Behav Res
DOI 10.3758/s13428-011-0092-x

iMap: a novel method for statistical fixation mapping of eye
movement data
Roberto Caldara & Sébastien Miellet# Psychonomic Society, Inc. 2011

Abstract Eye movement data analyses are commonly
based on the probability of occurrence of saccades andfixations (and their characteristics) in given regions ofinterest (ROIs). In this article, we introduce an alternative
method for computing statistical fixation maps of eye
movements—iMap—based on an approach inspired bymethods used in functional magnetic resonance imaging.
Importantly, iMap does not require the a priori segmentation of the experimental images into ROIs. With iMap,
fixation data are first smoothed by convolving Gaussian
kernels to generate three-dimensional fixation maps. This
procedure embodies eyetracker accuracy, but the Gaussian
kernel can also be flexibly set to represent acuity or
attentional constraints. In addition, the smoothed fixation
data generated by iMap conform to the assumptions of therobust statistical random field theory (RFT) approach,which is applied thereafter to assess significant fixation
spots and differences across the three-dimensional fixation
maps. The RFT corrects for the multiple statisticalcomparisons generated by the numerous pixels constituting
the digital images. To illustrate the processing steps ofiMap, we provide sample analyses of real eye movement
data from face, visual scene, and memory processing. The
iMap MATLAB toolbox is editable and freely available fordownload online (www.unifr.ch/psycho/ibmlab/).

R. Caldara (*)
Department of Psychology, University of Fribourg,
Faucigny 2,1700 Fribourg, Switzerlande-mail: roberto.caldara@unifr.ch
S. Miellet
Centre for Cognitive Neuroimaging,
Institute of Neuroscience and Psychology, University of Glasgow,
Glasgow, Scotland

Keywords Eye movements . Statistical fixation maps .
Data-driven analyses . Random field theory . Matlab toolbox

The human visual system is equipped with the mostsophisticated machinery to effectively adapt to the visual
world. Where, when, and how human eyes are moved togather information to adapt to the visual environment has
been a question that has fascinated scientists for more thana century. Javal (1879) coined the term saccade to describe
the rapid movement of the eyes produced during reading,an oculomotor phenomenon identified by Hering (1879)
and Lamare (1892) during this period. However, acomprehensive sense of the very nature of those ballistic
movements, a description of the use of fixations to gather
the information relevant to solving the task at hand, and thescientific definition of saccades came with Dodge (1916)
and the development of photographic techniques forrecording corneal reflections. This novel recording approach paved the way for the scientific study of eye
movements (see Wade, Tatler, & Heller, 2003).
Buswell (1935) published the first systematic study on
How People Look at Pictures: A Study of The Psychologyof Perception in Art. Buswell observed that trained anduntrained artists deployed similar fixation patterns toanalyze paintings. All observers shared similar oculomotor
behavior, deploying initial short fixations over the mainfeatures of the paintings, which were subsequently followed
by a series of longer fixations. Interestingly, when fixations
were collapsed across observers, they highlighted areas
containing salient or diagnostic parts of the images.
Critically, these observations revealed that eye movements
do not randomly sample the visual input space, but are
effective at solving problems in visual cognition. This work
was then followed by a series of studies, leading inBehav Res

particular to the seminal work of Yarbus (1965), which
extensively showed similar findings for diverse visualobjects. More importantly, Yarbus showed how top-down
factors modulate the eye movement strategies deployed byobservers to gather information from the very same picture,a discussion that attracted attention in the wider scientificcommunity.1 Since then, we have witnessed an explosion ofeye movement studies on the processing of language andvisual scenes (see Rayner, 1998, 2009), as well as studies
with clinical populations (see van Gompel, Fischer, Murray,& Hill, 2007).
Nowadays, the development of new technologies thathave increased the precision, ease, and affordability ofeyetracking devices has significantly impacted on the prominence of eye movement research. There is also an increasingawareness in the scientific community of the need to control
eye movements during any experiment in vision (e.g., YuvalGreenberg, Tomer, Keren, Nelken, & Deouell, 2008). Theseobservations have led to the prediction that eye movement
research will continue to gain importance in the future within
the vision sciences community.
Scientific disciplines rely on their own specific metrics.
Eye movement studies generate a quantity of rich data,
which traditionally have largely relied on measures fromtwo types of events characterizing where, when, and how
the eyes gather information from the visual world—
saccades (i.e., their latency, amplitude, direction, andoccurrence over time) and fixations (i.e., their location andduration)—as well as many measures derived from these
events, such as cumulative saccade length, pupil dilation,and so on. In order to characterize and isolate statisticaldifferences in the eye movements deployed to processvisual inputs, the large majority of the eye movement
literature has used a region- or area-of-interest (ROI or
AOI) approach. Strictly and formally speaking, every single
pixel of a digital image could be considered as a variable ofinterest to measure the occurrence of saccades andfixations, which results in a complex multidimensional
space. The goal of segmentation is to reduce the visual
input space (usually defined by thousands or millions ofpixels) of the digital images used during the experimental
tasks into something that is meaningful and easier toanalyze. Image segmentation is usually based on a mixtureof low-level boundaries of the object/feature shapes (lines,
curves, etc.) present in the digital images, as well as highlevel semantic a priori expectations that experimenters haveabout the parts constituting a particular visual object. Theseboundaries are typically related to object/feature locations.
More precisely, image segmentation is the process during
which pixels are clustered together and assigned a label,
1
Note that Yarbus (1965) recorded only a single observer for
demonstrating task effects on eye movement patterns.

such that pixels sharing a similar label also share particularvisual characteristics or semantic properties. This process
results in a set of regions that collectively cover the entire
image. Once the images used in a particular experiment aresegmented into ROIs, descriptive eye movements are thenmeasured, with the probability of fixation/saccade and theirrespective characteristics (i.e., number, duration, amplitude,etc.) calculated for each of the defined ROIs. The measuresobtained for those metrics are thereafter submitted toconventional statistical analyses.
So far, it has often been implicitly assumed that the
ROIs optimally represent the visual categories present in
the visual input space (e.g., for face processing, the eye,nose, and mouth regions). However, segmenting visual
inputs into ROIs is constrained by subjective evaluations,which is—by definition—problematic from a scientificpoint of view. For instance, how should the borders of avisual region representing the human eyes be defined?
Should both human eyes be considered as a single region?
Should the pixels outside the eye sclera be considered as
belonging to the eye region or not? If this is the case, howmany pixels of the skin should be included? Should the
pixels of this region be included by using a curvilinear,
elliptical, or rectangular shape? Obviously, besides anobjective definition of an ROI based on the human sclera,
the remaining options used to define ROIs for the eyeregion do not have an objective answer. For this reason,
there is great variability between eye movement studies inthe definition of ROIs representing the very same
information—for instance, the eye regions of faces (e.g.,
Barton, Radcliffe, Cherkasova, Edelman, & Intriligator,
2006; Henderson, Williams, & Falk, 2005; Orban de
Xivry, Ramon, Lefevre, & Rossion, 2008). The samedifficulties generalize to all of visual inputs. For instance,in the domain of visual scenes, it is difficult to define theobjects to be included in the ROI. Is it appropriate todetermine different regions for a human body embedded ina visual scene (e.g., head, neck, hands), or would a uniqueshape defining the body be more appropriate? Should an
ROI be strictly defined by using the edges of an object?
The physical boundaries of objects are usually used todefine ROIs. This choice appears sensible, but undercertain circumstances using ROIs might not be appropriate
for thoroughly and effectively capturing eye movement
behavior (see Fig. 1).
The more critical consequences of this drawback, however,rely on the fact that the subjective criteria used to define ROIs
compromise the potential to replicate findings across studies.
Since ROIs are based on the qualitative and quantitative
subjective evaluations of the experimenters, they engendernatural variations across authors, which in some cases lead todifficulty in generalizing observations across studies. Notethat other potential problems of using ROIs have also been

Behav Res

Fig. 1 (a) Example extracted from a trial of the animal visual searchtask used in Miellet et al.’s (2010) study. The red contour represents
an ROI based on the edges of the koala, a rule routinely used in theeye movement literature relying on ROIs. The white contours show
areas of the visual scene that were fixated significantly above chancelevel using iMap. The center of gravity of the location driving the
majority of fixations in this image is located outside the ROI. This
perceptual bias of the observers would therefore be inaccurately
reported by probability-of-fixation analyses based on ROIs. (b)Diagram illustrating the difficulty of defining a priori ROIs in faceprocessing studies (data from Caldara et al., 2010). The white contour
shows a significant area according to iMap, and blue contours show
examples of ROIs as commonly used in the literature. In this example,
it is difficult to objectively attribute the intermediary fixations to thenose or mouth regions

discussed in neuroimaging, particularly the dangers ofcircular analyses and “double dipping”—the use of the samedataset for selection and selective analysis (see Kriegeskorte,
Simmons, Bellgowan, & Baker, 2009). Therefore, thesubjective variation of ROIs alone could potentially explain
the absence of consistent effects across studies reported in
the eye movement literature and points toward a methodological problem.
To overcome these limitations, we have introduced a novel,robust, data-driven technique that does not require the a priori
segmentation of digital images used as stimuli into ROIs:
iMap. iMap generates fixation maps for each single
participant and every visual stimulus, resulting in an average
fixation map. The individual fixation maps can then be
averaged together, resulting in a group fixation map (Fig. 2).

The critical value and key innovative feature of the iMap
technique, however, is the ability to statistically compare
fixation maps, with an approach taking into account the
problem of the multiple comparisons generated by the pixel
space. To the best of our knowledge, iMap is the first freely
available technique integrating robust statistics in order togenerate unbiased, data-driven statistical fixation maps
from eye movements. iMap corrects for multiple comparisons, quantifies the effect size of the statistical differences,and provides descriptive measures routinely used in eye
movement research (i.e., number of fixations, average
fixation duration, fixation scan path length, and total
fixation duration).
The processing steps of the iMap method, its rationale
and logic, were very much inspired by methods used infunctional magnetic resonance imaging (fMRI). The threedimensional (3-D) fixation maps created with iMap can beconsidered as “activation maps” of eye movement data. In
fMRI studies, brain activations are represented by 3-D
maps containing many voxels (i.e., 3-D volumetric pixel
elements used to visualize and analyze fMRI data) overtime (which is considered the fourth dimension). It is
common practice to spatially smooth fMRI data by
applying Gaussian kernels prior to the statistical comparison of brain activations across conditions (e.g., comparing
neural responses for the processing of faces and of houses).
Firstly, the smoothing procedure improves anatomicalintersubject variability. Secondly, it increases signal-tonoise ratios by reducing the random noise in individual
voxels (Smith, 2003). Thirdly, this procedure ensures thatthe assumptions of random field theory (RFT; see below),commonly used to correct for multiple comparisons, aremet (Worsley & Friston, 1995). Conventionally, fMRI
results rely on massive univariate statistics testing for theeffect of interest in each brain voxel, which therefore results
in a large number of statistical comparisons, increasing the
likelihood of Type I errors. Hence, in fMRI, the results need
to be corrected for the multiple comparisons.
The statistical comparison of the 3-D fixation maps
generated by iMap shares this problem, since the 3-D
fixation maps contain thousands of pixels and, therefore,
also generate a large number of statistical comparisons. In
addition, similarly to voxels in the fMRI space, pixels are
not statistically independent; the data for a particular pixel
tend to be similar to those for nearby pixels. RFT (Adler,1981) is a recent branch of mathematics that has been
implemented in statistics to overcome this major limitation. RFT has been adapted and used to define theoreticalthresholds for smooth statistical maps in fMRI (Worsley etal., 1996). The RFT approach is based on two mainprocessing stages: First, it relies on the estimation of thesmoothness (spatial correlation) of the statistical maps,
and then these smoothness values are used to determineBehav Res

Fig. 2 General processing steps for the computation of a statistical
fixation map with iMap (adapted from Caldara et al., 2010). Individual
fixation maps are smoothed by convolving a Gaussian kernel on eachfixation. The resulting fixation maps for all trials are then averaged,
resulting in a single fixation map per condition. The differential

fixation map highlights significant eye movement biases. The
significant areas are determined by using the Pixel test (Chauvin etal., 2005). Finally, statistical fixation maps are produced that merge
the fixation patterns, the areas fixated significantly above chance level,
and the backgroundthe expected Euler characteristic at different thresholds.
This procedure estimates the threshold at which 5% ofequivalent statistical maps are expected to arise under thenull hypothesis. iMap relies on spatially normalizedsmoothed data, which therefore satisfy the formal constraints of the RFT used in fMRI. More precisely, iMap
applies the statistical Pixel test from the Stat4Ci toolbox(Chauvin, Worsley, Schyns, Arguin, & Gosselin, 2005),
which has been developed and validated for analyzingsmooth classification images. The sensitivity of the Pixel
test depends on the number of comparisons performed,
which is represented here by the size of the search space(i.e., the size of the digital images). The default search
space for iMap is the entire stimulus, but a specific search
space size can be specified. For instance, one could
consider that the background of a picture with a face does
not influence eye movements during face processing, andtherefore reduce the search space to pixels belonging to
the face only. To have a better understanding of some of
the limitations of using iMap, this and other caveats of theapproach will be addressed in the Discussion, after weprovide formal knowledge of the technique and someconcrete examples.

It is worth noting that methods sharing similarities with
iMap have been introduced by various authors before us (e.g.,
Barrington, Marks, Hsiao, & Cottrell, 2008; Bruce &Tsotsos, 2009; Buchan, Paré, & Munhall, 2007; Harding& Bloj, 2010; Henderson, 2003; Kita et al., 2010;
Pomplun, Ritter, & Velichkovsky, 1996; Tatler, Wade,
Kwan, Findlay, & Velichkovsky, 2010; Torralba, Oliva,
Castelhano, & Henderson, 2006; Wooding, 2002). Here,
we briefly present the characteristics that few of thesemethods share with iMap and what differentiate them fromit. Tatler, Baddeley, and Gilchrist (2005) used a fixed gridwith fixation counts in each cell and the Kullback–Leiber
divergence (KL) in order to test differences in probability
density functions. Contrary to iMap, these authors did notweight the probability density functions according tofixation durations. Moreover, because KL reports a single
index for each comparison, Tatler et al. (2005) could not
generate statistical fixation maps for single conditions (andtheir comparisons). Hence, in contrast with iMap, significant differences between conditions could not be localized inside the stimulus space. More recently, Tatler(2007) used Gaussian smoothing in order to generate
fixation maps. Nevertheless, this author still did notBehav Res

weight the density functions by the fixation duration, as
implemented in iMap. Moreover, no statistical test was
performed on individual or difference fixation maps.
Note that using a Gaussian smoothing on fixations,weighted by their durations, is not novel per se, and heatmap representations have become very popular in the last
few years (see, e.g., Barrington et al., 2008; Bruce &Tsotsos, 2009; Buchan et al., 2007; Harding & Bloj, 2010;
Henderson, 2003; Kita et al., 2010; Tatler et al., 2010;
Torralba et al., 2006; Watanabe, Matsuda, Nishioka, &Namatame, 2011; Wooding, 2002). However, in most ofthese cases, the heat maps were used only for illustration
purposes. More importantly, in comparison with iMap, no
statistical test was performed on these fixation maps inorder to locate significant effects within the stimulus
space. For instance, Buchan et al. (2007) or Tatler et al.
(2010) generated heat maps for visualizing eye movement
patterns, but they critically relied on ROIs to compute
statistics. Moreover, none of these approaches was
implemented as a freely available toolbox offering thenumerous statistical and descriptive analyses we provide
with iMap. For the whole stimulus space, iMap computes
and returns the number of fixations, the total fixation
duration, the mean fixation duration, the path length, andthe mean saccade length. In the areas fixated significantly
above the chance level after correction for multiple
comparisons, iMap also computes Z-scored fixation
durations (or number of fixations) and effect sizes(Cohen’s d). Finally, iMap can also produce mean fixation
durations, path lengths, total fixation durations, andnumber of fixations separated between significant areas
and the rest of the visual input.
To the best of our knowledge, the method used by
Leonards et al. (2007) is the one most similar to iMap.
These authors created fixation maps based on Gaussian
kernels, generated difference maps, and used robust
statistics to compare conditions. The main advantage ofiMap over their technique is its public availability, directaccess to the parameters used, and ease of use. Another
difference is that in Leonards et al.’s (2007) method, eachfixation is replaced by an elongated Gaussian distribution
around the landing point, with a spread determined by themagnitude and angle of the saccade used to get to thislocation (whereas iMap uses a circular Gaussian). This is avery interesting way to represent the distribution of landing
positions, depending of the direction and size of thesaccade. However, in iMap, the Gaussian kernel is used toapproximate a unique fixation location and not a distribution of fixations. In this sense, we think that using a circular(not an elongated) Gaussian kernel allows us to keep asmuch as possible to an assumption-free approach. In
addition, one could argue that representing the directionof the fixation with elongated Gaussians is valid, but only

for the few milliseconds following the arrival of thesaccade. Thereafter, a representation closer to the physiological constraints of the visual system, with a circular
foveal projection, might be more appropriate. But, similarly
to iMap, this approach raises novel questions: For instance,
for how long is an elongated representation the mostappropriate way to describe the data (for 20 ms? 40 ms?)?
Is the shape of the Gaussian dependent of the task at hand,on the background information, and so forth? Finally, it isworth noting that although the direction of the saccade
impacts on the landing distribution, the average acrosssaccade directions reveals a nearly circular Gaussian
distribution, as used in iMap (see Fig. 7 of Nuthmann &Henderson, 2010).
Other authors have used slightly different data-driven
approaches. We would particularly like to mention the
Scanmatch toolbox from Cristino, Mathôt, Theeuwes, andGilchrist (2010), the scan path similarity measure of
Jarodzka, Holmqvist, and Nyström (2010), and the approach
used by Mannan, Kennard, and Husain (2009). Interestingly,
Cristino et al. and Jarodzka et al. used similar methods: theNeedleman–Wunsch algorithm for Cristino et al., and theLevenshtein distance for Jarodzka et al. (see also Harding &Bloj, 2010, for a use of this method). A key advantage of
Cristino et al.’s method is that it is provided in an
implemented, freely available toolbox, allowing researchers
who are not expert in programming to use their approach.
The above approaches aim at describing and comparing
sequences of fixations, an analysis that is not implemented
in iMap. However, both Cristino et al.’s (2010) andJarodzka et al.’s (2010) techniques return a single number
reflecting how similar some fixation sequences are. Consequently, contrary to iMap, they do not allow for visualizing
and statistically testing which areas are fixated significantly
longer and which areas show significant differences
between two datasets. Note that Tatler et al. (2005) also
investigated temporal sequences of eye movements bycomputing KL for each specific order of fixations. This
strategy is also possible with iMap, since individual anddifference maps can be computed for single fixations orsequences of fixations in order examine the temporalcharacteristics of oculomotor behavior.
Carmi and Itti (2006) also implemented various metrics
for comparing fixation distributions. These approaches are
particularly interesting and appropriate for the question they
investigated, which was quantifying the agreement between
human attentional selection and attention priority maps. We
will not detail these metrics here, because none of themallows, as does iMap, for visualizing the effects at particularlocations in the stimulus space. The same limitation applies
to the Voronoi diagrams that Over, Hooge, and Erkelens(2006) used to provide a quantitative measure of theuniformity of fixation densities.

Behav Res

The approaches mentioned above raise the interesting
point that eye movements do not provide unequivocal
evidence about the measure of visual information being
used by observers(Posner, 1980). Hence, it is important tostress that iMap generates statistical fixation maps and not
so-called attentional maps (refer to, e.g., www.attentiontool.
com or http://eyequant.com). The Gaussian kernel is avariable that can be flexibly adjusted to characterize boththe visual information that can be sampled for a given
fixation and/or the potential error due to the eyetracker
accuracy. Therefore, the iMap user can flexibly set the kernel
size according to the specific hypothesis, materials, population, task, equipment, or presentation conditions. Researchers who would like to adopt an assumption-free approach
can set the kernel to a minimum value corresponding to theaccuracy of the eyetracker. This is the approach we adopted
in our previous articles, where the kernel size was set to 10
pixels, which in our setup related to the 0.5°-of-visual-angle
accuracy of our EyeLink 1000 desktop eyetracker. Importantly, the ability to manually adjust this parameter allows
researchers to adapt the kernel size not just to theirequipment, but to the participant as well. Hence, it ispossible to set a specific kernel for each individual, depending on the eyetracker accuracy, as measured during thecalibration procedure.
To sum up, despite its similarities to some previous
approaches, iMap remains an original, complementary tool
for analyzing eye movement data. The main difference
between iMap and the methods previously cited is that
iMap provides an implemented toolbox that allows users toshare an identical implementation of this technique and,above all, to compute robust statistical analyses. iMap
generates fixation distributions smoothed with Gaussian
kernels, transforming 2-D fixation maps, uniquely based on
fixation coordinate locations in x,y dimensional space, into
a 3-D fixation landscape, with z reporting the intensity offixations (weighted either by number of fixations or theirdurations; see Fig. 2).
iMap has already been used and successfully validated ina series of eye movement studies (Blais, Jack, Scheepers,
Fiset, & Caldara, 2008; Caldara, Zhou, & Miellet, 2010;
Jack, Blais, Scheepers, Schyns, & Caldara, 2009; Kelly etal., 2011; Kelly et al. 2011; Kelly, Miellet, & Caldara,
2010; Miellet, Caldara, & Schyns, 2011; Miellet, Zhou, He,
Rodger, & Caldara, 2010; Rodger, Kelly, Blais, & Caldara,2010). To illustrate the functionality and flexibility of iMap,
we provide examples below from real eye movement data
and diverse statistical comparisons (i.e., across different
groups of observers, in the same observers but withdifferent tasks, etc.). Importantly, iMap has been codedwith MATLAB; the code is fully editable and is freely
available to download and use. It is worth noting that weplan to continuously improve and update the iMap code

and also to add plug-ins for the toolbox in the future (e.g., aplug-in to generate 3-D fixation map movies or dynamic
statistical tests over time). The relevant information andfiles will always be freely available to use and to download
online (go to www.unifr.ch/psycho/ibmlab/). Finally, iMap
can be used on data acquired with any eyetracker andpreprocessing software that can provide a fixation report,
which includes the coordinates and duration of eachfixation, as well as an item number.

MethodInstallation and credits
iMap and the supporting functions (CiVol.m, HalfMax.m,exportfig.m, and stat_threshold.m) have to be copied to thesame folder as the input data files.
The exportfig code was written by Ben Hinkle in 2001
(bhinkle@mathworks.com) and can be downloaded fromwww.mathworks.com/matlabcentral/fileexchange/727.
The CiVol and HalfMax routines are part of the Stat4Ci
toolbox, which allows for performing the Pixel and theCluster tests, both based on RFT. The Stat4Ci toolbox isfree and can be downloaded from www.mapageweb.
umontreal.ca/gosselif/basic%20Stat4Ci%20tools/.
If you use the statistical functions of the Stat4Ci package
called with iMap (i.e., the Pixel or Cluster tests), please cite
Chauvin et al. (2005), listed below in the References.
The stat_threshold function was written by Keith
Worsley for the fmristat toolbox, which is free fordownload at www.math.mcgill.ca/~keith/fmristat.
An alternative to copying some of the supporting
functions into the data folder is to download the Stat4Ci
and fmristat toolboxes and add them to the MATLAB
path.
The Western Caucasian face images used in theexamples below of how to use the iMap toolbox belong
to the Karolinska Directed Emotional Faces database(KDEF; Lundqvist, Flykt, & Öhman, 1998).
Creating the input matrices
iMap can be used with eye movement data collected withany eyetracker. First, the data need to be preprocessed inorder to determine saccades and fixations. This filtering
process should be feasible for the majority of theanalysis software provided with eyetrackers, or alternatively, with a saccade detection algorithm based on eye
velocity.
The file resulting from this preprocessing will be used as
input data for the iMap function. The input data are a set of
matrices with a single fixation per line. The only dataBehav Res
Fig. 3 Validation of the fixation
map area when using iMap

required are the coordinates and duration of the fixations
and the item numbers. The order of the columns has noimportance, as they have to be specified in the iMap
function. Any other columns can be used for specifying
experimental conditions. A specific input data matrix has tobe created for each participant and/or condition. The input
files used by the iMap function are MATLAB .mat files(called data1.mat, data2.mat, ...). The matrix in each of thefiles is called “summary.” The matrices and files can becreated from any .txt file (e.g., a fixation report from the
EyeLink Data Viewer). Some of the examples below show
how to create such input data matrices.
Running the iMap function
iMap can then be used by calling a single function including a
set of parameters. The general format of the function is imap
(xSize, ySize, columnx, columny, columnduration, columnitem, dataset1, dataset2, standard deviation, maptype, firstfix,
backgroundfile, specificfix, searchspace).
The parameters of this function are defined as follows:
1. xSize, ySize: These parameters specify the stimulus
size in pixels (e.g., 382, 390).
Fig. 4 Statistical fixation maps
for the first and second datasets
and for their difference

2. columnx, columny, columnduration, columnitem:
These specify the column numbers for x,y coordinates,
fixation durations, and item number. Specifying theseparameters allows for flexible data format.
3. dataset 1, dataset 2: These specify the data .mat files thatwill be tested/compared. For example, [1:20], [21:40] willcompare Data Items 1–20 with Data Items 21–40. The
second dataset is optional; this field has to be left empty if
only one dataset is tested. If only one dataset is tested,
iMap produces a statistical map and eyetracking indexes
for this dataset. If two datasets are specified, iMap
provides the statistical maps and eyetracking indexes forboth datasets and the difference map and indexes.
4. standard deviation: The standard deviation in pixels of
the Gaussian kernel used for smoothing the data. The
default value is 10 pixels. Specifying a value isnecessary in order to specify the subsequent variablesof the function. With empty square brackets [] for thissetting, the default value will be used.
5. maptype: Values are 1 for fixation duration maps, 2 for
number-of-fixations maps. The default value is 1.
6. firstfix: This option discards the first fixation of eachtrial. This is particularly useful if the stimuli are
centered and a central fixation cross is presented beforeBehav Res

Fig. 5 The map in dataset1map.tif, with scale

the trials. 1 (the default option) keeps all of the
fixations, 2 ignores the first fixation of each trial.
7. backgroundfile (e.g., facebackground.tif): This option
allows for adding a background picture to the statistical
fixation maps. This value is optional and has to be set
to 0 or [] in order to specify the subsequent variables.
8. specificfix: This setting is used to select one or several
specific fixations—for example, [3 3] or [1 3]. Thisvalue is optional.
9. searchspace: By default, this is set to the size of thestimulus, xSize * ySize. The search space size can be
specified by directly indicating the number of pixels it
contains or by using a black-and-white picture (e.g.,
facemask.tif), where the black mask indicates thesearch space.

tiff, and diffpicedge.tiff, respectively. These maps can bemerged with a background picture. They display thesignificant areas based on a Pixel test. iMap also creates.
tif pictures of the scales of the Z-scored fixation measures,
called dataset1map.tif, dataset1map.tif, and Zdiffmap.tif,
respectively.
In addition, iMap generates .txt files with global
eyetracking measures for both datasets (called eyebasicdataset1.txt and eyebasicdataset2.txt). The columns in thesefiles are the number of fixations, the total fixation duration(in seconds), the mean fixation duration (in seconds), thepath length (in pixels), and the mean saccade length (inpixels). The lines correspond to the raw data files(participants, sessions). iMap also creates a text file called
Zscore.txt that includes the mean Z scores in the significant
area for (in respective columns) Dataset 1, Dataset 2,
Dataset 1 in Areas 1 and 2 (the areas in which the fixation
durations are significantly longer for Datasets 1 and 2,
respectively), and Dataset 2 in Areas 1 and 2.
iMap also produces a .txt file with Cohen’s d values(Cohen, 1988) for both datasets in Areas 1 and 2. This fileis called cohend.txt. Finally, imap creates .txt files with theeyetracking data in both the significant areas and the rest of
the picture. These files are called eyeareadataset1.txt andeyeareadataset2.txt and are organized in the following way:
mean fixation duration for Area 1, then for Area 2, then for
the rest of the picture. Path length, total fixation duration,and number of fixations are also organized according to thesame logic.

ExamplesImportantly, after you launch the function, a map willappear on the screen. To start using the program, maximize
the map, click on its top left and then bottom right corners,
then hit Enter (see Fig. 3).

For convenience, iMap and the supporting functions havebeen copied to each of the folders in these examples.

OutputExample 1 uses a subset of data from Caldara et al. (2010).
In this experiment, East Asian (EA) and Western Caucasian (WC) participants performed an old–new task on EA and
WC faces. The stimuli came from the KDEF (Lundqvist et al.,iMap creates .tif pictures of the single and difference
fixation maps, called dataset1picedge.tiff, dataset2picedge.
Table 1 Output in eyebasicdataset1.txt: Averages of global
eyetracking measures for
Dataset 1

Example 1

Number of
Fixations

Total Fixation
Duration

Mean Fixation
Duration

Path
LengthMean Saccade
Length29.09677
27.87097
27.67742
28.41935
27.87097
27.32258

7.483355
5.932745
7.76056
7.174361
4.960087.850284

0.2343271
0.2136969
0.2542954
0.2308422
0.1603119
0.2606777

1,539.18
1,699.69
1,674.231
1,353.442
2,130.377
1,726.404

48.12055
61.15729
54.56829
43.39324
69.2846
57.04416

Behav Res
Table 2 Output in eyeareadataset1.txt: Averages of eyetracking measures for significant areas in Dataset 1
Mean Fixation Duration (s)Path Length (pixels)

Total Fixation Duration (s)Number of Fixations

Area 1

Area 2

Rest

Area 1

Area 2

Rest

Area 1

Area 2

Rest

Area 1

Area 2

Rest

0.31780.18810.31660.29040.18080.25060.22970.28290.24480.16930.25210.21280.27780.25400.1775

135
63
300
108
89

47
117
71
53
59

65
90
89
82
63

0.89010.15661.42850.70690.26400.27530.45800.32570.32260.17107.12565.32166.84476.93845.0603

2.79
0.87
4.75
2.50
1.46

1.00
1.87
1.18
1.39
1.00

28.46
25.16
24.75
27.61
28.43

0.33230.31890.2759

266

113

86

1.53210.6282

6.5381

4.64

1.96

23.68

1998) and the Asian Face Image Database (Bang, Kim, &Choi, 2001). Presentation was gaze contingent, with a 2°, 5°,or 8° Gaussian aperture around the fixation location. For thisexample, the aperture size is 8°. A central fixation cross waspresented before each trial, then the 382 x 390 pixel stimulus
was randomly placed on an 800 x 600 screen. Eye position
was recorded every 8 ms with MATLAB. We then extracted
fixations and saccades (with a custom-made MATLAB
script) by using the same filter parameters as the EyeLink
software: saccade velocity threshold = 30°/s; saccade
acceleration threshold = 9,500°/s.
The data in this example are in .mat files (called data1.mat,data2.mat, ...), and the matrices are named “summary.”
The 1st contrast aims at comparing the respective eye
movement strategies deployed by WC [2 3 8 12 13 18]
versus EA [22 23 24 32 33 34] observers when learning
human faces (WC and EA face stimuli for both groups).
The values for maptype and firstfix were 1, so the fixation
duration maps were generated and the first fixation of eachtrial was included in the analysis. (Note that the position of
the stimulus was randomized on the screen.) The iMap
function could then be executed by typing
imap (382, 390, 6, 7, 5, 1, [2 3 8 12 13 18], [22 23 24
32 33 34], 10, 1, 1, 'facebackground.tif').
The statistical fixation maps produced are shown in Fig. 4.
This example shows the presence of significant fixationbiases across the two group of observers (i.e., areasdelimited by white borders). WC observers showed afixation bias toward the eyes and mouth (dataset1picedge,
red color in the diffpicedge difference map), whereas EA

observers showed a fixation bias toward the center of theface (dataset2picedge, blue color in the difference map).
The scaling could be obtained from the dataset1map.tif,
dataset2map.tif, and Zdiffmap.tif files. Figure 5 shows
dataset1map.tif with its scale.
The numerical outputs produced by the analysis arereported in Tables 1, 2, 3 and 4. Here, for simplicity, we
report only the global eyetracking measures and themeasures for the significant areas in Dataset 1.
For this particular task, it was appropriate to specify only
the face area as the search space (excluding the whitebackground). Indeed, if the default search space contains alarge number of pixels that are never fixated (because there
is no visual information, for instance), the Zcrit can be too
low to accurately capture the sensitivity of eye movement
patterns. Adjusting the search space to an appropriate levelof analysis can be done by using a mask to limit the search
to pixels containing information (here, a face mask):
imap (382, 390, 6, 7, 5, 1, [2 3 8 12 13 18], [22 23 24 32
33 34], 10, 1, 1, 'facebackground.tif', [], 'facemask.tif').
Reducing the search space increases the threshold, thus
decreasing the sensitivity, as highlighted by the iMap
analysis reported in Fig. 6.
Example 2This example uses a subset of the data from Miellet et al. (2010).
In this experiment, the participants had to detect andidentify an animal in a natural visual scene (full-screen
color pictures). The two main manipulations were the size

Table 3 Output in Z-score.txt: Z-scored fixation durations in significant areas
Single Maps

Difference Map

Dataset 1

Dataset 2

Dataset 1, Area 1

Dataset 1, Area 2

Dataset 1, Area 1

Dataset 1, Area 2

4.483855

4.909454

4.646505

2.261218

1.699236

5.669345

Behav Res
Table 4 Output in cohend.txt: Effect sizes in the significant areas on
the difference map
Cohen’s d Area 1

Cohen’s d Area 2

1.575624–1.697319

of the target and the size of a gaze-contingent artificial
central scotoma (“Blindspot”). The target and the Blindspot
sizes could be 0° (natural vision), 2°, 5°, or 8° of visual
angle. The raw eyetracking data were recorded in MATLAB, and preprocessing was done with the velocity-basedsaccade detection algorithm described above.
The target position was randomly distributed in thescene. In order to make the fixation maps, either the trials
could be considered individually (please refer to thesinglescenes.m Matlab code to obtain the details of thisprocedure) or fixation positions could be normalized
relative to the target position, by creating a new fixation
space where all the targets were centered in the middle ofthe screen (please refer to the normalizedscenes.m Matlab
code to obtain the details of this procedure).
The function singlescenes.m shows how to generate
data1... datan files containing the summary matrix with gazecoordinates, fixation durations, and item numbers. Here, wecreated such files only for the no-Blindspot (0°) and 5° target
conditions. Moreover, because there is no spatial normalization in this example, we selected only the fixations
corresponding to a specific item (here, Item 49). In thisexample, only one dataset (including 10 participants) was
considered, and the first fixation of each trial was excluded(there was a central fixation cross before each trial, and thestimuli covered the full screen). Hence, the iMap function
could be executed by typing the following sequence,
imap(600, 800, 1, 2, 3, 4, [1:10], [], 10, 1, 2, '5deg_9.tif'),
producing the scene shown in Fig. 7.
The function normalizedscenes.m is constructed similarly to singlescenes.m, aside from the fact that here
there is no need to filter the data for a specific trial, as
the target positions were spatially normalized. There wasFig. 6 Statistical fixation maps
for the first and second datasets
and for the difference map,
when using a mask limiting thesearch space

also no background, as different target stimuli wereconsidered. The iMap function could be executed bytyping the following sequence,
imap(600, 800, 1, 2, 3, 4, [1:10]),producing the image seen in Fig. 8.
Figure 9 shows an example of a selection of specificfixations (6–15 of each trial, which were late fixations):
imap(600, 800, 1, 2, 3, 4, [1:10], [], 10, 1, 2, [], [6:15]).
The syntax imap(600, 800, 1, 2, 3, 4, [1:10], [], 10, 1, 2,
0, [6:15]) would also be also accepted.
This example reveals fewer surrounding fixations related
to image exploration. In contrast, only a central hotspot ispresent, showing that most of the “late” fixations are on thetarget. This example also shows that it is possible to extract
individual or a series of fixations for an eye movement
analysis with iMap.
Example 3This example uses data collected during an experimentusing eye movements and a memory task similar to the one
described in Harkin and Kessler (2009).
This experiment involved learning letter positions on a
2 x 3 grid, testing high- versus low-checking participants
who were presented correct versus incorrect probes (seeFig. 10). The experiment was presented with E-Prime. The
raw data were recorded in SR Research’s .edf format andthen preprocessed using the SR Research DataViewer,with the fixation report exported in .txt format. The
memorytask.m function prepares the data for analysis
and runs iMap. The screen-based coordinates are also
centered on the stimulus.
The preparation code (memorytask.m) allows for considering specific conditions. We can specify Probe 1 as acorrect or incorrect probe (corP1 or incorP1) and several
time periods (Period 1 < 2 s; 2 s < Period 2 < 4 s; 4 s <Period 3). The fixation maps reveal specific patterns foreach time period.

Behav Res

Fig. 7 Statistical fixation map (fixation durations) for a specific item,with the first fixation of each trial excluded and the search spacecovering the entire stimulus/screen

Fig. 9 Statistical fixation map for spatially normalized items and latefixationsFigure 11 shows the fixation pattern for the learning
stage (period), regardless of the probe correctness or theparticipant group (note that a specific analysis showed noeffect of these factors during learning). We used thefollowing function:using the following parameters in the input of the iMap
function:imap(396, 288, 1, 2, 3, 4, [lowcheck highcheck], [],10, 1, 1).
These data clearly show that the central fixation crossappearing before the beginning of the trial impacted on
the fixation pattern. In such experimental situations (withno randomization of stimulus location and identical first
fixation locations for all the trials), it is recommended
that the first fixation be excluded. Figure 12 presents thesame analysis when the first fixation was excluded, byimap(396, 288, 1, 2, 3, 4, [lowcheck highcheck], [],10, 1, 2).
The statistical fixation map reveals significant hotspots
on each of the six positions where the letters could appear.
It also shows an upper-field bias.
Figure 13 shows the fixation pattern during the delay(Period 2), regardless of the probe correctness or theparticipant group. During the delay, no information waspresented on the screen. The statistical fixation maps
revealed central fixations with an upper-field bias and noeffect of the probe or the group of participants.
Interestingly, the fixation maps were different for lowversus high-checkers in the third time period when the probe
was incorrect (see Fig. 14). During the third period, the empty
grid was presented, and the participant had to indicate thelocation of the probe. The hotspots, at the top of the fixation
maps, indicate that the participants were gazing at the
instruction (probe, the letter they had to localize). Looking
at the incorrect probe was sufficient for a response from thelow-checkers. In contrast, the high-checkers verified on thegrid (which was empty during this period) before answering.

Discussion

Fig. 8 Statistical fixation map for spatially normalized items

We developed an alternative method for analyzing eye
movement data: iMap. As in previous approaches (e.g.,
Barrington et al., 2008; Bruce & Tsotsos, 2009; Buchan etal., 2007; Harding & Bloj, 2010; Henderson, 2003; Kita etal., 2010; Pomplun et al., 1996; Tatler et al., 2010;

Behav Res
Fig. 10 Presentation of thestimuli used in Example 3 andthe time course of the trials

Torralba et al., 2006; Wooding, 2002), this method does
not rely on the subjective definition of ROIs; in fact, it
simply does not require the use of ROIs. Crucially,
however, iMap offers some advantages relative to previous
methods. First, it relies on robust statistics to assess thesignificance of effects. Second, it is coded as an editable
toolbox for MATLAB, freely available for download anduse (www.unifr.ch/psycho/ibmlab/).
To illustrate the functionality and flexibility of thistoolbox, we have provided three examples. The resultsfrom those examples, coupled with those from our previous
work on face (Blais et al., 2008; Caldara et al., 2010; Jacket al., 2009; Kelly et al., 2011; Kelly et al., 2011; Kelly etal., 2010; Miellet et al., 2011; Rodger et al., 2010) andscene (Miellet et al., 2010) processing—which are largely
consistent with the Western Caucasian (WC) eye movement
literature and the literature on East Asian (EA) observers(Kita et al., 2010; Watanabe et al., 2011)—demonstrate thatthe toolbox effectively captures eye movement sensitivity
for the tasks at hand. In the first dataset, we initiallycompared the fixation strategies deployed by WC and EA
observers while learning WC and EA faces. This comparison resulted in significant fixation biases across observers.
WC observers fixated the eye region more than did EA
observers, whereas EA observers fixated the central part of
the face more than did WC observers. Additional analyses
also revealed similar fixation patterns for both types ofstimuli (WC vs. EA faces) or correct versus incorrect faceFig. 11 Fixation map of the learning stage, calculated across all
participants and probe conditions

Fig. 12 Fixation map of the learning stage, calculated across all
participants and probe conditions without the first fixation

Behav Res

Fig. 13 Statistical fixation map for the delay period (empty screen)

recognition performance (see Caldara et al., 2010). In thesecond dataset, observers had to detect and identify ananimal in a natural visual scene. We presented iMap
analyses for the natural digital images and for digital
images normalized (centered) on the position of the animal.
These analyses showed significant fixation hotspots on the
search target. Additional analyses revealed fixations on thetargets despite large Blindspots and similar fixation patterns
for EA versus WC participants (see Miellet et al., 2010),
along with the flexibility of using a subset of fixations forgenerating the statistical maps. Finally, in the last dataset,
we compared high- and low-checking observers in amemory task. This analysis showed that high- and lowcheckers deploy different strategies when confronted with
ambiguous/erroneous information.
iMap was inspired by methods in fMRI and it suffers forvery similar caveats, which we will address in turn,beginning with the choice of the width of the standard
Fig. 14 Statistical fixation maps
for low- versus high-checkers
during the third period in theincorrect-probe condition

deviation of the Gaussian kernel used during the smoothing
and the normalization procedures.
This parameter (i.e., width of the Gaussian kernel) is
clearly dependent on the experimental stimuli used in theeye movement study and on the task at hand. In neuroimaging, this choice is perhaps easier, as the width of theGaussian should be no larger than the brain area or regionof interest; for instance, it would be between 3 and 6 mm
for full width at half maximum for a small region such as
the fusiform face area (e.g., Caldara & Seghier, 2009;
Caldara et al., 2006; Rossion et al., 2003; Schiltz et al.,2006), or up to 12 mm for full width at half maximum for alarger region, such as the insula (see Mutschler et al.,2007). The logic is similar for eye movement analyses. In
our previous work, we were very careful not to “oversmooth” our data. We used Gaussian kernels with astandard deviation covering approximately 0.5° of visual
angle (Blais et al., 2008; Caldara et al., 2010; Jack et al.,2009; Kelly et al., 2011; Kelly et al., 2011; Kelly et al.,2010; Miellet et al., 2011; Miellet et al., 2010; Rodger etal., 2010), which is roughly the size of a fourth of the fovea(Hood & Finkelstein, 1986). We thought that this was asensible choice for the question we aimed to address:
investigating cultural diversity in face processing. However,this would not be the optimal parameter for analyzing any
eye movement task. For instance, let’s assume that wewould like to investigate the role of pupil size in theevaluation of attractiveness for a series of human facestimuli. In this case, it would be necessary to significantly
decrease the standard deviation of the Gaussian kernel inorder to capture subtle differences in the fixation patterns
falling in the pupil/eye region. As for neuroimaging, there
is no governing rule for defining the size of the standard
deviation of the Gaussian kernel. Experimenters mustevaluate and back up their choice with existing knowledge
from the literature and also justify their choices with respect
to the stimuli, the equipment, and the task used in theexperiment.

Behav Res

As in neuroimaging with data recorded from brains ofdifferent participants, iMap requires a normalized space inwhich to perform statistical analyses. Without entering
into the details of this procedure in fMRI, there are several
approaches used to normalize the human brains ofdifferent participants. The most commonly usedapproaches involve the realignment of brains into the
Tailairach (Talairach & Tournoux, 1988) or the Montreal
Neurological Institute (MNI) standard template spaces.
This process ensures that the comparison of voxels acrossparticipants will be valid. Similarly, iMap requires that the
fixation landscapes created for a particular condition bebuilt on a homogenous space, such that a given fixationfor one item is fully comparable to a fixation on the same
location for another item. Therefore, to meet this requirement, the faces we used in our previous studies (Blais etal., 2008; Caldara et al., 2010; Jack et al., 2009; Kelly etal., 2011; Kelly et al., 2011; Kelly et al., 2010; Miellet etal., 2011; Rodger et al., 2010), and in the present
examples, were normalized for their eye and mouth
positions. In the example we provided for visual scenes,
we demonstrated that is also possible to normalize naturalscenes, by arbitrarily centering the object of interest in the
middle of the scene (see Miellet et al., 2010); note that thetask used here was to find and identify an animal.
However, iMap does not prevent the analysis of a uniqueinput space, as long as many eye movement samples arecollected for a particular input space to ensure thestatistical validity of the analysis.
It is worth noting that iMap has been developed toanalyze where and when eye movements are performed byobservers. As illustrated by the previous examples, iMap
can extract (and compare) the fixation maps of eachparticular fixation (first, second, etc.). This descriptive
analysis provides information on the time course of
fixations. Recently, there have been various fruitfulattempts to integrate these measures occurring over time
and to extract the occurrence of statistically significant
sequences in the scan paths used by the observers (e.g.,
Cristino et al., 2010; Jack et al., 2009).
To sum up, iMap can analyze eye movement data with arobust data-driven approach that generates statistical fixation
maps. As with every novel method, we anticipate improvements in the near future arising from the feedback ofpotential users. We aim to keep the iMap method updated,
and will freely provide new versions of the MATLAB
toolbox code online (www.unifr.ch/psycho/ibmlab/). We
believe that various approaches and methods are necessary
in any scientific discipline, so that researchers will be able toflexibly use the most appropriate method to answer thequestion at hand. We hope that users will help us improve
iMap and eventually build bridges with other data-driven
MATLAB-based toolboxes for eye movement analysis.

Author Note Both authors were supported by the Economic andSocial Research Council and the Medical Research Council (ESRC/
RES-060-25-0010). We thank Helen Rodger, Benjamin W. Tatler, andan anonymous reviewer for their comments on a previous version of
the article.

References
Adler, R. J. (1981). The geometry of random fields. New York: Wiley.
Bang, S., Kim, D., & Choi, S. (2001). Asian face image database.
Pohang, Korea: Pohang University of Science and Technology,
Intelligent Multimedia Lab.
Barrington, L., Marks, T. K., Hsiao, J. H., & Cottrell, G. W. (2008).
NIMBLE: A kernel density model of saccade-based visualmemory. Journal of Vision, 17(14), 1–14. 8(14).
Barton, J. J., Radcliffe, N., Cherkasova, M. V., Edelman, J., &Intriligator, J. M. (2006). Information processing during facerecognition: The effects of familiarity, inversion, and morphingon scanning fixations. Perception, 35, 1089–1105.
Blais, C., Jack, R. E., Scheepers, C., Fiset, D., & Caldara, R. (2008).
Culture shapes how we look at faces. PLoS ONE, 3, e3022.
Bruce, N. D., & Tsotsos, J. K. (2009). Saliency, attention, and visual
search: An information theoretic approach. Journal of Vision, 9(5), 1–24.
Buchan, J. N., Paré, M., & Munhall, K. G. (2007). Spatial statistics ofgaze fixations during dynamic face processing. Social Neuroscience, 2, 1–13.
Buswell, G. T. (1935). How people look at pictures: A study of thepsychology of perception in art. Chicago: University of Chicago
Press.
Caldara, R., & Seghier, M. L. (2009). The fusiform face area responds
automatically to statistical regularities optimal for face categorization. Human Brain Mapping, 30, 1615–1625.
Caldara, R., Seghier, M. L., Rossion, B., Lazeyras, F., Michel, C., &Hauert, C. A. (2006). The fusiform face area is tuned forcurvilinear patterns with more high-contrasted elements in theupper part. NeuroImage, 31, 313–319.
Caldara, R., Zhou, X., & Miellet, S. (2010). Putting culture under the“spotlight” reveals universal information use for face recognition.
PLoS ONE, 5, e9708. doi:10.1371/journal.pone.0009708
Carmi, R., & Itti, L. (2006). The role of memory in guiding attention
during natural vision. Journal of Vision, 4, 898–914. 6(9).
Chauvin, A., Worsley, K. J., Schyns, P. G., Arguin, M., & Gosselin, F.
(2005). Accurate statistical tests for smooth classification images.
Journal of Vision, 1, 659–667. 5(9).
Cohen, J. (1988). Statistical power analysis for the behavioral
sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Cristino, F., Mathôt, S., Theeuwes, J., & Gilchrist, I. D. (2010).
ScanMatch: A novel method for comparing fixation sequences.
Behavior Research Methods, 42, 692–700.
Dodge, R. (1916). Visual motor functions. Psychological Bulletin, 13,421–427.
Harding, G., & Bloj, M. (2010). Real and predicted influence of image
manipulations on eye movements during scene recognition.
Journal of Vision, 8, 1–17. 10(2).
Harkin, B., & Kessler, K. (2009). How checking breeds doubt:
Reduced performance in a simple working memory task.
Behaviour Research and Therapy, 47, 504–512.
Henderson, J. M. (2003). Human gaze control during real-world scene
perception. Trends in Cognitive Sciences, 7, 498–504.
Henderson, J. M., Williams, C. C., & Falk, R. J. (2005). Eye
movements are functional during face learning. Memory &Cognition, 33, 98–106.

Behav Res
Hering, E. (1879). Uber Muskelgerausche des Auges. Sitzungsberichte
der kaiserlichen Akademie der Wissenschaften in Wien.
Mathematisch-naturwissenschaftliche Klasse. Abt, III(79), 137–
154.
Hood, D. C., & Finkelstein, M. A. (1986). Sensitivity to light. In K. R.
Boff, L. Kaufman, & J. P. Thomas (Eds.), Handbook ofperception and human performance, vol. 1: Sensory processes
and perception (pp. 5.1–5.66). New York: Wiley.
Jack, R. E., Blais, C., Scheepers, C., Schyns, P. G., & Caldara, R.
(2009). Cultural confusions show that facial expressions are notuniversal. Current Biology, 19, 1543–1548.
Jarodzka, H., Holmqvist, K., & Nyström, M. (2010, March). A vectorbased, multidimensional scanpath similarity measure. Paper
presented at the Eye-Tracking Research and Applications(ETRA) conference, Austin, TX.
Javal, É. (1879). Essai sure la physiologie de la lecture. Annales
d’Oculistique, 82, 242–253.
Kelly, D. J., Jack, R. E., Miellet, S., De Luca, E., Foreman, K., &Caldara, R. (2011). Social experience does not abolish cultural
diversity in eye movements.
Kelly, D. J., Liu, S., Rodger, H., Miellet, S., Ge, L., & Caldara, R.
(2011). Developing cultural differences in face processing.
Developmental Science.
Kelly, D. J., Miellet, S., & Caldara, R. (2010). Culture shapes eye
movements for visually homogeneous objects. Frontiers in
Perception Science. doi:10.3389/fpsyg.2010.00006. 1(6).
Kita, Y., Gunji, A., Sakihara, K., Inagaki, M., Kaga, M., Nakagawa,E., et al. (2010). Scanning strategies do not modulate face
identification: Eye-tracking and near-infrared spectroscopy study.
PLoS ONE, 5, e11050.
Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S., & Baker, C. I.
(2009). Circular analysis in systems neuroscience: the dangers of
double dipping. Nature Neuroscience, 12, 535–540.
Lamare, M. (1892). Des mouvements des yeux dans la lecture.
Bulletin et Mémoire de la Societé Franç-aise d’Ophthalmologie,
10, 354–364.
Leonards, U., Baddeley, R., Gilchrist, I. D., Troscianko, T., Ledda, P.,& Williamson, B. (2007). Mediaeval artists: Masters in directing
the observers’ gaze. Current Biology, 17, R8–R9.
Lundqvist, D., Flykt, A., & Öhman, A. (1998). The Karolinska
Directed Emotional Faces [Database]. Stockholm, Sweden:
Karolinska Institute.
Mannan, S. K., Kennard, C., & Husain, M. (2009). The role of visualsalience in directing eye movements in visual object agnosia.
Current Biology, 19, R247–R248.
Miellet, S., Caldara, R., & Schyns, P. (2011). Local Jekyll and global
Hyde: The dual identity of face identification. Psychological
Science.
Miellet, S., Zhou, X., He, L., Rodger, H., & Caldara, R. (2010).
Investigating cultural diversity for extrafoveal information use invisual scenes. Journal of Vision, 21(6), 1–18. doi:10.1167/10.6.21.
10(6).
Mutschler, I., Schulze-Bonhage, A., Glauche, V., Demandt, E., Speck,O., & Ball, T. (2007). A rapid sound-action association effect inhuman insular cortex. PLoS ONE, 2, e259.
Nuthmann, A., & Henderson, J. M. (2010). Object-based attentional
selection in scene viewing. Journal of Vision, 20, 1–12. 10(8).
Orban de Xivry, J. J., Ramon, M., Lefevre, P., & Rossion, B. (2008).
Reduced fixation on the upper area of personally familiar faces
following acquired prosopagnosia. Journal of Neuropsychology,
2, 245–268.
Over, E. A., Hooge, I. T., & Erkelens, C. J. (2006). A quantitative
measure for the uniformity of fixation density: The Voronoi
method. Behavior Research Methods, 38, 251–261.

Pomplun, M., Ritter, H., & Velichkovsky, B. (1996). Disambiguatingcomplex visual information: Towards communication of personalviews of a scene. Perception, 25, 931–948.
Posner, M. I. (1980). Orienting of attention. Quarterly Journal of
Experimental Psychology, 32, 3–25.
Rayner, K. (1998). Eye movements in reading and information
processing: 20 years of research. Psychological Bulletin, 124,
372–422.
Rayner, K. (2009). Eye movements and attention in reading, scene
perception, and visual search. Quarterly Journal of Experimental
Psychology, 62, 1457–1506.
Rodger, H., Kelly, D., Blais, C., & Caldara, R. (2010). Inverting faces
does not abolish cultural diversity in eye movements. Perception,
39, 1491–1503.
Rossion, B., Caldara, R., Seghier, M., Schuller, A. M., Lazeyras, F., &Mayer, E. (2003). A network of occipito-temporal face-sensitive
areas besides the right middle fusiform gyrus is necessary fornormal face processing. Brain, 126, 2381–2395.
Schiltz, C., Sorger, B., Caldara, R., Ahmed, F., Mayer, E., Goebel, R.,
et al. (2006). Impaired face discrimination in acquired prosopagnosia is associated with abnormal response to individual faces in
the right middle fusiform gyrus. Cerebral Cortex, 16, 574–586.
Smith, S. M. (2003). Preparing fMRI data for statistical analysis.
Oxford: Oxford University Press.
Talairach, J., & Tournoux, P. (1988). Co-planar stereotaxic atlas of
the human brain. Stuttgart: Thieme.
Tatler, B. W. (2007). The central fixation bias in scene viewing:
selecting an optimal viewing position independently of motor
biases and image feature distributions. Journal of Vision, 4, 1–17.
7(14).
Tatler, B. W., Baddeley, R. J., & Gilchrist, I. D. (2005). Visualcorrelates of fixation selection: Effects of scale and time. Vision
Research, 45, 643–659.
Tatler, B. W., Wade, N. J., Kwan, H., Findlay, J. M., & Velichkovsky,
B. M. (2010). Yarbus, eye movements, and vision. i-Perception,
1, 7–27.
Torralba, A., Oliva, A., Castelhano, M. S., & Henderson, J. M. (2006).
Contextual guidance of eye movements and attention in realworld scenes: The role of global features in object search.
Psychological Review, 113, 766–786.
van Gompel, R. P. G., Fischer, M. H., Murray, W. S., & Hill, R. L.
(2007). Eye movements: A window on mind and brain.
Amsterdam: Elsevier.
Wade, N. J., Tatler, B. W., & Heller, D. (2003). Dodge-ing the issue:
Dodge, Javal, Hering, and the measurement of saccades in eyemovement research. Perception, 32, 793–804.
Watanabe, K., Matsuda, T., Nishioka, T., & Namatame, M. (2011).
Eye gaze during observation of static faces in deaf people. PLoS
ONE, 6, e16919.
Wooding, D. S. (2002). Eye movements of large populations: II.
Deriving regions of interest, coverage, and similarity using
fixation maps. Behavior Research Methods, Instruments, &Computers, 34, 518–528.
Worsley, K. J., & Friston, K. J. (1995). Analysis of fMRI time-series
revisited—again. NeuroImage, 2, 173–181.
Worsley, K. J., Marrett, S., Neelin, P., Vandal, A. C., Friston, K. J., &Evans, A. C. (1996). A unified statistical approach for determining significant signals in images of cerebral activation. Human
Brain Mapping, 4, 58–73.
Yarbus, A. L. (1965). Role of eye movements in the visual process.
Moscow: Nauka.
Yuval-Greenberg, S., Tomer, O., Keren, A. S., Nelken, I., & Deouell,
L. Y. (2008). Transient induced gamma-band response in EEG asa manifestation of miniature saccades. Neuron, 58, 429–441.