.
071.
116
5C
acah
uete
Pea
nut
0.32
87
'0.
3793
4.49
0.74
4.3
00.
982.
841.
053.
001.
022
0.89
2.82
0.95
4.47
0.77
1.4
0.63
166
Pera
Pear
0.00
100
0.00
100
4.67
0.51
4.62
0.62
4.02
1.05
3.55
1.14
1.19
0.43
1.15
0.36
4.53
0.7
2.27
1.1
167
Bol
igra
foPe
n0.
2274
0.32
953.
251.
243.
221.
044.
940.
314.
780.
722.
170.
913.
150.
943.
891.
452.
871.
4616
8L
apiz
Penc
il0.
1684
0.00
100
4.2
0.73
4.40
0.80
4.9
0.3
4.42
1.00
1.68
0.8
2.32
0.91
4.47
0.77
2.47
0.99
169
Pin
guin
oP
engu
in0.
0010
00.
3890
4.42
0.9
3.22
1.15
1.39
0.75
1.70
0.93
2.58
0.88
2.82
0.70
4.37
0.9
2.8
1.21
170
Pim
ient
oP
eppe
r0.
1482
1.07
673.
221.
133.
641.
283.
061.
292.
921.
292.
050.
782.
480.
954
1.41
2.33
1.23
171
Pia
noP
iano
0.10
950.
7081
4.43
0.91
4.02
1.06
2.16
1.16
3.42
1.48
4.6
90.
74.
580.
774.
740.
562.
871.
1917
2C
erdo
Pig
0.07
920.
6090
4.04
1.02
3.62
1.04
2.27
1.17
2.18
0.97
3.17
0.87
3.00
0.81
4.68
0.58
2.67
0.82
173
Pina
Pin
eapp
le0.
0010
00.
0010
04.
351.
034.
600.
622.
691.
092.
951.
303.
951.
074.
351.
014.
210.
922.
60.
9117
4Pi
paPi
pe0.
0498
0.16
984.
30.
864.
261.
001.
610.
942.
901.
141.
710.
71.
880.
714.
110.
993.
071.
1617
5Ja
rra
Pit
cher
0.11
890.
5488
3.73
1.08
3.62
0.84
3.98
1.22
3.50
0.92
1.81
0.63
1.85
0.57
4.21
0.85
2.93
1.22
176
Ali
cate
sP
lier
s1.
2032
0.38
883.
671.
134.
221.
152.
391.
173.
381.
132.
20.
712.
200.
604.
051.
311.
671.
1117
7E
nchu
fePl
ug0.
1184
0.29
883.
31.
424.
081.
024.
221.
054.
180.
772.
070.
892.
250.
703.
321.
292.
41.
0617
8B
olso
Poc
ketb
ook
0.07
951.
7257
2.93
1.12
3.05
0.92
3.86
1.23
3.95
1.28
2.61
0.97
2.70
0.78
3.53
1.35
3.33
1.11
179
Caz
oPo
t0.
2074
0.86
813.
391.
43.
560.
984
.06
1.07
4.22
0.96
1.85
0.81
2.22
0.69
3.84
1.3
2.6
1.06
AP
PE
ND
IXA
(Con
tin
ued
)
Nam
eA
gree
men
tIm
age
Agr
eem
ent
Fam
ilia
rity
Vis
ual
Com
plex
ity
Pic
-Nam
eA
gree
men
tV
aria
bilit
ySp
anis
hE
nglis
hS
pani
shE
nglis
hS
pani
shE
ngli
shS
pani
shE
ngli
sh(S
pani
sh)
(Spa
nish
)
Spa
nish
Eng
lish
H%
H%
MSD
MSD
MSD
MSD
MSD
MSD
MSD
MSD
180
Pata
taPo
tato
0.11
950.
3490
4.3
0.9
3.97
1.14
4.15
1.06
3.46
1.17
1.36
0.66
2.20
1.10
3.89
1.05
2.33
0.72
181
Cal
abaz
aP
umpk
in0.
2587
0.00
984.
240.
924.
181.
182.
261.
243.
081.
352.
120.
792.
600.
704.
530.
72.
470.
9218
2C
onej
oR
abbi
t0.
0010
00.
0010
04.
130.
94.
200.
812.
311.
292.
951.
073.
311.
023.
280.
844.
790.
712.
871.
1318
3M
apac
heR
acco
on1.
0832
0.58
793.
11.
373.
081.
081.
27.0
.72.
201.
234.
250.
824.
400.
834.
260.
871.
40.
6318
4T
ocad
isco
sR
ecor
dpl
ayer
0.49
821.
7350
4.38
0.93
3.35
1.22
3.86
1.02
4.40
0.86
2.05
0.84
3.32
0.93
50
2.6
1.12
185
Fri
gori
fico
Ref
rige
rato
r0.
3553
0.44
934.
130.
823.
851.
134.
410.
944.
680.
652.
070.
832.
200.
604.
110.
882
1.07
186
Rin
ocer
onte
Rhi
noce
ros
0.20
940.
5683
3.95
1.34
3.84
0.93
4.73
0.8
1.52
0.89
30.
814.
150.
853.
161.
53.
331.
2918
7A
nillo
Rin
g0.
2990
0.16
983.
271.
393.
080.
963.
631.
383.
481.
281.
640.
82.
550.
803.
371.
263.
071.
118
8M
eced
ora
Roc
king
chai
r0.
9671
0.53
903.
651.
234.
120.
952.
451.
273.
251.
303.
780.
933.
580.
924.
261.
052.
130.
8318
9Pa
tinR
olle
rsk
ate
0.09
921.
0052
3.75
1.33
3.48
1.36
2.2
1.06
2.25
1.11
3.95
0.75
4.08
0.93
3.95
1.35
2.07
0.8
190
Rod
illo
Rol
ling
pin
0.83
400.
9471
4.43
0.84
4.44
0.96
1.27
0.67
2.22
1.08
4.02
0.92
1.52
0.50
4.58
0.61
1.53
0.83
191
Gal
loR
oost
er0.
5381
1.21
764.
360.
854.
080.
902.
451.
222.
221.
083.
760.
94.
120.
904.
580.
612.
61.
0619
2R
egIa
Rul
er0.
1497
0.16
984.
021.
13.
981.
042.
311.
243.
580.
952.
610.
871.
850.
944.
211.
082.
531.
1919
3B
arco
Sai
lboa
t0.
3039
0.37
932.
351.
143.
250.
992.
11.
192.
921.
173.
730.
873.
580.
923.
051.
183.
331.
419
4Sa
lero
Sal
tsha
ker
0.18
950.
9683
3.42
1.55
4.00
1.12
2.1
1.17
4.18
0.92
2.51
0.99
3.00
0.92
3.21
1.58
3.27
1.1
195
Sand
wic
hS
andw
ich
0.52
840.
0010
02.
71.
43.
550.
973.
271.
224.
450.
971.
660.
863.
420.
862.
741.
332
1.07
196
Sie
rra
Saw
0.18
790.
1698
4.24
0.79
4.55
0.77
2.27
1.25
2.92
1.19
2.46
0.84
2.25
0.62
4.11
0.99
3.13
1.19
197
Tije
ras
Sci
ssor
s0.
1294
0.16
984.
060.
944.
400.
831.
430.
883.
980.
994.
530.
842.
150.
654.
051.
132.
070.
8819
8T
orni
lloSc
rew
0.58
820.
3393
4.4
0.89
3.67
0.89
3.65
1.26
3.20
1.00
1.66
0.73
3.25
0.99
4.68
0.48
2.53
1.25
199
Des
torn
illa
dor
Scr
ewdr
iver
0.29
820.
0098
4.15
0.93
4.30
0.64
2.27
1.22
3.42
1.14
2.12
0.91
2.35
0.73
4.37
1.01
1.93
1.03
200
Cab
alli
tode
mar
Sea
hors
e0.
8255
0.34
884.
1I
0.95
3.58
1.22
1.33
0.77
1.50
0.89
4.17
0.91
4.50
0.71
4.47
0.9
1.33
0.49
'"e20
1Fo
caSe
al0.
1389
0.61
883.
51.
063.
181.
061.
350.
741.
620.
732.
860.
82.
900.
743.
951.
352.
21.
15..... (J
202
Ove
jaSh
eep
0.30
900.
9567
3.56
1.08
3.00
1.11
2.28
1.21
1.85
0.82
3.46
0.9
3.80
0.75
3.68
1.42
2.53
1.19
.....,
203
Cam
isa
Shir
t0.
0797
0.00
100
3.74
1.18
3.86
0.98
4.69
0.73
4.56
0.70
2.85
0.89
3.08
0.79
3.37
1.2
I3.
21.
37C
204
Zap
ato
Shoe
0.00
100
0.28
953.
21.
253.
021.
264.
750.
84.
620.
703.
170.
873.
380.
863.
531.
543.
271.
39i':
ltT
l20
5Fa
lda
Skir
t0.
0597
0.16
982.
911.
173.
281.
103.
751.
443.
641.
531.
360.
641.
400.
583.
261.
733
Itr
:
206
Mof
eta
Skun
k0.
7261
0.16
983.
441.
253.
401.
091.
270.
782.
301.
174.
411.
084.
720.
744.
320.
891.
871.
06r.n
207
Tri
neo
Sled
0.59
570.
0098
3.2
1.31
4.49
0.81
3.75
1.13
2.80
1.03
4.73
0.52
3.05
0.84
41.
23.
130.
99~
208
Car
acol
Snai
l0.
0298
0.51
864.
270.
763.
331.
182.
141.
081.
851.
062.
660.
843.
400.
804.
211.
032.
271.
28Z
209
Ser
pien
teSn
ake
0.16
840.
1698
3.6
1.08
3.54
1.01
3.82
1.18
1.90
1.04
1.81
0.8
4.52
0.81
3.84
1.3
1.8
0.86
~21
0M
uiie
code
niev
eS
now
man
0.06
950.
0010
03.
641.
014.
000.
952.
161.
143.
151.
042.
050.
92.
520.
593.
841.
121.
930.
8i':
l21
1C
alce
tin
Sock
0.00
100
0.00
100
3.98
0.93
3.72
1.00
4.51
0.86
4.52
0.84
1.61
0.67
1.62
0.62
4.21
1.13
3.13
1.36
021
2A
raiia
Spi
der
0.D
794
0.61
883.
241.
252.
951.
162.
271.
222.
281.
103.
191.
173.
680.
853.
791.
323.
270.
88..... N
213
Spi
nnin
gw
heel
unav
aila
ble
tTl
214
Hilo
Spoo
lo
fthr
ead
1.86
291.
5455
3.80
1.44
3.25
1.16
3.12
1.14
3.73
I3.
180.
970
215
Cuc
hara
Spoo
n0.
1690
0.16
984.
180.
94.
101.
114.
670.
774.
500.
891.
860.
862.
020.
824.
580.
841.
80.
68"T
l0
216
Ard
illa
Squ
irre
l0.
0298
0.17
934.
420.
694.
420.
891.
530.
73.
820.
893.
290.
893.
750.
974.
530.
72.
21.
32i':
l21
7E
stre
lla
Star
0.00
100
0.00
100
4.4
0.97
4.41
1.10
3.02
1.35
3.35
1.33
1.19
0.51
1.05
0.22
41.
333.
670.
98r.n
218
Tab
uret
eSt
ool
1.04
680.
1698
4.05
1.13
4.12
1.08
3.25
1.35
3.08
1.13
1.85
0.78
2.32
0.72
4.53
0.7
I0
~21
9C
ocin
aSt
ove
0.12
731.
1276
2.73
1.22
4.10
1.00
4.02
1.19
4.65
0.65
3.83
0.85
4.02
0.94
2.95
1.39
2.47
0.99
Z22
0Fr
esa
Str
awbe
rry
0.00
100
0.17
903.
931.
033.
981.
043.
371.
183.
201.
292.
471.
023.
380.
913.
741.
372.
671.
11..... r.n
221
Mal
eta
Sui
tcas
e0.
0597
1.01
793.
211.
012.
981.
174.
081.
043.
650.
913.
190.
843.
600.
864.
050.
973.
131.
06::I
:22
2So
lSu
n0.
0498
0.00
100
3.22
1.24
4.22
1.08
4.65
0.74
4.90
0.30
2.32
0.94
1.20
0.46
3.68
1.42
2.4
0.99
223
Cis
neSw
an0.
3390
0.64
884.
340.
93.
690.
721.
960.
981.
970.
833.
070.
913.
050.
804.
790.
421.
930.
8822
4Je
rsey
Swea
ter
0.05
970.
9883
3.66
0.98
2.78
1.11
4.94
0.24
4.48
0.74
2.73
0.96
2.90
0.77
3.84
1.17
3.13
1.6
Vl
.j::..
225
Col
umpi
oSw
ing
0.05
970.
1795
3.48
1.41
4.15
0.92
2.48
1.2
3.02
1.24
1.58
0.72
2.72
0.97
4.05
1.18
1.73
0.8
-.l
AP
PE
ND
IXA
(Con
tinu
ed)
VI~
Pic
-Nam
e0
0
Nam
eA
gree
men
tIm
age
Agr
eem
ent
Fam
ilia
rity
Vis
ual
Com
plex
ity
Agr
eem
ent
Var
iabi
lity
Spa
nish
Eng
lish
Spa
nish
Eng
lish
Spa
nish
Eng
lish
Spani~
Eng
lish
(Spa
nish
)(S
pani
sh)
trx
Spa
nish
Eng
lish
H%
H%
MSD
MSD
MSD
MSD
MSD
MSD
MSD
MSD
::>22
6M
esa
Tab
le0.
Q4
9895
4.27
1.03
3.42
1.36
4.78
0.67
4.35
0.88
1.41
0.67
1.72
0.77
4.42
0.9
3.27
1.33
Z0.
32..
"22
7T
elef
ono
Tel
epho
ne0.
0010
00.
5986
4.1
I0.
964.
281.
164.
670.
794.
800.
511.
590.
653.
520.
974.
211.
182.
871.
06tT
lt'"
'22
8T
elev
isio
nT
elev
isio
n0.
2845
1.46
523.
61.
244.
000.
824.
650.
774.
820.
383.
440.
973.
220.
964.
470.
73.
21.
08.....
.22
9R
aque
taT
enni
sra
cket
0.00
100
0.62
864.
270.
784.
620.
582.
021.
243.
621.
303.
190.
973.
250.
944.
580.
772.
731.
1C
230
Ded
alT
him
ble
0.11
950.
6383
4.34
0.85
4.26
0.93
2.55
1.32
2.48
1.12
2.22
0.89
3.35
0.82
4.47
0.84
1.53
1.25
~23
1D
edo
Thu
mb
1.31
440.
1698
3.44
1.33
4.48
0.63
4.61
1.06
4.72
0.74
1.97
0.85
2.38
0.97
3.95
1.47
2.2
1.32
t:I23
2C
orba
taT
ie0.
0010
00.
8969
4.19
1.03
4.05
0.94
2.55
1.25
3.80
1.03
2.32
0.73
2.90
0.80
4.53
0.61
2.53
1.25
.."
233
Tig
reT
iger
0.40
900.
3393
3.06
1.28
3.82
1.14
3.24
1.39
2.10
0.92
2.46
0.88
4.62
0.80
3.84
1.38
2.33
1.11
tTl
234
Tos
tado
rT
oast
er1.
2344
0.00
100
3.95
1.1
3.92
0.79
2.27
1.08
4.08
0.90
2.68
1.11
2.78
0.85
4.21
0.71
20.
76~
235
Pie
Toe
0.21
361.
5755
3.55
1.49
4.18
0.83
4.73
0.7
4.48
0.81
1.95
0.75
1.98
0.82
4.53
0.61
2.8
1.01
::>23
6T
omat
eT
omat
o0.
0498
0.80
883.
821.
24.
051.
124.
041.
023.
781.
063.
581.
021.
980.
573.
421.
542.
530.
99Z
237
Cep
illo
dedi
ente
sT
ooth
brus
h0.
1987
0.16
983.
051.
764.
400.
744.
780.
644.
620.
731.
920.
72.
420.
773.
211.
42.
870.
99t:I tT
l23
8Pe
onza
Top
0.10
810.
6886
4.\
10.
883.
461.
051.
961.
041.
880.
982.
491.
022.
650.
824.
470.
91.
731.
1N
239
Tra
ffic
light
unav
aila
ble
240
Tre
nT
rain
0.07
950.
7486
3.42
1.38
3.20
1.38
3.06
1.33
4.15
0.88
3.05
1.02
4.32
0.88
4.11
1.2
1.93
0.8
241
Arb
olT
ree
0.05
970.
0010
03.
891.
183.
521.
004.
061.
084.
680.
613.
951.
113.
700.
814.
260.
873.
81.
0124
2C
amio
nT
ruck
0.04
980.
5390
3.15
1.19
2.80
1.10
3.43
1.28
4.02
0.91
2.59
1.08
2.75
0.86
4.11
1.2
2.6
1.3
243
Tro
mpe
taT
rum
pet
0.13
941.
1079
2.89
1.42
1.53
0.9
2.60
1.26
4.19
1.03
3.58
0.92
244
Tur
tleun
avai
labl
e24
5P
arag
uas
Um
brel
la0.
0298
0.00
100
4.04
1.1
3.92
0.90
3.47
1.12
3.95
0.92
2.85
0.87
3.00
1.05
4.42
1.07
2.47
1.06
246
Jarr
onV
ase
0.07
940.
3295
2.69
1.13
2.72
1.02
2.94
1.19
2.78
1.26
2.93
1.03
3.15
0.66
40.
942.
81.
2\24
7C
hale
coV
est
0.06
950.
1698
3.8
1.0\
3.70
1.10
3.76
1.12
3.48
1.05
2.46
0.73
2.60
0.74
4.37
0.6
2.4
0.91
248
Vio
linV
iolin
0.51
840.
7286
4.42
0.83
4.18
1.05
1.82
1.13
2.68
1.21
4.37
0.69
4.10
0.86
4.74
0.56
2.13
1.25
249
Car
rito
Wag
on0.
1919
0.92
793.
561.
631.
59I
2.50
1.22
3.34
0.98
3.35
0.91
250
Rel
ojW
atch
0.13
860.
4590
2.33
1.35
3.18
1.07
4.41
0.94
4.58
0.73
2.12
0.72
3.40
1.04
3.84
1.3
3.2
1.32
251
Reg
ader
aW
ater
ing
can
0.04
982.
0355
4.13
0.98
4.08
0.98
2.08
1.21
2.72
1.50
3.31
1.04
2.78
0.79
4.47
0.84
3.2
0.94
252
San
dia
Wat
erm
elon
0.16
530.
5586
3.31
1.37
2.85
1.31
21.
113.
051.
094.
390.
832.
280.
924.
111.
292.
130.
9225
3W
ell
unav
aila
ble
254
Rue
daW
heel
0.11
920.
3393
3.84
1.57
3.48
1.36
2.\
61.
212.
221.
041.
460.
652.
420.
834.
420.
691.
931.
0325
5S
ilba
toW
hist
le0.
2174
0.00
100
4.43
0.93
4.55
0.67
2.02
1.21
2.45
0.92
1.78
0.72
2.55
0.84
4.26
1.15
2.53
1.06
256
Mol
ino
Win
dmil
l0.
0994
0.16
983.
391.
113.
351.
081.
651.
051.
801.
004.
660.
734.
620.
763.
951.
392.
271.
0325
7V
enta
naW
indo
w0.
0498
0.32
952.
151.
073.
250.
834.
410.
834.
400.
862.
921.
093.
18
0.86
3.37
1.38
2.53
1.13
258
Cop
aW
ineg
lass
0.04
981.
4350
3.72
1.07
3.31
1.35
4.1
1.04
4.02
1.1\
1.71
0.79
1.85
0.48
4.26
1.15
3.33
1.18
259
LIa
vein
gles
aW
renc
h1.
0652
0.89
763.
131.
232
.5\
1.18
2.24
1.23
2.72
1.28
1.63
0.74
2.02
0.79
3.74
1.33
1.4
0.63
260
Zeb
raun
avai
labl
e
AP
PE
ND
IXB
Sh
ow
nh
ere
are
all
the
con
cep
tsfo
rw
hic
ho
ne
or
mo
ren
amin
g,
imag
ing
,o
rid
enti
fica
tio
nfa
ilu
res
occ
urr
ed,
ad
iffe
ren
to
bje
ctw
asim
aged
,or
mo
reth
ano
ne
nam
ew
asgi
ven.
Fai
lure
sin
the
nam
ing
task
are
list
edas
DK
N(d
on
'tk
no
wn
ame)
,D
KO
(do
n't
kn
ow
ob
ject
),an
dT
OT
(tip
oft
he
ton
gu
e).
Iden
-ti
fica
tio
nfa
ilu
res
inth
efa
mil
iari
tyra
tin
gta
skar
eli
sted
asD
KO
(FA
M).
Imag
ing
fail
ure
sin
the
imag
eag
reem
ent
task
are
list
edas
NI
(no
imag
e),
and
imag
ing
ad
iffe
ren
to
bje
ctas
DO
(dif
fere
nt
ob
ject
).A
Un
on
do
min
ant
nam
esg
iven
for
each
con
cep
tar
eli
sted
and
acco
mp
anie
db
yth
eir
freq
uen
cies
.
DK
OD
KN
DK
OT
OT
(FA
M)
NI
DO
Non
dom
inan
tNam
es
IA
cord
eon
Acc
ordi
on0
05
0I
0S
axof
onI
2A
vion
Air
plan
e0
00
00
03
Coc
odri
loA
llig
ator
00
I0
00
Cai
man
2,la
gart
o4
4A
ncla
Anc
hor
00
20
00
5H
orm
iga
Ant
III
I0
00
Ara
na2,
inse
cto
3,m
osqu
ito
46
Man
zana
App
le0
0I
00
07
Bra
zoA
rm0
00
00
0C
odo
2,m
ano
I8
Flec
haA
rrow
00
00
215
Sefi
alS
9A
lcac
hofa
Art
icho
ke0
I3
I6
0C
olif
lor
I,pi
fia
I,re
poll
oI
10C
enic
ero
Ash
tray
I0
00
0I
IIE
spar
rago
Asp
arag
us6
50
2I
0A
stil
laI,
cofi
aI,
puer
roI,
ram
a3
12H
acha
Axe
00
00
I0
Mac
heta
I13
Coc
heci
toB
aby
Car
riag
eI
0I
0I
8C
arri
coch
e12
,car
rito
7,ca
rrit
ode
bebe
I,ca
rrit
ode
nino
2,ca
rro
deni
no4,
coch
e2,
coch
eco
nca
pota
I,co
che
debe
beI,
coch
ede
nino
I,si
llaI,
silla
deni
no2,
sill
eta
I14
Pelo
taB
all
00
02
00
Bal
on19
,bol
aI
15G
lobo
Bal
loon
00
0I
07
16Pl
atan
oB
anan
a0
00
00
0""C
17C
asa
32
I2
Alm
acen
I,ca
bana
2,ca
sade
cam
poI,
case
taI,
casi
taI,
......
Bar
no
cobe
rtiz
o2,
edif
icio
I,es
tabl
o2,
fabr
ica
8,ga
raje
I,gr
aner
o10
,>
-lgr
anja
6,m
olin
oI,
paja
rI
e ;::tl
18B
arri
lB
arre
l0
05
0I
2B
idon
I,cu
ba2,
cube
taI,
cubo
2,to
nel
15tI
119
Bat
eB
aseb
all
30
3I
29
Bas
ton
I,ba
tede
beis
bol
20,
palo
I,pa
lode
beis
bol
3,po
rra
Ir:
/J
20C
esta
Bas
ket
00
00
0I
Boi
saI,
cest
ade
cam
poI
r:/J
21O
soB
ear
00
00
45
Oso
pola
rI
~22
Cam
aB
ed0
00
00
0C
ama
dem
atri
mon
ioI
Z23
Abe
jaB
eeI
0I
00
0A
visp
a8,
inse
cto
3,m
osca
10,m
osco
nI,
mos
quit
oI
~24
Cuc
arac
haB
eetle
124
00
Bic
hoI,
esca
raba
jo13
,gri
llo4,
inse
cto
10,m
osca
I;::
tl25
Cin
turo
nB
elt
00
00
00
Cin
to3,
coll
arI,
corr
ea4
t:! ......
26C
ampa
naB
ell
00
00
00
Cam
pani
lla
IN
27B
icic
leta
Bic
ycle
00
00
00
Bic
i7
tI1 t:!
28Pa
jaro
Bir
d0
00
00
0C
anar
io3,
gorr
ion
3,pa
jari
llo
2,pa
jari
to2,
poll
itoI,
polio
I'T
j29
Cam
isa
Blo
use
00
I0
00
Am
eric
ana
3,bl
usa
II,
caza
dora
2,ch
aque
ta18
030
Lib
roB
ook
00
00
00
;::tl
31B
ota
Boo
t0
00
00
3B
ota
deag
uaI,
botin
I,bo
toI
r:/J
32B
otel
laB
ottle
00
00
00
~33
Laz
oB
ow0
00
07
3L
azad
aI
Z34
Cue
nco
Bow
l2
I6
0I
0B
o15,
ensa
lade
raI,
hond
illa
I,pl
ato
I,re
cipi
ente
I,ta
za5,
......
r:/J
tazo
n14
,vas
ija4
::c35
Caj
aB
ox0
00
20
36Pa
nde
mol
deB
read
00
00
0I
Pan
26,
pan
inte
gral
I,pa
nto
stad
oI,
reba
nada
depa
nI,
tost
ada
depa
n2
VI
.j:>
.\0
AP
PE
ND
IXB
(Con
tinu
ed)
Vl
Vl
DK
O0
DK
ND
KO
TO
T(F
AM
)N
ID
ON
ondo
min
ant
Nam
es
37E
scob
aB
room
00
00
00
Cep
illo
deba
rrel
'I,
esco
mbr
aI
C/)
38C
epil
loB
rush
00
00
I15
Cep
illo
del
peIo
7>-
39A
utob
usB
us0
00
00
0A
utoc
ar3,
bus
3,om
nibu
sI
Z 'Tj
40M
arip
osa
But
terf
ly0
00
00
0tT
l41
Bor
onB
utto
nI
00
00
Ir- -
42T
arta
Cak
e0
00
I0
0P
aste
l10
,qu
eso
4C
43C
amel
loC
amel
20
20
0I
Dro
med
ario
9>-
44V
ela
Can
dle
00
00
03
Z45
Can
non
unav
aila
ble
0 'Tj
46G
orr
aC
ap0
02
I0
IG
orr
o3,
som
brer
oI
tTl
47C
oche
Car
00
00
00
Coc
hazo
I,li
mus
ina
I,ve
hicu
loI
:::048
Zan
ahor
iaC
arro
t0
0I
00
0Z >-
49G
ato
Cat
00
00
I0
Z50
Oru
ga
Cat
erpi
ller
00
I0
00
Cie
mpi
es8,
gusa
node
seda
I,gu
sano
230
51A
ceIg
asC
eler
y16
II10
3A
pio
3,be
reng
ena
I,b
erza
I,co
Iifl
orI,
espi
naca
sI,
hort
aliz
aI,
tTl
nabo
I,pu
erro
2,pu
erro
s3,
rem
olac
haI,
verd
ura
5N
52C
aden
aC
hain
00
20
04
cade
nas
I53
Sil
laC
hai
r0
00
00
054
Cer
eza
Ch
erry
0I
00
00
Cir
ueIa
2,gu
inda
I,m
anza
na2
55G
alli
naC
hick
en0
0I
00
0G
allo
256
Des
torn
illa
dor
Chi
sel
1810
127
Cor
tagi
raI,
esca
fina
I,es
patu
la4,
herr
amie
nta
I,li
ma
4,pu
ncel
I,p
un
zon
I57
Igle
sia
Chu
rch
00
00
00
58Pu
roC
igar
00
I0
00
59C
igar
roC
igar
ette
00
00
00
Cig
arri
llo
9,ta
baco
I60
Rel
ojC
lock
00
00
0I
Rel
ojde
pare
d2,
relo
jde
mes
aI
61P
inza
Clo
thes
pin
00
00
00
AIf
iler
I,p
inza
dero
pa6,
pinz
as4
62N
ube
Clo
udI
10I
220
0A
lgod
on3,
col
2,co
lifl
or3,
espo
nja
3,es
pu
ma
I,m
apa
I,pe
lla
I,so
toI,
tafe
taI
63P
ayas
oC
low
n0
00
00
IC
ara
depa
yaso
I64
Abr
igo
Coa
t0
00
00
IB
ata
9,ba
tin
I,ca
mis
aI,
chaq
ueto
n2,
gaba
rdin
a6
65Pe
ine
Co
mb
00
00
00
66M
aiz
Cor
n0
03
00
0M
azor
ca17
,maz
orca
dem
aiz
7,pi
fia
dem
aiz
I,tr
igo
I67
Sof
aC
ouch
00
00
00
Sil
lon
19,t
resi
llo
468
Vac
aC
ow0
00
00
069
Cor
ona
Cro
wn
00
00
03
70T
aza
Cu
p0
00
0I
0T
azon
3,va
soI
71C
ierv
oD
eer
I0
50
I0
Ale
eI,
arce
I,re
no4,
vena
doI
72E
scri
tori
oD
esk
0I
20
02
Co
mo
da
I,co
nsol
aI,
mes
aco
nca
jone
sI,
mes
ade
escr
ibir
I,m
esa
dees
tudi
o5,
mes
ade
ofi
cin
aI,
mes
ade
trab
ajo
I,m
esa
15,m
esa
escr
itor
io3,
mue
ble
I,pu
pitr
eI
73P
erro
Dog
00
00
00
74M
ufie
caD
oll
00
00
03
Nif
ia2
275
Bur
roD
onke
y0
00
02
0A
sno
II,
poni
I76
Pue
rta
Doo
r0
00
00
3V
enta
na5
77Po
rno
Doo
rkno
b5
210
IA
garr
ader
oI,
bolo
dela
pu
erta
I,lI
aver
oI,
man
ecil
laI,
man
goI,
man
ija
I,m
anil
la7,
man
illa
r3,
man
ille
raI,
man
ivel
a2,
pest
illo
I,pi
capo
rte
8,po
rno
dela
pu
erta
2,po
mul
oI,
tim
bre
I,ti
rado
rI
AP
PE
ND
IXB
(Con
tin
ued
)D
KO
DK
ND
KO
TO
T(F
AM
)N
ID
ON
ondo
min
ant
Nam
es
78V
estid
oD
ress
00
00
0I
Rop
aI,
traj
e4
79C
orno
daD
ress
erI
07
0A
rmar
io8,
cajo
nI,
cajo
nero
I,ca
jone
s2,
com
odin
5,co
nsol
aI,
coqu
eta
3,es
tant
eI,
mes
a2,
mes
illa
5,m
esita
2,m
uebl
e5,
taqu
illo
n2,
toca
dor
380
Tam
bor
Dru
m0
0I
00
0T
ambo
ril
I81
Pato
Duc
k0
00
00
0O
caI
82A
guila
Eag
le0
00
00
0A
guila
real
I,bu
itre
3,ha
lcon
3,lo
roI,
paja
ro3
83O
reja
Ear
00
00
00
Oid
o4
84E
lefa
nte
Ele
phan
t0
00
00
085
Sobr
eE
nvel
ope
00
00
00
Car
ta5
86O
joE
ye0
00
00
087
Val
laFe
nce
I0
30
00
Bar
rera
I,ce
rca4
,val
lada
I,va
llas
I,ve
rja
9,ve
rjas
I88
Ded
oFi
nger
00
00
00
Ded
oin
dice
5,de
dose
fial
ando
I,in
dice
889
Pez
Fish
00
00
00
Pesc
ado
3,tr
ucha
290
Ban
dera
Flag
I0
I0
I0
Ban
deri
n4
91F1
0rFl
ower
00
00
02
92F1
auta
Flut
e4
173
190
4A
guja
I,ag
uja
depu
nto
I,ba
nder
illa
I,ca
fia
I,fl
auta
trav
eser
aI,
flau
tin3,
term
omet
ro3
93M
osca
Fly
00
I0
00
Bic
hoI,
mos
quit
oI
94Pi
eFo
ot0
00
00
095
Bal
onde
rugb
yFo
otba
ll0
I0
00
0B
alon
16,b
alon
debe
isbo
l9,
halo
nde
futb
olI,
beis
bol
I,bo
las
debe
isbo
lI,
peIo
ta2,
pelo
tade
beis
bol
I,pe
lota
deru
gby
3,ru
gby
I""C
96C
asco
Foot
ball
helm
et1
3I
50
3A
carg
ader
aI,
casc
ode
beis
bol
I,ca
sco
deho
ckey
I,ca
sco
de- o
rugb
y10
,gor
roI,
gorr
ode
rugb
yI,
met
roI
...,97
Ten
edor
Fork
00
00
00
c:::98
Zor
roFo
x0
00
00
0L
obo
2:::0
99T
rom
bon
Fren
chh
om
I35
89
0I
Saxo
fon
5,tr
ompa
2,tr
ompe
taII
,tro
mpe
tinI,
trom
pon
I,vi
ola
ItT
i\/
J10
0R
ana
Frog
00
00
02
Sapo
9\/
J
101
Sart
enFr
ying
pan
00
00
00
Caz
o3,
cazu
ela
I~
102
Cub
ode
basu
raG
arba
gebi
nI
02
00
IB
asur
a5,
basu
rera
I,ba
sure
ro3,
cont
ened
or2,
cubo
5,Z
pape
lera
I,ta
scon
I,tu
boI
010
3Ji
rafa
Gir
affe
00
00
00
>10
4V
aso
Gla
ss0
00
00
3:::0 0
lOS
Gaf
asG
lass
es0
00
00
0-N
106
Gua
nte
Glo
ve0
00
00
0M
ano
3,m
anop
la2
tTi
107
Cab
raG
oat
2I
50
I0
Chi
voI,
ovej
a2,
rebe
coI
ti10
8G
orila
Gor
illa
00
00
00
Chi
mpa
nce
4,m
ono
13,o
rang
utan
II'T
j
109
Uva
sG
rape
s0
00
00
4R
acim
o3,
raci
mo
deuv
as17
,uva
140 :::0
110
Sal
tam
onte
sG
rass
hopp
er3
I2
2I
IC
igar
raI,
grill
o3,
inse
cto
I,m
osqu
ito
2\/
JII
IG
uita
rra
Gui
tar
00
00
00
~11
2Pi
stol
aG
un0
00
00
0R
evol
ver
8Z
113
Pelo
Hai
r0
40
90
2C
abel
lo9,
casc
oI,
pein
ado
I,pe
luca
I,so
mbr
ero
2-\/J
114
Mar
tillo
Ham
mer
I0
I0
00
::r:II
SM
ano
Han
d0
00
00
011
6Pe
rcha
Han
ger
00
00
02
117
Arp
aH
arp
20
50
II
Lir
aI
Vl
118
Som
brer
oH
at0
00
00
0G
orro
3V
l -
AP
PE
ND
IXB
(Con
tinu
ed)
VI
VI
DK
ON
DK
ND
KO
TO
T(F
AM
)N
ID
ON
ondo
min
ant
Nam
es11
9C
oraz
onH
eart
00
00
02
(/)
120
Hel
icop
tero
Hel
icop
ter
00
00
00
Avi
onI
>-12
1C
abal
loH
orse
00
00
00
Z 'Tj
122
Cas
aH
ouse
00
0I
00
Cab
ana
3,ca
sade
cam
po3,
casi
taI,
chal
etI
tTl
123
Pla
ncha
Iron
00
02
00
r- -12
4T
abla
depl
anch
arIr
onin
gbo
ard
00
30
0I
Mes
aI,
mes
ade
plan
char
17,p
lanc
hado
rI,
plan
char
I,ta
bla
5,C
tabl
ero
depl
anch
aI
>-12
5C
haqu
eta
Jack
etI
0I
0I
0A
brig
oI,
amer
ican
aI,
bata
I,bl
usa
I,ca
mis
a16
,ca
zado
ra2,
Zch
aque
ton
I,ga
ban
I,ga
bard
ina
I,ro
paI,
traj
eI
t:I 'Tj
126
Can
guro
Kan
garo
o0
0I
00
0P
ingi
iino
ItT
l12
7T
eter
aK
ettle
00
00
0I
Ace
iter
aI,
cafe
tera
16,c
azo
dele
che
I::0
128
Lla
veK
ey0
00
00
0Z >-
129
Com
eta
Kite
00
00
II
Z13
0C
uchi
llo
Kni
fe2
0I
00
0P
uerr
oI
t:I13
1E
scal
era
Lad
der
00
I0
06
Esc
aler
ade
man
o2
tTl
132
Lam
para
Lam
p0
00
00
7N
133
Hoj
aL
eaf
00
00
I7
Hoj
ade
arbo
l2,
hoja
depa
rra
213
4P
iern
aL
eg0
00
00
IPi
e4,
rodi
lla
213
5L
imon
Lem
on0
00
00
013
6L
eopa
rdo
Leo
pard
00
I0
20
Gue
pard
oI,
jagu
arI,
pant
era
4,pu
ma
3,tig
re21
137
Lec
huga
Let
tuce
34
27
II
Ber
za,
2,co
l10
,col
iflo
r7,
espo
nja
I,ho
rtal
iza
I,pe
loI,
repo
llo
2,ve
rdur
aI
138
Bom
bill
aL
ight
bulb
00
00
00
139
Inte
rrup
tor
Lig
htsw
itch
I2
4I
0I
Enc
ende
dor
I,en
chuf
e3,
lIav
ede
Iuz
5,lu
z2,
tim
bre
314
0L
eon
Lio
n0
00
00
014
1L
abio
sL
ips
00
00
00
Boc
a9,
labi
oI
142
Can
grej
oL
obst
er0
08
II
IC
igal
a5,
crus
tace
o3,
esco
rpio
n4,
gam
ba2,
lang
osta
14,
lang
osti
no2,
mar
isco
I14
3C
anda
doL
ock
00
60
00
Cer
radu
raI,
cerr
ojo
314
4M
anop
laM
itten
00
00
I2
Gua
nte
2114
5M
ono
Mon
key
00
00
02
Chi
mpa
nce
4,m
andr
il2
146
Lun
aM
oon
00
00
00
147
Mot
oM
otor
cycl
e0
00
00
0M
otoc
icle
ta7
148
Mon
tana
Mou
ntai
n0
00
00
0C
ima
I,cu
mbr
eI,
mon
te4,
pico
6,pi
code
mon
tana
714
9R
aton
Mou
se0
00
00
IR
ata
1315
0Se
taM
ushr
oom
00
I0
00
Cha
mpi
fion
I,ho
ngo
I15
1C
lavo
Nai
l3
00
00
0B
asto
nI,
c1ar
inet
e1,
punt
a18
,pun
zaI,
torn
illo
1015
2L
ima
Nai
l-fi
le0
I3
II
IB
olig
rafo
I,cu
chil
lo4,
Iija
2,lim
ade
ufia
s3,
nava
ja2
153
Col
lar
Nec
klac
e0
0I
0I
0A
nill
oI,
coll
arde
perl
as4
154
Agu
jaN
eedl
e0
0I
I0
0A
guja
deco
ser
I,al
file
r2,
plum
aI
155
Nar
izN
ose
00
00
00
156
Tue
rca
Nut
30
30
01
Ros
caI,
torn
illo
1215
7C
ebol
laO
nion
I0
00
I0
Ace
lga
I,aj
oI
158
Nar
anja
Ora
nge
30
I6
00
Frut
a3,
gran
ada
I,lim
onI,
pom
elo
I15
9A
vest
ruz
Ost
rich
4I
90
I0
Abu
tard
aI,
gans
oI,
pavo
216
0B
uho
Ow
l0
00
00
0L
echu
za8
161
Pinc
elP
aint
brus
h0
00
00
0P
lum
a216
2P
anta
lon
Pant
s0
0I
00
0P
anta
lone
s9
AP
PE
ND
IXB
(Con
tin
ued
)D
KO
DK
ND
KO
TO
T(F
AM
)N
ID
ON
ondo
min
ant
Nam
es
163
Pavo
real
Pea
cock
00
30
00
Paj
aro
1,pa
vo1
164
Mel
ocot
onPe
ach
31
23
00
A1b
aric
oque
1,ci
ruel
a1,
man
zana
1,na
ranj
a5,
pelo
tade
rugb
yI
165
Cac
ahue
teP
eanu
t0
50
20
0A
vell
ana
1,m
ani
I,pe
pino
I16
6Pe
raPe
ar0
00
00
016
7B
olig
rafo
Pen
00
00
00
Bol
i14
,plu
ma
216
8L
apiz
Penc
il0
00
00
0L
apic
ero
9,la
piz
con
gom
a1
169
Pin
giii
noP
engu
in0
00
00
017
0P
imie
nto
Pep
per
32
44
10
Tom
ate
217
1Pi
ano
Pia
no0
00
01
IP
iano
deco
la3
172
Cer
doPi
g0
00
00
0C
hon
1,co
chin
o1,
gorr
ino
I,m
arra
noI,
rino
cero
nte
117
3Pi
iiaP
inea
pple
00
00
I5
174
Pipa
Pipe
00
00
08
Pipa
defu
mar
117
5Ja
rra
Pit
cher
00
00
00
Jarr
on5,
tina
ja1,
vasi
ja1
176
Ali
cate
sP
lier
s5
07
01
2A
lica
te16
,he
rram
ient
a2,
tena
za1,
tena
zas
1117
7E
nchu
fePl
ug0
16
00
11C
able
I,in
terr
upto
r2
178
Boi
soP
ocke
tboo
k0
00
00
0B
oIs
a1,
cart
era
217
9C
azo
Pot
10
60
04
Cac
erol
a5,
cazu
ela
418
0P
atat
aPo
tato
I0
I4
I0
Gal
leta
I18
1C
alab
aza
Pum
pkin
2I
3I
00
Nar
anja
I,sa
ndia
I18
2C
onej
oR
abbi
t0
00
00
018
3M
apac
heR
acco
on9
413
05
2A
nim
alI,
cast
or2,
lince
I,m
ofet
a3,
yena
I,zo
rro
818
4T
ocad
isco
sR
ecor
dpl
ayer
I0
50
00
Gir
adis
cos
I,m
agne
tofo
n1,
meg
aI,
pinc
hadi
scos
I,ra
dioc
asse
tte
I"'t
'l18
5F
rigo
rifi
coR
efri
gera
tor
00
00
00
Nev
era
30.....
. o18
6R
inoc
eron
teR
hino
cero
s2
00
00
0H
ipop
otar
noI,
jab
ali
I"'""
I18
7A
nill
oR
ing
II
I3
00
Aro
I,pe
ndie
nte
I,so
rtij
aI
c::::
188
Mec
edor
aR
ocki
ngch
air
00
I0
I2
But
aca
2,ha
mac
a6,
silla
7,si
llon
1,tu
mbo
naI
:::c tTl
189
Patin
Rol
ler
skat
e0
00
00
3M
onop
atin
I,pa
tine
s1,
pati
nete
3tr
:19
0R
odil
loR
olli
ngpi
n9
118
00
2A
mas
ador
5,m
asa
I,m
azo
I,m
azo
deam
asar
1,to
rmo
tr:
191
Gal
loR
oost
er0
0I
00
0G
alli
na10
,pol
ioI
~19
2R
egia
Rul
er0
00
00
0M
etro
2Z
193
Bar
coS
ailb
oat
00
00
I2
Bar
ca2,
barc
ode
vela
17,
vele
ro19
019
4S
aler
oS
alts
hake
r0
I0
00
0F
rasc
oI,
torn
illo
I;» :::c
195
San
dwic
hS
andw
ich
0I
00
0I
Boc
adil
lo6,
boca
ta3
019
6S
ierr
aSa
w0
02
00
0S
erru
cho
10,s
ierr
ade
man
oI
......
N19
7T
ijer
asS
ciss
ors
00
00
0I
Tij
era
4tT
l19
8T
orni
llo
Scre
wI
00
00
0C
lavo
7,pu
nta
2,tu
erca
I0
199
Des
torn
illa
dor
Scr
ewdr
iver
40
50
00
Tor
nava
sI,
torn
illo
I'T
j
200
Cab
alli
tode
mar
Sea
hors
e3
04
0I
0C
abal
lode
mar
19,c
amal
eon
I,ca
raco
lde
mar
10 :::c
201
Foca
Seal
I0
00
I0
Mar
mot
a1,
mor
sa5
o:20
2O
veja
She
ep2
00
II
0C
amer
o3,
cord
ero
I~
203
Cam
isa
Shi
rt0
00
0I
0B
lusa
2Z
204
Zap
ato
Shoe
00
00
00
......
r.n20
5Fa
lda
Ski
rt0
00
00
0Fa
ldon
1,m
andi
l1
::z::
206
Mof
eta
Sku
nk5
0II
04
IA
nim
alI,
ardi
lla
3,ca
stor
I,zo
rril
lo3
207
Tri
neo
Sled
310
100
01
Cam
illa
1,es
qui
I,pa
tin
I,pa
tine
teI
208
Car
acol
Snai
l0
0I
00
0V
l
209
Ser
pien
teS
nake
00
00
00
Cul
ebra
9,vi
bora
IV
lV
J
AP
PE
ND
IXB
(Con
tin
ued
)V
lV
l
DK
O.j::
>.
DK
ND
KO
TO
T(F
AM
)N
ID
ON
ondo
min
ant
Nam
es
210
Muf
ieco
deni
eve
Sno
wm
an0
0I
00
0E
span
tapa
jaro
sI,
hom
bre
deni
eve
IC
/)
211
Cal
ceti
nSo
ck0
00
00
0;I>
-
212
Ara
naS
pide
rI
00
00
0A
racn
ido
I,cu
cara
cha
I,in
sect
aI
Z 'Tj
213
Spi
nnin
gw
heel
unav
aila
ble
t'Ii
214
Hil0
Spo
olo
fth
read
I0
I0
Bob
ina
9,bo
bina
dehi
la13
,ca
fia
dehi
lo1,
carr
ete
8,ca
rro
del' ......
hilo
1,cu
erda
2,en
rall
ado
I,ov
illo
2,ov
illo
dehi
lo3,
rollo
Cde
hila
1;I>
-21
5C
ucha
raS
poon
00
00
00
Cuc
hari
lla
6Z
216
Ard
illa
Squ
irre
l1
00
00
00 'T
j21
7E
stre
lla
Star
00
00
00
t'Ii
218
Tab
uret
eSt
ool
00
20
00
Ban
coI,
banq
ueta
6,bu
taca
2,si
lla6,
tajo
3;;0
219
Coc
ina
Stov
e0
01
00
4C
ocin
ael
ectr
ica
1,co
cini
lla
5,es
tufa
1,fo
gon
1,h
om
illo
1,Z ;I>
-h
om
o6,
lava
dora
1Z
220
Fres
aS
traw
berr
y0
00
01
00
221
Mal
eta
Sui
tcas
e0
00
01
1C
arte
ra1,
mal
etin
1t'I
i22
2So
lSu
n0
00
00
0B
ola
1N
223
Cis
neSw
an0
02
02
0G
anso
I,oc
a1,
pato
222
4Je
rsey
Sw
eate
r0
00
00
2C
amis
a1,
suet
er1
225
Col
umpi
oS
win
g0
00
31
2B
alan
cin
1,si
llita
122
6M
esa
Tab
le0
00
00
0M
esil
la1
227
Tel
efon
oT
elep
hone
00
00
00
228
Tel
evis
ion
Tel
evis
ion
00
00
00
Tel
e3,
tele
viso
r24
,T
V7
229
Raq
ueta
Ten
nis
rack
et0
00
00
023
0D
edal
Thi
mbl
e1
01
02
0T
apon
123
1D
edo
Thu
mb
10
00
00
Ded
ogo
rdo
4,de
dode
lam
ano
2,de
dopu
lgar
6,m
ano
1,pu
lgar
2123
2C
orba
taT
ie0
00
01
023
3T
igre
Tig
er0
00
00
IL
eopa
rdo
4,lin
ce1,
pant
era
123
4T
osta
dor
Toa
ster
04
40
00
Bal
anza
1,b
rasa
do
r1,
caja
fuer
te1,
carr
oI,
sand
wic
hera
I,to
stad
era
4,to
stad
ora
1823
5Pi
eT
oe0
00
00
0D
edo
5,de
dogo
rdo
3,de
dode
lpi
e16
,de
dos
2,de
dos
delo
spi
es11
,pul
gar
1,un
a2
236
Tom
ate
Tom
ato
00
00
00
Fru
ta1
237
Cep
illo
dedi
ente
sT
ooth
brus
h0
00
0I
15C
epil
lo1
238
Peo
nza
Top
10
30
00
Pep
ion
2,pe
rigo
nza
1,pe
tanc
a1,
tror
nbon
1,tr
ompo
323
9T
raff
iclig
htun
avai
labl
e24
0T
ren
Tra
in0
00
00
0L
ocom
otor
a2,
tren
rapi
doI
241
Arb
olT
ree
00
00
00
Enc
ina
1,ro
ble
124
2C
arni
onT
ruck
00
00
00
Tra
iler
124
3T
rom
peta
Tru
mpe
t1
02
0F
laut
aI
244
Tur
tleun
avai
labl
e24
5P
arag
uas
Um
brel
laI
00
00
024
6Ja
rron
Vas
e0
01
I0
IF
lore
roI,
jarr
a1,
jarr
on
chin
oI
247
Cha
leco
Ves
t0
02
00
0C
haqu
eta
I24
8V
iolin
Vio
lin0
00
00
0C
ontr
abaj
oI,
guit
arra
1,vi
olon
chel
o6
249
Car
rito
Wag
on11
66
0C
arre
ta4,
carr
etil
la7,
carr
etil
lo4,
carr
icoc
heI,
carr
o6,
coch
ecit
o2,
pati
n3
250
Rel
ojW
atch
00
00
13
Rel
ojde
man
e1,
relo
jde
rnuf
ieca
I,re
loj
depu
lser
a7
AP
PE
ND
IXB
(Con
tin
ued
)D
KO
DK
ND
KO
TO
T(F
AM
)N
ID
ON
ondo
min
antN
ames
251
Reg
ader
aW
ater
ing
can
00
00
02
Reg
ador
I25
2S
andi
aW
ater
mel
on0
00
I0
0C
ata
desa
ndia
I,lim
onI,
mel
on6,
raja
dem
elon
6,ra
jade
sand
ia12
,rod
aja
desa
ndia
I,ta
jada
desa
ndia
I,tr
ozo
desa
ndia
I25
3W
ell
unav
aila
ble
254
Rue
daW
heel
00
00
06
Rue
dade
carr
o4,
rued
ade
mad
era
I25
5Si
lbat
oW
hist
le0
0I
0I
IPi
to14
,que
soI
256
Mol
ino
Win
dmill
00
00
I0
Asp
asI,
mol
ino
devi
ento
325
7V
enta
naW
indo
w0
00
00
IPu
erta
I25
8C
opa
Win
egla
ss0
00
00
IV
aso
I25
9L
lave
ingl
esa
Wre
nch
4I
90
25
Alic
ates
I,he
rram
ient
a2,
llave
9,lla
vede
herr
amie
nta
I,lla
vern
ecan
ica
I,lla
vetu
erca
I,tu
erca
I26
0Z
ebra
unav
aila
ble
(Man
uscr
ipt
rece
ived
Nov
embe
r21
,19
94;
revi
sion
acce
pted
for
publ
icat
ion
Sep
tem
ber
6,19
95.)
""C n o-l
C ~ en en ~ Z ~ § ......
N tTl
t:I 'Tj o ::0 en ~ Z ......
en == VI
VI
VI