An Ecological Alternative to Snodgrass & Vanderwart:
360 High Quality Colour Images with Norms for Seven
Francisco Javier Moreno-Martı ´nez, Pedro R. Montoro*
Departamento de Psicologı ´a Ba ´sica I, Universidad Nacional de Educacio ´n a Distancia, Madrid, Spain
This work presents a new set of 360 high quality colour images belonging to 23 semantic subcategories. Two hundred and
thirty-six Spanish speakers named the items and also provided data from seven relevant psycholinguistic variables: age of
acquisition, familiarity, manipulability, name agreement, typicality and visual complexity. Furthermore, we also present
lexical frequency data derived from Internet search hits. Apart from the high number of variables evaluated, knowing that it
affects the processing of stimuli, this new set presents important advantages over other similar image corpi: (a) this corpus
presents a broad number of subcategories and images; for example, this will permit researchers to select stimuli of
appropriate difficulty as required, (e.g., to deal with problems derived from ceiling effects); (b) the fact of using coloured
stimuli provides a more realistic, ecologically-valid, representation of real life objects. In sum, this set of stimuli provides a
useful tool for research on visual object-and word- processing, both in neurological patients and in healthy controls.
Citation: Moreno-Martı ´nez FJ, Montoro PR (2012) An Ecological Alternative to Snodgrass & Vanderwart: 360 High Quality Colour Images with Norms for Seven
Psycholinguistic Variables. PLoS ONE 7(5): e37527. doi:10.1371/journal.pone.0037527
Editor: Luis M. Martinez, CSIC-Univ Miguel Hernandez, Spain
Received January 18, 2012; Accepted April 24, 2012; Published May 25, 2012
Copyright: ? 2012 Moreno-Martı ´nez, Montoro. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: No current external funding sources for this study.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Throughout the last 30 years, many clinical and experimental
studies on cognitive processing (i.e., exploring memory, attention
or language) have been performed with the items created by
Snodgrass and Vanderwart (S&V) . These authors standardized
their stimuli in four variables relevant to cognitive processing:
familiarity, image agreement, name agreement and visual
complexity. Experimental control of these variables is essential
because they are known to affect cognitive processing both of
pictorial and verbal material. Thus, more familiar items, those
with higher name and image agreement, as well as those with
lesser visual complexity, are more easily named both by intact and
neurological participants [2–5].
Apart from these variables, other cognitive and psycholinguistic
variables such as age of acquisisiton (AoA) and manipulability and
typicality of items significantly affect cognitive processing. Thus,
AoA is a powerful predictor of object-naming performance both in
normal and brain-injured individuals, with earlier acquired words
being more easily processed than later acquired ones [6,7].
Similarly, there is a significant relationship between the degree of
manipulability of an object; that is, the degree of use of the human
hand that is necessary for an object to perform its function and its
semantic representation (e.g., [8–12]). Indeed, it has been
proposed that differences in manipulability could explain category
effects on object identification, consisting of a better performance
with items from the domain of nonliving things (e.g., tools)
compared to living things (e.g., animals; see , for a review).
Lastly, typicality of items (i.e., how typical, or representative, a
member is of a category) is another important psycholinguistic
variable. Classic studies by Eleanor Rosch showed the relevance of
this variable and its strong influence on performance in tasks
assessing cognitive processing and memory, language use and
communication, or development-related phenomena such as
category learning and conceptual development (see, for example,
[14–16]). Similarly, typicality of items has also been found to
significantly impact the performance of neurological patients (e.g.,
aphasics: ). Despite the relevance of typicality in normal and
damaged cognitive processing, most of the recent normative works
and new semantic tests have not paid close attention to this
variable (for example, [1,18–28]; but see also [29–31]). Likewise,
only a few recent works have provided ratings of AoA  or
manipulability , and, to our knowledge, only [25,30] have
presented ratings of both variables concurrently, but with a
relatively sparse number of items, as they only studied 140 
and 112  coloured stimuli.
Some recent concerns respect to S&V corpi are related to the
ecological validity of the stimuli and ceiling effects in the responses.
Items from S&V consist of black and white line drawings. From an
ecological view, the validity of studies using this type of stimuli has
been questioned . Colour is an essential attribute of objects
and, except for unusual pathologies, it is difficult to separate colour
from real world objects [32,33]. Consequently, the number of
works using coloured items, providing a more realistic represen-
tation of objects, as well as studies normalising coloured stimuli,
have been progressively increasing (see, for example, [19–
21,23,27,28,34–42]). Regarding ceiling effects, it has been
observed that most of the items from S&V are easily named by
healthy participants, at least under normal viewing conditions.
This facilitates non-damaged participants showing ceiling effects in
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studies that involve the processing of objects, especially when using
not very demanding tasks, (e.g., picture naming; see [37,43]). As
shown by Laws and collaborators in studies on category-specificity,
this problem may distort both the degree and type of deficit
reported in patients [37,43].
The goal of the present work was twofold: (a) to present a broad
set of high quality ecological colour photographs, on white
backgrounds, across a difficulty range to deal with problems
derived from ceiling effects; and (b) to give detailed norms, derived
from a large group of healthy participants, of several relevant
psycholinguistic variables, some of them not sufficiently studied in
several previous works: AoA, familiarity, manipulability, name
agreement, typicality and visual complexity, as well as lexical
frequency. Furthermore, indexes of individual item analysis,
including a measure of item difficulty and two indexes of item
discrimination have been included.
Following previous normative and semantic assessment studies,
we selected 23 semantic subcategories (and their items) based on
relevant theoretical and methodological reasons [1,19–21,23,25–
29,30,44,45]. Consequently, we included problematic/atypical
subcategories, such as body parts, musical instruments or foodstuff
[13,46,47], different types of plant life subcategories [48–51];
insects ; subcategories differing in their degree of manipula-
bility, such as buildings or tools [10–12]. As a result, we included
ten subcategories from the living domain: animals, birds, body
parts, dried fruits, insects, flowers, fruits, sea creatures, trees and
vegetables; and twelve subcategories from the nonliving domain:
buildings, clothing, foodstuff, furniture, jewellery, kitchen utensils,
musical instruments, office material, sports/games, tools, vehicles
and weapons; plus the subcategory of the nonliving natural things,
such as a mountain or a stone. Table 1 contrasts the present work
with previous normative studies carried out with coloured
stimuli—plus the classic findings by S&V—regarding the number
of categories and items studied.
Following the aforementioned procedure, 360 items were
selected, and colour photographs were obtained for each one.
All the photographs were directly taken by the first author and a
collaborator (Sara Can ˜amo ´n). Subsequently, the images were
removed from their original backgrounds (except for the nonliving
natural things) and placed on a plain white background; the mean
dimension of the images was 2656223 pixels. Regarding the left-
right orientation of each image, it was decided that, for each
category susceptible to being oriented (i.e., animals, vehicles or
tools), half of the items were left-facing and the other half right-
The experimental items were displayed to a sample of 236
participants (see Participants and Procedure sections) for naming
the pictures and, then, for evaluating the five psycholinguistic
variables included in the study: AoA, familiarity, manipulability,
typicality and visual complexity. Several examples of items are
presented in Figure 1; the whole set of items are included as
supplemental material (Appendix S1).
The sample consisted of 236 healthy Spanish-speaking under-
graduate students (119 males; 117 females) with a mean age 36.7
years (SD=10.9; range 19–63 years; Males M=37.4, SD=10.2;
Females M=36 SD=11.5, F=1.03, n.s.) and a mean number of
years of education of 14.4 years (SD=2.5; range 12–17 years;
Males M=14.6, SD=2.5; Females M=14.3, SD=2.5, F=1.3,
n.s.). All had normal or corrected-to-normal vision, and Spanish
was their first language. Any person with a known history of
neurological disease, head trauma, or stroke was excluded. The
student participants were assigned course credit for their
participation in the study. The study was approved by the
Bioethics Committee from the UNED and conforms with the
Declaration of Helsinki. All participants provided written informed
consent (approved by the Bioethics Committee from the UNED)
for the collection of data and subsequent analysis. Additionally,
participants were explained that they were free to suspend their
participation in the experiments at any time and for any cause.
The 360 images were divided into three groups of items (120
each), namely lists A, B and C. We implemented a pseudorandom
selection in order to ensure that the three resulting lists included a
similar number of exemplars belonging to the 23 subcategories.
The 236 participants were randomly assigned to work with one of
the groups of items. Each group of items was evaluated by n=77
(38 males; 39 females, list A), n=80 (41 males; 39 females, list B),
and n=79 (40 males; 39 females, list C). Participants were tested
individually in two sessions. They all carried out the naming
session first and, subsequently, they rated the items for familiarity,
age of acquisition, visual complexity, manipulability and typicality.
The whole experiment, combined across both sessions, lasted
approximately ninety minutes, with self-administered rest periods
during the two sessions and between sessions. Each experimental
session was preceded by the instructions provided by researchers
and a practice phase to enable each participant to become familiar
with the task, and, additionally, to generate the acquisition of
anchor points for the stimulus ratings. In the practice phase, each
participant observed ten pictures that were not included in the
main stimulus set. The pictures were displayed on 19-inch LCD
colour monitors with a screen resolution of 10246768 pixels and a
32-bit color mode controlled by microcomputers running E-Prime
1.2 software (Psychology Software Tools, 1996–2002). Every
monitor was calibrated by means of the Display Color Calibration
tool available in Windows 7 Professional operating system
(Microsoft corporation, 2009) including brightness, contrast, color
balance and Gamma adjustments. Previously to the beginning of
each experimental session, at least 45 minutes were provided to
warm up the monitors. Periodically, the screens were carefully
cleaned in order to ensure an optimal picture quality. Viewing
distance was approximately 60 cm.
During the test phase, the 120 images were presented in a
random order. Each image was preceded by a cross (+) for 500 ms,
and remained on the screen for 3,000 msec (naming task phase) or
until the participant responded (during the item rating phase).
During the latter part of the task, visual complexity and typicality
were always the first and the last variables evaluated, respectively;
the rest of the variables were randomly displayed. To evaluate
visual complexity, participants were asked to ‘‘rate the visual
complexity of the image itself, rather than that of the object it
represents’’. To evaluate the remaining variables (AoA, familiarity,
manipulability and typicality), participants were asked to ‘‘rate the
object represented rather than the image itself’’. When the
participants evaluated the variables AoA, familiarity, manipula-
bility and typicality, experimenters provided them with the
canonical name of the item (i.e., the intended one). Additionally,
when participants evaluated the typicality of the items, they were
also provided with the category of the item on the screen (e.g.,
‘‘animals’’ —category— for ‘‘elephant’’—item).
Participants were asked to name each image
by typing its name with the keyboard on the screen. They were
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told to give the specific—rather than the general—name for the
different items. For example, in the case of the subcategory of
‘‘trees’’, if the participant knew the name of the item, he/she
should give the name of that particular tree, e.g., ‘‘pine tree’’,
instead of the general name of ‘‘tree’’. Participants were asked to
type the initials for ‘‘don’t know’’ (NC=‘‘No Conozco’’, in
Spanish), if the image was unknown to them, to type ‘‘tip of the
tongue’’ (PL=‘‘Punta de la Lengua’’, in Spanish) if they were
momentarily unable to remember the name, or to type ‘‘don’t
remember’’ (NR=‘‘No Recuerdo’’, in Spanish). All their respons-
es were automatically saved by the program. According to this
task, ‘‘name agreement’’ was calculated based on the percentage of
participants who named the item according to its canonical name.
Participants were asked to estimate the age in years at
which they had learned each word following the same procedure
of other similar previous studies (e.g., [4,7]). Scores were obtained
by asking participants to rate age of acquisition for each word on a
seven-interval scale (range: 1=0–2 years; 7=13 years or more; see
Participants were instructed to rate each item,
assessing ‘‘how usual or unusual the concept is in your realm of
experience’’ on the basis of ‘‘how frequently you think about the
concept, and how frequently you come into contact with the
concept —both directly (e.g., seeing a real-life exemplar) and in a
mediated way (e.g., represented in the media)’’. Participants
provided their responses on a 5-point Likert scale (1=very
unfamiliar, 5=very familiar) by pressing the corresponding number
on the keyboard.
Participants were instructed to rate each
item, assessing ‘‘the degree to which using a human hand is
necessary for this object to perform its function’’. Participants
provided their responses on a 5-point Likert scale (1=never
necessary, 5=totally indispensable) by pressing the corresponding
number on the keyboard.
Typicality: This reflects the degree to which a concept is a
representative exemplar of its category. Scores were obtained by
asking participants to rate on a 5-point scale (1=not at all
prototypical, 5=very prototypical) how representative of its category
they thought an exemplar was (e.g., car for vehicles).
Instructions from S&W’s study were
adapted to evaluate the visual complexity of the items. Conse-
quently, participants were asked to evaluate ‘‘the amount of detail,
intricacy of lines, pattern and quantity of colours presented in the
image’’. Participants recorded their responses on a 5-point scale
(1=very simple, 5=very complex) by pressing corresponding numbers
on the keyboard.
Owing to the unavailability of norms for
all of the words in a standard Spanish corpus (e.g. ), we
gathered norms for lexical frequency using an Internet search
engine. This method is a viable alternative to the currently
available databases and may even provide a more representative
 as well as a constantly updating measure of word frequency
 that has high convergent validity with other more classical
Table 1. Present study compared to previous normative ones with coloured stimuli (Adlington et al., 2008; Brodeur et al., 2010;
Moreno-Martı ´nez et al., 2011; and Viggiano et al., 2004), plus the one by S&V, concerning the categories and number of items
Present studyAdlington’sBrodeur’s M-Martı ´nez’sS&V’s Viggiano’s
1. Animals21 08---103042
2. Birds 2008--- ---08---
3. Body parts20 10---10 12 ---
4. Flowers12 08 ---10 --- ---
5. Fruits2110 ---10 11 ---
6. Insects17 08---1008 ---
7. Marine creatures18--- ------ ------
8. Nuts11 ------ ------ ---
9. Trees 11--- ---10 --- ---
10. Vegetables 20 09---10 1336
11. Buildings15 10--- 10--- ---
12. Clothing1311 2810 1912
13. Desk material 15--- 38 --- ------
14. Food15 07 78 ------ 10
15. Furniture 1512 02 1014 13
16. Jewellery12--- 08------ ---
17. Kitchen utensiles 1312 60 1014 12
18. Musical instruments 1611 04 --- 09 06
19. Sports/Games 16 --- 41 ---18 ---
20. Tools15 12 37 101229
21. Vehicles 13 11 ---1010 09
22. Weapons 15 ------ --- 07 ---
23. Nature 16 --- 11 ---------
Note: ---=Category not studied.
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databases. Furthermore, search engines permit the gathering of
word frequency values for more unusual items that do not typically
feature in conventional databases (see [19,30,54,55]). With more
than 250 million web pages, the AltaVista search engine (www.
altavista.com) is one of the largest search engines currently
available and, for this reason, it was selected for this process. These
names were entered into the search function of AltaVista, and a
search was performed, specifying that results should be for Spain
Figure 1. Several selected examples of the standardised stimuli (subcategory in brackets).
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and in Spanish only. The number of hits returned, after
conversion to their natural logarithm, served as the frequency
estimate for each word [19,31,53,56].
1. Descriptive results
A summary of the rating data for each item is reported in
Appendix S2 of the supplemental material. For each item, the
following information is presented: 1) the number of order of each
item; 2) the most frequent name in English and Spanish; 3) two
measures of name agreement: the statistic H and the percentage of
participants who produced the canonical/dominant name, plus
the percentage of participants who produced the modal name of
the item in those cases in which the latter did not match the
dominant name. Although both indexes are measures of name
agreement (statistic H and the percentage), the latter indicates only
how dominant the most common name is in a sample, whereas H
(or entropy ) is sensitive to how widely distributed responses
are over all the unique names that are provided for a picture.
Consequently, index H is more informative than name agreement
(e.g., it gives information about the dispersion of the names). H was
calculated according to the following formula:
where k is the number of unique names given for a picture, and pi
is the proportion of the sample providing each unique name. H=0
when there is perfect agreement among participants (e.g., just one
name) and increases as agreement decreases. ‘‘Don’t know’’, ‘‘tip
of the tongue’’ and ‘‘don’t remember’’ responses were not taken
into account to calculate index H; 4) the means and standard
deviation for AoA, familiarity, manipulability, typicality, visual
complexity and lexical frequency values expressed as a natural
logarithm. Appendix S3 reports alternative names of each item;
indexes of individual item analysis, including a measure of item
difficulty and two indexes of item discrimination based on item-test
correlations—point-biserial and biserial— are also included in
Appendix S4. Table 2 presents summary statistics for all the
aforementioned variables. Likewise, Table 3 shows summary
statistics for all the variables for all the subcategories. Lastly,
Table 4 shows Pearson correlations among the variables. In
general, as with other normative studies, the standard psycholin-
guistic variablestendto correlate
with eachother (see
2. Reliability and validity of the study
To establish validity, we compared our norms/stimuli with
those of the classical S&V, collected in USA, plus four recent
studies which, like ours, were conducted with high quality colour
images and coloured pictures, collected in United Kingdom,
Canada, Italy and Spain, respectively: [1,19,20,28,30]. Pearson’s
correlations, including those items sharing the same name in the
four studies (n=50 with , n=68 with , n=113 with ,
n=80 with , and n=106 with ) are shown in Table 5. A
high pattern of significant correlations (fluctuating between .25
and .99) was found among the diverse variables observed across
the five studies. So, even where compared across English (different
countries, languages and studies), Italian and Spanish, the ratings
remain highly correlated. Regarding reliability, Cronbach’s alpha
coefficients were also high: a=.83 (name agreement), a=.97
(visual complexity, familiarity and manipulability) and a=.98
(AoA and typicality).
The goal of the present work was twofold: (a) to present a broad
set of high quality ecological colour photographs across a range of
difficulty, to deal with problems derived from ceiling effects; and
(b) to give detailed norms, derived from a group of healthy
participants, of several relevant psycholinguistic variables. To the
best of our knowledge, this work is the first to provide such a high
number of quality ecological items (360), pertaining to so many
different (23) subcategories and providing indexes of seven relevant
psycholinguistics variables: age of acquisition, familiarity, lexical
frequency, manipulability, name agreement, typicality and visual
complexity, gathered from such a large number of participants
(n=236). Another main contribution of our study, compared to
previous recent normative works, is that it incorporates item
analyses, for those authors interested in selecting the more suitable
items according to their goals and recently collected norms on
Recent normative works have provided valuable data from a
high number of coloured items and have also presented ratings for
relevant psycholinguistics variables [19,20,30,28]. However, (i)
they have excluded theoretically relevant subcategories, such as
animals, body parts, buildings and vehicles (, also did not
evaluate typicality), (ii) they have provided ratings for only several
psycholinguistics variables: familiarity, name agreement and visual
complexity (, did not include body parts) or (iii) the number of
items evaluated is relatively sparse, compared to the 260 items
originally studied by S&V (147: ; 140: ; 174: ). The
category-specific literature has convincingly shown that there are
important differences within the living domain (e.g., animals and
plant life—fruits, flowers and vegetables) between the animals and
plant-life subcategories . Similarly, processing differences have
been reported within the non-living domain (e.g., tools, vehicles
and furniture), between small manipulable objects, such as tools,
and large outdoor objects, such as buildings . In their domain-
specific theory, Caramazza and collaborators posited that, for the
subcategories of items for which rapid identification confers
reproductive advantages, natural selection has produced special-
ized, dissociable neural pathways—modules [48,60,61]. According
to these proposals, such modules exist for animals and plant life,
although the domains of tools and conspecifics have recently been
incorporated into this view . Similarly, within the nonliving
thing domain, Warrington and McCarthy  reported a case
that revealed a clear dissociation within this domain: a greater
impairment in identifying small and manipulable objects, com-
pared to large and non-manipulable things. Regarding psycholin-
guistics variables, while familiarity , name agreement  and
visual complexity  have been shown to be significantly relevant
to the processing of pictorial and verbal material, both in control
participants and patients, other no less relevant variables, such as
age of acquisition , manipulability , typicality  and
word frequency  have also robustly revealed their impact on
normal and impaired processing of items. Consequently, works
providing normative data for these variables are, in our view,
particularly demanded in the object processing arena. Likewise,
the above commented semantic specialization (i.e., differences
between manipulable and non-manipulable things/biologically
derived modules), strongly recommend having a sufficient number
of items that make it possible to elucidate these theoretical issues.
Validity indexes showed that our stimuli had similar features as
those of other corpi and they presente high internal consistency as
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well; this suggests that the new corpus has adequate psychometric
characteristics. Likewise, the fact that our scales presented high
cross-language correlations with similar studies indicates that our
stimuli are suitable to be used in countries other than Spain and in
different cultures with different languages.
Table 2. Summary statistics for all the variables.
AoAFam LF (Log)Man TypVCNAH
3.633.56 15.560 3.113.652.550.720.94
SD1.30 0.88 14.190 1.260.860.74 0.280.87
Median3.44 3.5515.5203.52 3.742.460.82 0.70
Mode3.054.7814.190 1.223.37 1.53 1.000.00
Range5.36 3.65 11.3003.87 3.49 3.310.99 3.73
Min 1.371.329.0101.01 1.461.180.01 0.00
Max6.73 4.97 20.3104.88 4.954.49 1.003.73
Q1 2.59 2.87 14.3101.70 3.061.940.53 0.20
Q34.58 4.3616.630 4.21 4.42 3.14 0.96 1.57
Note: AoA=Age of acquisision; Fam=Familiarity, LF=Lexical frequency (natural logarithm); Man=Manipulability; Typ=Typicality; VC=Visual complexity; %
NA=Percentage of name agreement.
Table 3. Summary statistics for all the variables for each category.
AoAFam Man TypicalityVC LF (Log)%NA
M SDM SDM SDM SDM SDM SDM SD
1. Animals3.461.39 2.910.74 1.42 0.41 3.600.873.19 0.36 15.221.63 0.79 0.25
2. Birds3.88 1.213.01 0.751.45 0.283.47 0.673.25 0.4415.16 1.100.66 0.25
3. Body parts2.74 1.504.13 0.922.09 0.82 4.070.70 2.360.53 16.80 1.560.740.29
4. Flowers 4.18 1.15 3.430.672.060.25 3.820.69 2.73 0.5015.561.80 0.520.31
5. Fruits 3.501.463.92 0.83 3.71 0.21 3.750.93 1.79 0.3915.781.44 0.77 0.26
6. Insects 3.281.04 3.36 0.801.41 0.18 3.870.67 2.990.48 14.761.14 0.750.20
7. Marine creatures 3.12 0.993.860.54 4.260.563.63 1.672.63 0.7415.67 1.670.89 0.12
8. Nuts 3.57 0.72 3.700.543.670.473.85 0.701.96 0.3115.10 1.680.670.25
9. Trees 4.40 .813.28 0.53 1.71 0.24 3.710.49 2.820.4315.290.960.370.32
13. Desk material3.4188.8.131.524.550.204.090.571.830.5315.481.740.770.29
15. Furniture3.301.474.23 0.683.190.543.890.882.450.48 15.971.720.79 0.21
16. Jewellery 4.03 1.123.39 0.613.88 0.443.77 0.892.79 0.5015.97 1.73 0.660.23
17. Kitchen utensiles 3.88 1.433.84 1.01 4.410.30 3.681.072.20 0.6414.54 1.330.63 0.29
4.05 1.243.09 0.704.69 0.133.79 0.793.130.7315.05 1.740.77 0.31
19. Sports/Games 3.12 0.993.86 0.544.260.56 3.63 0.632.630.73 15.671.67 0.89 0.12
20. Tools 4.211.20 3.490.73 4.530.18 3.820.69 1.930.3914.742.07 0.700.28
21. Vehicles2.93 0.973.78.653.930.553.32 1.182.980.6216.831.830.83 0.15
22. Weapons3.911.012.59 .494.20 0.523.191.022.560.7615.801.890.780.25
23. Nature 2.851.013.71.791.49 0.593.66 0.732.680.7217.501.530.670.29
Note: AoA=Age of acquisision; Fam=Familiarity, LF=Lexical frequency (natural logarithm); Man=Manipulability; VC=Visual complexity; % NA=Percentage of name
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Although we have attempted to address the methodological
issues of this literature reviewed in the Introduction, there remains
one limitation in the current study: the fact that cognitive/
conceptual effects are able to drive the categorization beyond the
low level features. As mentioned in Introduction, we selected our
categories —and stimuli— in a ‘‘top-down perspective’’, based on
relevant theoretical reasons, and mainly derived from Cognitive
From a different perspective, vision studies from psychophysical
and neurophysiological field have, traditionaly, made used of
accurate low-level quantitative methods to define the physical
parameters of naturalistic photographs, in order to explore basic
aspects of the human visual system (see, e.g., [65–67]). However, it
should be recognized that the human visual system is sufficiently
adaptable to make possible that different low level features in the
stimuli can be compensated to obtain higher level invariant
categorizations. Clearly, this is something that cannot be taken for
granted and should be recognized in any study dealing with
Beyond the low level properties of objects, another relevant
point has been relatively ignored in the previous literature on
normative and semantic assessment studies. This point is related to
the control of the relations between objects specified by abstract
feature spaces (see , for a review). Most of the recently
developed corpi have been designed according to arbitrary criteria
for the selection of the categories and the assigning of their stimuli.
In contrast, an alternative selection method could take advantage
of the semantic structural descriptions derived from hierarchical
Bayesian models, which fits quite well the human performance in
semantic induction tasks [68,69]. This procedure should be
seriously considered by researchers in order to develop more
accurate instruments in this field.
To conclude, the present work provides a useful tool for
researchers examining language, memory, object- and word-
processing, particularly for those interested in comparing healthy
versus neurologically damaged populations. Accordingly, the new
instrument, in combination with other recently developed corpi, is
intended to be an ecological alternative to the corpus developed by
Snodgrass and Vanderwart thirty years ago, especially, but not
exclusively, in a Spanish-speaking population.
Colour photographs of the 360 items.
Normative psycholinguistic ratings for
alternative names, acceptable synonyms of each item,
plus ‘‘Don’t know’’ (DK), ‘‘Don’t remember’’ (DR), and
‘‘Tip of the tongue’’ (TOT) responses.
Proportion (in brackets) of target names,
ing a measure of item difficulty and two indexes of item
discrimination based on item-test correlations (point-
biserial and biserial).
Indexes of individual item analysis includ-
Table 4. Correlation matrix for naming performance and psycholinguistic variables.
AoA FamLF ManTyp VC%NAH
Note: AoA=Age of acquisision; Fam=Familiarity, LF=Lexical frequency; Man=Manipulability; Typ=Typicality; VC=Visual complexity; % NA=Percentage of name
Table 5. Correlations between current stimuli and those of
Adlington et al. (2009), Brodeur et al. (2010), Moreno-Martı ´nez
et al. (2011), Snodgrass and Vanderwart (1980), and Viggiano
et al. (2004)1.
Items (n)AoAFamLF Man VC%NA
50.84 .78 .74 n.e..66 .62
68n.e. .76 n.e..48 .68 .25
113 .99.98.99 .88 .92 .94
S&V 106 .812
.79 .62n.e. .76.41
80 n.e..73 n.e.n.e..80 .34
80n.e..77n.e.n.e. .84 .46
Note: AoA=Age of acquisision; Fam=Familiarity, LF=Lexical frequency;
VC=Visual complexity; % NA=Percentage of name agreement, n.e.=not
1=Viggiano et al.’s work studied two samples: English and Italian speakers
evaluated the same items.
2=39 items (in their original study, S&V presented AoA data only for some
A New Corpus of 360 High Quality Color Images
PLoS ONE | www.plosone.org7May 2012 | Volume 7 | Issue 5 | e37527
We wish to thank Sara Can ˜amo ´n for her help taking and selecting
photographs of the items. We also wish to thank Professors Gregory
Francis, Roberto Dell’Acqua and to one anonymous reviewer for their
valuable comments and observations which contributed to improve earlier
versions of the manuscript.
Conceived and designed the experiments: FJMM PRM. Performed the
experiments: FJMM PRM. Analyzed the data: FJMM PRM. Contributed
reagents/materials/analysis tools: FJMM. Wrote the paper: FJMM PRM.
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