sam v. wass phd · 2014. 4. 16. · sam v. wass phd medical research council cognition and brain...
TRANSCRIPT
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
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Comparing methods for measuring peak look duration: are individual differences observed on
screen-based tasks also found in more ecologically valid contexts?
Sam V. Wass PhD
Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK
Correspondence address: Sam Wass, MRC Cognition and Brain Sciences Unit, 15 Chaucer
Road, Cambridge CB2 7EF. Tel: +44 (0)1223 355294.
Email: [email protected]
Abstract – 240 words; Main text – 6709 words; 4 figures; 2 tables.
Acknowledgements: Thanks to Kaya de Barbaro and Emily Jones for reading drafts of the
manuscript and for helpful discussions. Thanks to Nurah Ahmad, Amada Worker, Victor Shierly,
Jaimie Bartholomew for performing the video coding on which these analyses were based. Thanks
to Ronny Geva and Hagar Harel for advice about focused attention and to Peter Watson for advice
on statistics. This work was supported by a British Academy Postdoctoral Fellowship.
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SUBSTANTIALLY CHANGED PASSAGES MARKED IN RED
Abstract
Convergent research points to the importance of studying the ontogenesis of sustained attention
during the early years of life, but little research hitherto has compared and contrasted different
techniques available for measuring sustained attention. Here, we compare methods that have been
used to assess one parameter of sustained attention, namely infants’ peak look duration to novel
stimuli. Our focus was to assess whether individual differences in peak look duration are stable
across different measurement techniques. In a single cohort of 42 typically developing 11-month-
old infants we assessed peak look duration using six different measurement paradigms (four screen-
based, two naturalistic). Zero-order correlations suggested that individual differences in peak look
duration were stable across all four screen-based paradigms, but no correlations were found
between peak look durations observed on the screen-based and the naturalistic paradigms. A factor
analysis conducted on the dependent variable of peak look duration identified two factors. All four
screen-based tasks loaded onto the first factor, but the two naturalistic tasks did not relate, and
mapped onto a different factor. Our results question how individual differences observed on screen-
based tasks manifest in more ecologically valid contexts.
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Introduction
Research is increasingly suggesting that early-developing, domain-general aspects of attentional
control may mediate subsequent skill acquisition in a variety of areas (e.g. Heckmann, 2006;
Karmiloff-Smith, 1998; Wass et al., 2012). For example, aspects of domain-general attentional
control have been shown to predict, on starting school, children’s’ subsequent learning on literacy
and numeracy tasks (e.g. Welsh et al., 2010). And research into the development of attentional
control within clinical disorders suggests that early disruption to attentional control may play a key
role in impairing early learning in social settings, for example during word learning, leading to
subsequent catastrophic developmental cascades (e.g. Karmiloff-Smith, 1998). This suggests the
importance of researching the ontogenesis of attentional control during the first few years of life.
Cohen suggested that infant attention involves at least two different mechanisms: an attention-
getting process which determines whether an individual will orient toward a stimulus presented in
his periphery, and an attention-holding process which determines how long his attention will be
maintained once he fixates (Cohen, 1972). This second phase, the attention-holding process, is
commonly described as ‘sustained attention’ (Richards, 2011). However, although individual
differences in attention are frequently reported in applied and developmental psychology, the terms
used are rarely precisely defined and are conventionally assessed using a variety of methods.
Historically, the most widely used technique for measuring infants’ looking behaviour involves
presenting static stimuli using a slide projector or computer screen across a number of discrete but
contiguous trials; the infant’s viewing behaviour is coded either live by an experimenter viewing
the infant on a video feed, or post hoc (Colombo & Mitchell, 2009). Two variables are typically
derived: peak look duration, the duration of the longest unbroken look to the screen, and habituation
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rate, i.e. the rate of change of looks over time. Colombo and Mitchell argued in favour of peak look
duration as the better metric of individual and developmental differences in visual attention during
infancy because it is more reliable, and shows more robust relationships with long-term cognitive
outcomes (Colombo & Mitchell, 1990).
Previous research has demonstrated that peak look duration to novel, static, screen-based stimuli
shows a U-shaped trajectory over the first year of life (Colombo & Mitchell, 2009; Colombo &
Cheatham, 2006; Courage et al., 2006). Research has also robustly demonstrated that peak look
duration to novel stimuli during the first year of life relates negatively with long-term cognitive
outcomes: shorter look duration during the first year is associated with better performance on later
IQ and language measures (Colombo, 1993; McCall & Carriger, 1993; Tamis-LeMonda &
Bornstein, 1989) and recognition memory (Rose et al., 2002; Rose et al., 2003a, 2003b). Shorter
looking is also associated with higher pre-existing knowledge bases and general arousal levels (de
Barbaro et al., 2011; Dixon & Smith, 2008).
An alternative technique for assessing looking durations during infancy involves presenting
dynamic stimuli on a computer screen (Courage et al., 2006; Shaddy & Colombo, 2004; see
Richards, 2010 for a review). This work has generally used either TV clips (e.g. Richards &
Anderson, 2004) or specially filmed naturalistic or semi-naturalistic dynamic scenes (Wass et al.,
2011). These techniques have been used to investigate how autonomic indices change in different
attention states (Richards, 2011; Richards & Cronise, 2000), how looking behaviour towards the
screen changes over time (Anderson et al., 1987; Richards & Anderson, 2004), and how these
changes are different in children with Attention Deficit Hyperactivity Disorder (ADHD) (Lorch et
al., 2004). To our knowledge, no research has investigated whether individual differences in look
duration are consistent across static vs dynamic looking time paradigms.
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A third paradigm that has been used to assess looking durations involves presenting a number of
unfamiliar objects consecutively or concurrently in a table-top setting, and performing video coding
post hoc to analyse looking behaviour. For example, Kannass & Oakes (2008) videoed 9-month-old
and 31-month-old infants playing with toys, in both single-object (objects presented consecutively)
and four-object (objects presented concurrently) conditions; they also measured 31-month language
performance in the same children (see also Sarid & Breznitz, 1997). They found that shorter look
durations in the single-object task correlated with larger vocabularies at 31 months (Kannass &
Oakes, 2008). For the multiple object condition, however, they found the opposite relationship:
longer durations at 9 months correlated with larger vocabularies at 31 months (see also Choudhury
& Gorman, 2000).
Despite the strong face similarities between these paradigms, no previous research has assessed
whether individual differences using one type of looking time paradigm are consistent across
different assessment techniques. A number of studies have addressed this indirectly, but none
directly. Kagan & Lewis (1965) examined the relationship between looking behaviour towards
static stimuli at 6 and 13 months and the amount of free-play locomotor activity at 13 months, and
found that infants with long fixation times at 6 and 13 months were more sedentary during free
play. Coldren found that infants’ attention to stimuli in laboratory tasks correlated with the attention
to their caregiver in face-to-face interactions at 3- and 4-month-olds but not at 6 months (Coldren,
unpublished data, described in Colombo & Mitchell, 1990). Pêcheux & Lecuyer (1983) found with
4-month-olds that fixation time towards static stimuli was positively correlated with their visual
exploration of a toy.
This gap in the literature is important for a number of reasons. As we note in Part 2, there are a
number of marked differences between these different looking time paradigms, such as: the size of
the target towards which attention is being directed, the presence or absence of movement in the
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target or periphery of the visual field of the child, and the relative luminance of the target relative to
other elements within the infants’ field of view (Figure 2). In the absence of data showing cross-
paradigm consistency, we cannot be sure how individual differences in attention as assessed using
screen-based tasks might relate to individual differences in attention in naturalistic settings. Are the
dissimilarities between screen-based and naturalistic attention tasks documented in Figure 2
incidental to the individual differences that are assessed on these tasks? Or are they central to them?
Within the habituation literature, shorter looking to static stimuli during the first year is frequently
described as an index of ‘shorter processing speed’. This is frequently posited as an explanation for
the negative correlations noted between look duration during the first year and long-term outcomes.
One question that follow from this is: does ‘faster processing’ as assessed using screen-based
attention tasks also manifest as different (‘better’, or ‘more efficient’) orienting in naturalistic
contexts? Or is shorter looking to screen-based stimuli associated with better long-term outcomes
because both measures tap some underlying, ‘pure’ aspect of cognition that is entirely independent
of naturalistic orienting? The present study is intended as a small step towards addresing these
questions.
The Present Study
As described above, there exists to our knowledge no previous research that has addressed whether
individual differences in peak look duration are consistent across different assessment techniques.
The present study was conducted in order to address this question. We presented four screen-based
assessments, namely: i) looking behaviour towards ‘interesting’ (complex) static stimuli, ii)
‘boring’ (non-complex) static stimuli, iii) mixed static and dynamic stimuli and iv) to videos under
conditions of distraction (during the recording of EEG data). We also presented two semi-
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naturalistic looking assessments involving the presentation of novel objects in a table-top setting, in
i) a single-object condition (novel objects presented one by one) and ii) a four-object condition
(four novel objects presented concurrently) (following (Kannass & Oakes, 2008). The six measures
were presented in different testing rooms and inter-leaved in order, to a single cohort of typically
developing 11-month-old infants. 11-months was chosen as the age for the present study because
this has been characterised as an age that shows the first emergence of endogenous attentional
control (Colombo & Cheatham, 2006; Courage et al., 2006).
Across all six paradigms, the single dependent variable we assessed was peak look duration. This
was selected because it has previously been argued to be the most stable assessment of looking
behaviour during infancy – in comparison for example to habituation rate (the rate of change of
looks over time), which is less reliable, and shows less robust relationships with long-term
cognitive outcomes (Colombo & Mitchell, 1990). As far as possible, peak look duration was
assessed identically across the six paradigms we administered.
From reviewing the literature we were able to find no discussions suggesting that different factors
might influence peak look duration differentially between screen-based and semi-naturalistic
settings. Therefore we predicted that individual differences in peak look duration would be
consistent across all the paradigms administered.
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Methods
a) Participants
42 typically developing 11-month-old infants participated in the study. Mean age at testing was 337
days (range 312-259, standard deviation 9). Gender ratios were 26 male/16 female. Of note, other
aspects of these data have already been published elsewhere (Wass et al., 2011; Wass & Smith,
under review). The current data contain, however, completely novel analyses which do not overlap
with previous publications.
b) Apparatus and Procedure
The six peak look assessments were administered in three sections. Section A consisted of the
‘static non-complex’ assessment, the ‘static complex’ assessment and the ‘mixed dynamic/static’
assessment. Section B consisted of the ‘structured free play’ assessment. Section C consisted of the
‘videos during EEG’ assessment.
Presentation order. All three sections were administered during a single visit, which generally
lasted c. 90 minutes with breaks. Section A was presented in two halves (‘A1’ and ‘A2’). The order
in which the sections were administered was: Section A1, then Section B, then Section A2, then
Section C. The naturalistic ‘structured free play’ assessment (Section B) was therefore presented
between the other screen-based tasks. This design was chosen in order to preclude the possibility of
order effects being responsible for the results observed.
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Testing rooms. Sections A, B and C were each presented in different rooms. Of note, therefore, the
screen-based tasks included in Section C were presented in a different room to the screen-based
tasks in Section A.
In the detailed descriptions of the methods that follows, materials are described section by section,
together with the data processing techniques that were used.
INSERT FIGURE 1 HERE
INSERT FIGURE 2 HERE
Section A - ‘static non-complex’/‘static complex’/‘mixed dynamic/static’.
Materials: For the three peak look assessments contained in section 1 infants were seated on their
caregiver’s lap while the viewing material was presented on a Tobii 1750 eyetracker subtending 24˚
of visual angle. The three assessments were presented interleaved with each other.
Static non-complex images. Two different still images were presented at different stages of
the testing protocol. The two ‘non-complex’ images were both monochromatic objects
presented against a white background (see Figure 1 for example). Trials were presented
concurrent with child-friendly music, such as songs from Sesame Street. Four different songs
were used that were paired randomly with the different images. All infants heard the same
four songs over the course of all experiments. Trials were presented using a gaze-contingent
infant-controlled habituation protocol procedure: images were presented and remained on-
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screen for as long as the infant looked to the screen. Following cessation of a look, the image
was re-presented until two successive looks had taken place that were less than 50% of the
longest look so far. In order to confirm eyetracker contact, a small (c.0.4˚) re-fixation target
was briefly presented every 15 seconds; subsequent analyses (described in the
Supplementary Materials) suggested that this did not influence the timing of peak look
duration measure. Peak look was calculated independently for each image and then
averaged.
Static complex images. The two ‘complex’ images were polychromatic scenes (see Figure 1
for example). The testing procedures used were identical to those used for the static non-
complex assessment. For practical reasons, individual trials were capped at 120 seconds; 11
out of the 152 individual trials included reached this cap (see Figure S1).
To confirm our classification of images into ‘complex’ and ‘non-complex’ feature
congestion was calculated for each image using Matlab scripts from Rosenholz et al. (2007).
Feature congestion quantifies local variability across different first-order features such as
colour, orientation and luminance; see SM for a more detailed description. For the two ‘non-
complex’ images, average feature congestion across the whole frame was found to be 1.7
and 1.6; for the two ‘complex’ images, average feature congestion was 7.6 and 5.1 (see
Figure S2). This confirmed our classification of the stimuli into ‘complex’ and ‘non-
complex’.
Mixed static/dynamic images. 3 blocks of mixed static and dynamic images were presented
at different stages of the testing protocol. Each block lasted 65 seconds. Each block consisted
of a mixture of: head shots of actors (single and in groups) reciting nursery rhymes, still
images of actors’ faces, and shots of toys and birds accompanied by background music (see
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Figure 1 for example). The individual stimuli within each 65-second block each lasted 4-12
seconds. As with the static images, a small re-fixation target was briefly presented c. every
15 seconds in order to confirm eyetracker contact (see analyses in SM).
Data processing: Infants’ looking behaviour was coded from a camera on top of the monitor. Gaze
was coded in one-second bins, as either looking at the screen or not. Total percentage looking time
and the length of each unbroken look to the target were calculated. Instances in which the
participant looked away from and then back to the screen within one second were treated as
constituting one continuous look rather than two discrete looks. Coder 1 coded 76%, coder 2 48%;
25% of the videos were double coded. Cohen’s kappa was calculated to assess inter-rater reliability
and was found to be 0.71.
Section B - ‘structured free play’
Materials: The two peak look assessments contained in section 2 were conducted in a puppet
theatre with attractive surrounds, and a stage behind which experimenter and the camera were
visible. Infants sat on their caregiver’s lap, close enough to the stage so that they could reach to and
touch the objects on it. Between each trial, the curtains of the puppet theatre were closed and new
objects were placed on the stage; reopening them marked the start of the next trial.
The two assessments were presented consecutively:
Free play – 1-object condition. In the single-object condition, five objects (an plastic figure/a
basting pipette/a glitter lamp/a lion mask/a rabbit mask) were presented in randomised order
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consecutively for 30 seconds each. Figure 1 shows an example of the objects used; Figure S3
shows images of all the objects used. The objects used varied in size from 5cm-20cm.
Free play – 4-object condition. The four-object condition was presented immediately after
the one-object condition. Four objects (a rubber duck, a plastic train, a plastic teddy bear, a
tiger finger puppet) were presented concurrently in a line across the stage, in a randomised
order, for 90 seconds. The objects used varied in size from 5cm-10cm. Data from 10
participants was unusable for the four-object condition due to changes made to the
experimental protocol during testing.
Data processing: Infants’ looking behaviour was recorded from a camera positioned behind the
stage. The coding protocol used was based on that used by Kannass & Oakes (2008). Infants’
looking behaviour was coded for whether the infant was looking at the object or not. Sections where
the object was not on the stage (because the infant had knocked or thrown it off) were excluded. All
coding was conducted in one-second bins. Data were triple coded. Coder 1 coded 70%, coder 2
50% and coder 3 24%; 40% were double coded by coders 1 and 2 and 24% by coders 1 and 3.
Cohen’s kappa was calculated to assess inter-rater agreement. This was found to be 0.72 between
coders 1 and 2 and 0.78 between coders 1 and 3.
Section C - ‘videos during EEG’
Materials: The peak look assessment contained in section 3 was presented with infants sitting on
their caregiver’s lap while viewing materials were presented on a cathode ray TV subtending 30˚ of
visual angle. Simultaneously with the administration of this task, infants were having EEG data
recorded using a 128-channel EGI hydrocel net (Wass, 2011).
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Only one assessment was presented in this section:
Videos during EEG. Three videos were presented sequentially in rotation during EEG
recording. These videos were: i) a series of actresses reciting nursery rhymes to camera; ii)
videos of toys spinning; iii) a short TV clip. Videos lasted 32-44 seconds each. Each video
was presented twice.
Data processing: Infants’ looking behaviour was recorded from a camera positioned below the
monitor. Videos were coded according to whether the infant was looking to or away from the
screen, using an identical coding scheme to that used in sections and 2. Coder 1 coded 76%, coder 2
48%; 24% were double coded. Cohen’s kappa was calculated to assess inter-rater reliability and
was found to be 0.88.
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Results
The results section is in two parts. Firstly, descriptive statistics of the looking time data obtained
from the different paradigms are presented. Secondly, analyses are presented that examine the inter-
relationships in looking time across the different assessments administered. Specifically we wished
to evaluate the hypothesis that individual differences in looking time would be consistent across the
six assessments.
Part 1 - Descriptive statistics of looking time data
Descriptive statistics for the entire data set are shown in Table 1. For each assessment, mean peak
look oberved across all infants has been reported, together with the Standard Error of the Mean
(S.E.M.) and range. Additionally, for comparison, identical data have been reported for mean look
duration (i.e. the average of all looks recorded towards the stimuli). For each assessment, the
number of participants who provided usable data is shown in the final column. With the exception
of the free-play 4 object task, for which (as described above) changes were made to the
experimental protocol during testing, drop-out rates are acceptable (maximum 4/42). These were
due to fussiness and non-compliance during testing.
INSERT TABLE 1 HERE
Figures 3a-f show histograms of all the individual looks collected on the different tasks. Figure 3g
shows plotted lognormal fittings. Lognormal distributions were calculated as these are generally
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reported to be the best fit on infant looking time data (Pempek et al., 2010; Richards & Anderson,
2004). Marked differences in the patterns of look durations observed on different tasks can be seen:
both peak and mean look duration were higher for all of the screen-based tasks than for the
structured free play tasks. Within the screen-based tasks, markedly longer peak looking times were
observed in the static complex and mixed dynamic-static categories than in the other categories.
INSERT FIGURE 3 HERE
The netween-participant distributions of peak look durations were found to be positively skewed, in
common with all looking time assessments (see e.g. Richards & Anderson, 2004); therefore all
subsequent analyses have been calculated based on log-transformed data (following e.g. Frick et al.,
1999).
Part 2 - Analyses to examine the inter-relationships in looking time across the different assessments
administered
We wished to evaluate the hypothesis that individual differences in looking time would be
consistent across the six assessments we administered. In order to examine this, two analyses were
conducted. First, zero-order correlations were calculated. Second, a factor analysis was performed.
First, histograms and scatterplots were calculated to assess whether per-participant peak look values
derived from the log-transformed data were parametrically distributed, and whether any bivariate
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relationships observed were robust. Figure 4 shows these scatterplots. All parameters were found to
be parametrically distributed.
Zero-order correlations. Figure 4 shows the zero-order bivariate correlations that were observed
between the variables entered into the factor analysis. The four screen-based tasks (static complex,
static non-complex, mixed dynamic-static, videos during EEG) all show significant correlations
(r=.33 to.56, all ps <.05) with the exception of the static complex to dynamic during EEG (r=.24,
p<.10). Inspection of the scatterplot (Figure 4) suggests that this relationship is weakened by an
outlier. In comparison the two FP tasks do not correlate with any of the screen-based tasks (negative
in 5 of the 9 comparisons conducted, and r<=.12 in the remaining 4). The scatterplots in Figure 4
suggest that this not attributable to the presence of outliers.
INSERT FIGURE 4 HERE
One explanation that was considered for the low zero-order correlations observed with the free play
data was that these data were inherently more ‘noisy’ than the screen-based looking time data. In
order to evaluate this possibility, data were examined from an overlapping dataset that has been
published previously (Wass et al., 2011; Wass, 2011). In this paper, an identical task to that
presented here was presented twice at fifteen days’ interval to a smaller cohort (N=21) of infants.
Analyses assessed the number of total attentional reorientations and attentional shifts from object to
person. Test-retest reliability between the two testing sessions was r=.53, p<.01 for total attentional
reorientations, and r=.52, p<.05 for attentional shifts from object to person. This suggests that these
measures are relatively stable as indices of individual differences.
Factor analysis. Factor analyses were conducted to examine the factorial structure underlying our
data in more detail. Our analytical approach was based on that used in previously published
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research (Rose et al., 2004, 2005). First, the sample size was examined. The ratio of participants to
variables for the factor analysis was found to be 6.3, which is above the prescribed ratio of 5
suggested by Hair and colleagues (Hair et al., 1998). To maximise the sample size for factor
analysis, missing values were imputed based on the mean of the subscale to which that item
belonged (following Blair & Razza, 2007).
The factor analysis yielded a two-factor solution (Eigenvalues > 1.0) representing 59% of the total
variance (see Table 2). These two factors were submitted to a principal axis rotation (oblimin) and
the scree plot was inspected, supporting a two-factor solution. Thresholds were set at 0.70 for
principal loading and 0.50 for secondary loading (Guadagnoli & Velicer, 1988).
INSERT TABLE 2 HERE
The first factor, which had an Eigenvalue of 2.29 and accounted for 38% of the variance, was
defined by three of the screen-based tasks, with the fourth screen-based task allotted a secondary
loading. The second factor, with an Eigenvalue of 1.23, was loaded onto by the two FP variables (4-
object as primary loading and 1-object as secondary loading), and (negatively) by the static
complex variable (primary loading).
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Discussion
Our analyses were designed to assess whether individual differences in peak look duration are
consistent across different looking time measurement paradigms. To 42 typically developing 11-
month-old infants we administered six assessments of peak look duration, including four screen-
based assessments and two free-play based assessments. We predicted that results obtained would
be consistent across all paradigms. The results were not as predicted. The factor analysis suggested
a two-factor solution. The first factor was defined by the four screen-based tasks (‘static non-
complex’, ‘mixed dynamic-static’ and ‘videos during EEG’, with ‘static complex images’ as a
secondary loading). The second factor was defined by the two free play tasks (one as a secondary
loading) and also (with a negative loading) by the static complex screen task.
The four screen-based tasks were administered across different testing rooms, and interspersed with
the free play tasks, which precludes the possibility of room or order effects being responsible for
our results. Looking time to static screen stimuli and to dynamic screen stimuli showed strong
correlations. Strikingly, we also found that looking behaviour towards a TV screen during recording
of EEG data, which has the additional variance of tightness of fit of the EEG net, reactivity to
testing and so on, mapped onto the same factor as the other three screen-based tasks, that were
administered using a different screen in a different room. In contrast the two FP assessments
mapped onto a separate factor, and showed non-significant (max r=.12) zero-order correlations with
each of the screen-based tasks. The zero-order correlations observed between the FP and screen-
based tasks were negative in 5 out of 8 comparisons. In the factor analysis, the only screen-based
task (static complex) that loaded on to the second factor loaded on negatively (higher looking time
to static complex images associated with lower looking time during structured free play). Further
analyses were conducted to assess the possibility that these findings might be attributable to other
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factors such as increased measurement error during the administration of the free play tasks, with
negative results.
There are a number of limitations to this study. The sample size was relatively small (N=42), and a
number of techniques used by other researchers to complement looking time measures (such as
heart rate measurement and focused attention coding) were not applied. Furthermore, measurements
were only taken with one age group (11-month-olds), whereas the limited data available suggests
that different results may have been observed if the experiment were repeated with younger infants
(Coldren, unpublished data; discussed in Colombo & Mitchell, 1990).
Nevertheless, our results suggest that, in 11-month-old infants, individual differences in peak look
duration are constant across different screen-based tasks but not between screen-based and semi-
naturalistic tasks. We were able to find no discussion in the literature suggesting that different
factors might influence peak look duration between screen-based and semi-naturalistic settings.
What kinds of differences might these be? The following discussion is structured around a number
of factors commonly thought to influence peak look duration.
The first factor commonly associated with peak look duration is processing speed. Sokolov argued
that the initial presentation of a novel stimulus produces a conflict between a “neural model” of the
current environment and the sensory processes occurring in the brain; prolonged exposure to that
stimulus allows the viewer to form an internal representation of it, which is why looking durations
decline over time (Sokolov, 1963). 'Faster processors' are thought to require less time to form an
internal representation; this is frequently linked to the finding that shorter peak look duration to
static stimuli during the first year correlates negatively with long-term cognitive outcomes (e.g.
Jensen, 1987; Rose et al., 2002, 2008).
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Advocates of the importance of processing speed in influencing peak look duration might predict
that individual differences would not be stable between looking time to static screen and dynamic
screen stimuli, since one involves static visual information and the other constantly changing
information. They might also suggest that individual differences might be stable between static
screen and our free play task, since both require forming internal representations of static targets
(on-screen pictures and ‘real-world’ objects). In fact we found the opposite pattern: individual
differences in peak look duration were consistent across the static screen and dynamic screen
stimuli but not with the free play task.
A second factor related to peak look duration is ease of disengaging of visual attention. Frick and
colleagues measured the relationship between experimentally assessed attentional disengagement
latencies and spontaneous looking behaviour to static screen stimuli in typically developing 3- and
4-m-os (Frick et al., 1999). They found that long-looking infants showed greater variability in their
response latencies. This suggests that, at least in younger infants than the 11-month-olds studied
here, attentional disengagement may play a role in mediating spontaneous looking behaviour.
This is one area where differences can be noted between our screen-based and semi-naturalistic
paradigms (see Figure 2). Screen-based paradigms tend to be designed with the screen occupying a
relatively large proportion of the infant’s visual field (typically c.25° of visual angle, as here),
whereas in FP paradigms the target is generally much smaller (c. 5° in our case). In screen-based
tasks the target is generally much more luminant than the surrounds (which are typically dark); in
our free play paradigms, in contrast, this was not the case (see Figure 2). Lastly, in screen-based
tasks there are sharp luminance contrasts between the edge of the screen and the surrounds; again,
these were not present in the FP task (see Figure 2). These differences may be important because
previous research has noted that viewers tend to dwell on areas of high luminance contrasts such as
object boundaries. Although this effect has been reported at all ages from 6-week-old infants
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(Bronson, 1994) through to adults (Henderson et al., 2009) its effect has been reported to decline
with increasing age (Frank et al., 2009; Karatekin, 2007). The high-contrast and prominent
luminance contrasts present in our screen-based but not in our naturalistic tasks may influence
behaviour in the current study, and perhaps more for some infants than others.
A third factor that may influence peak look duration is autonomic arousal. This can be assessed in
both phasic (i.e. event-related) and tonic contexts (De Barbaro et al., 2011; Richards, 2011).
Richards and colleagues explored phasic arousal changes by examining changes in heart rate
variability and peak look during object examination; greater phasic arousal changes were associated
with higher peak look (Richards & Casey, 1991; Richards, 2011). Of note, our screen-based tasks
(particularly the static screen stimuli) contained abrupt changes in luminance coincident with the
onset of each trial: the screen transitioned from dark to bright in an otherwise darkened room; such
changes were completely absent in the naturalistic task. It is possible that these abrupt changes in
luminance are associated with phasic changes in arousal, and that some infants are more susceptible
to these changes than others (Alkon et al., 2006). This factor would influence looking behaviour in
the screen-based but not the naturalistic looking time tasks.
Aston-Jones and colleagues suggested a role for the arousal system in shifting between attention
states; they distinguish between a ‘scanning’ mode, in which look durations are short and the focus
of visual attentiveness is wide, and a ‘focused’ mode, in which look durations are longer and the
spatial distribution of attention is narrower (Aston-Jones & Cohen, 2005; Aston-Jones et al., 1999,
2007; cf. Pannasch et al., 2008). In our semi-naturalistic tasks, other targets (objects and people) are
present within the peripheral visual field of the infant, whereas attempts were made to ‘black out’
all peripheral objects for the screen-based tasks (as is typical in other labs) (see Figure 2). The
shifting between attention states that Aston-Jones and colleagues describe may therefore be a factor
in our semi-naturalistic tasks but not in our screen-based tasks.
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
22
A fourth factor that may relate to peak look duration is executive control. Aspects of executive
control have been reliably associated with sustained attention in older children (e.g. Reck & Hund,
2011). (Note however that sustained attention in these studies with children is assessed not using
looking time measures but with tasks such as the Continuous Performance Task, whose relationship
with peak look duration has, to our knowledge, not been studied.) Colombo and Cheatham point out
that positive correlations are observed between long-term cognitive outcomes and peak look to
static stimuli after the first year of life, whereas negative correlations are observed between the
same two variables during the first year. They suggest that this may be attributable to the emergence
of effortful control as a factor mediating behaviour at about the 12-month boundary (Colombo &
Cheatham, 2006; see also Courage et al., 2006). Note, however, that Kochanska et al. (2006) found
that focused attention (not peak look duration) during the second half of the first year correlated
positively with effortful control at 22 months.
One difference between our screen-based and our semi-naturalistic tasks may be relevant here. This
is that, for the semi-naturalistic tasks, a number of other informative gaze targets (such as the
experimenter) are within the field of view of the child - whereas the screen-based tasks were
conducted in a darkened room. There are a variety of reasons why looks away from the object may
have an adaptive value in our free play paradigm but not in our screen-based paradigm (e.g. Rueda
et al., 2005; Sheese et al., 2008). It may be therefore that executive control relates more strongly to
peak look duration in the free play than in the screen-based tasks, although future work is required
to investigate this in more detail (cf. Rothbart et al., 2003; Sheese et al., 2008).
Conclusions
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
23
To a cohort of typically developing 11-month-old infants we presented several assessments of peak
look duration, including some that assessed looking behaviour on screen-based tasks and others that
assessed behaviour on semi-naturalistic tasks. We found that the four screen-based tasks (looking to
static non-complex, to static complex, to mixed dynamic/static and to dynamic stimuli during EEG
recording) all mapped onto a single factor, whereas the two free play assessments mapped onto a
separate factor. In our discussion we noted a number of ways in which factors such as susceptibility
to high luminance contrasts and abrupt stimulus onset-offset changes may be key factors mediating
individual differences on screen-based tasks, but relatively unimportant in more naturalistic
contexts. Future research should exploit recent technological advances such as head-mounted
eyetrackers (e.g. Aslin, 2009) to increase our understanding of how individual differences in
naturalistic attention relate to individual differences in infant attention as assessed using screen-
based paradigms.
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
24
References
Alkon, A., Lippert, S., Vujan, N. R., M.E., Boyve, W. T., & Eskenazi, B. (2006). The Ontogeny of
Autonomic Measures in 6- and 12-Month- Old Infants. Developmental Psychobiology, 48(3),
197-208.
Anderson, D. R., H. P. Choi and E. P. Lorch (1987). Attentional inertia reduces distractibility
during young children's' TV viewing. Child Development 58(3): 798-806.
Aslin, R. N. (2009). How Infants View Natural Scenes Gathered From a Head-Mounted Camera.
Optometry and Vision Science 86(6): 561-565.
Aston-Jones, G., M. Iba, E. Clayton, J. Rajkowski and J. Cohen (2007). The locus coeruleus and
regulation of behavioral flexibility and attention: clinical implications. Brain Norepinephrine:
Neurobiology and Therapeutics. M. A. S. a. A. F. Gregory A. Ordway. Cambridge, UK.,
Cambridge University Press.
Aston-Jones, G. and J. D. Cohen (2005). An integrative theory of locus coeruleus-norepinephrine
function: Adaptive gain and optimal performance. Annual Review of Neuroscience. 28: 403-
450.
Aston-Jones, G., J. Rajkowski and J. Cohen (1999). Role of locus coeruleus in attention and
behavioral flexibility. Biological Psychiatry 46(9): 1309-1320.
Blair, C. and R. P. Razza (2007). Relating effortful control, executive function, and false belief
understanding to emerging math and literacy ability in kindergarten. Child Development
78(2): 647-663.
Bronson, G. W. (1994). Infants' transitions toward adult-like scanning. Child Development 65(5):
1243-1261.
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
25
Choudhury, N., & Gorman, K. S. (2000). The relationship between sustained attention and
cognitive performance in 17-24-month old toddlers. Infant and Child Development, 9(3), 127-
146.
Cohen, L. B. (1969). Observing responses, visual preferences, and habituation to visual stimuli in
infants. Journal of Experimental Child Psychology, 7, 419–433.
Cohen, L. B. (1972). Attention-getting and attention-holding processes of infant visual preferences.
Child Development, 43, 869–879.
Coldren, J.T. (1987). The relationship of infant attention across laboratory and social interaction
tasks. Society fo Research in Child Development, Baltimore. Described in Colombo &
Mitchell, 1990.
Colombo, J. (1993). Infant cognition: Predicting later intellectual functioning. Newbury Park, CA:
Sage Publications.
Colombo, J. and C. L. Cheatham (2006). The emergence and basis of endogenous attention in
infancy and early childhood. Advances in Child Development and Behavior, Vol 34 34: 283-
322.
Colombo, J., & Mitchell, D. W. (1990). Individual and developmental differences in infant visual
attention: Fixation time and information processing. In J. Colombo & J. W. Fagen (Eds.),
Individual differences in infancy: Reliability, stability, and prediction (pp. 193–227).
Hillsdale, NJ: Lawrence Erlbaum.
Colombo, J. and D. W. Mitchell (2009). Infant visual habituation. Neurobiology of Learning and
Memory 92(2): 225-234.
Courage, M. L., G. D. Reynolds and J. E. Richards (2006). Infants' attention to patterned stimuli:
Developmental change from 3 to 12 months of age. Child Development 77(3): 680-695.
de Barbaro, K., A. Chiba and G. O. Deak (2011). Micro-analysis of infant looking in a naturalistic
social setting: insights from biologically based models of attention. Developmental Science
14(5): 1150-1160.
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
26
Dixon, W. E., Jr. and P. H. Smith (2008). Attentional focus moderates habituation - Language
relationships: Slow habituation may be a good thing. Infant and Child Development 17(2):
95-108.
Frank, M. C., Vul, E., & Johnson, S. P. (2009). Development of infants' attention to faces during the
first year. Cognition, 110(2), 160-170
Frick, J. E., J. Colombo and T. F. Saxon (1999). Individual and developmental differences in
disengagement of fixation in early infancy. Child Development 70(3): 537-548.
Guadagnoli E and Velicer W (1988). Relation of sample size to the stability of component patterns.
Psychological Bulletin 103 265-275.
Hair JF, Tatham RL, Anderson RE and Black W (1998). Multivariate data analysis. (Fifth Ed.)
Prentice-Hall:London.
Heckman, J. J. (2006). Skill formation and the economics of investing in disadvantaged children.
Science 312(5782): 1900-1902.
Henderson, J. M., & Smith, T. J. (2009). How are eye fixation durations controlled during scene
viewing? Further evidence from a scene onset delay paradigm. Visual Cognition, 17(6-7),
1055-1082.
Jensen, A. R. (1987). Individual differences in the Hick paradigm. In P. A. Vernon (Ed.), Speed of
information-processing and intelligence. Norwoord, NJ: Ablex.
Kagan, J. & Lewis, M.(1965). Studies of attention in the human infant. Merill-Palmer Quarterly, 11,
95-127.
Kannass, K. N. and L. M. Oakes (2008). The development of attention and its relations to language
in infancy and toddlerhood. Journal of Cognition and Development 9(2): 222-246.
Karatekin, C. (2007). Eye tracking studies of normative and atypical development. Developmental
Review, 27(3), 283-348.
Karmiloff-Smith, A. (1998). Development itself is the key to understanding developmental
disorders. Trends in Cognitive Sciences 2(10): 389-398.
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
27
Kochanska, G. and N. Aksan (2006). Children's conscience and self-regulation. Journal of
Personality 74(6): 1587-1617.
Lorch, E. P., Eastham, D., Milich, R., Lemberger, C. C., Sanchez, R. P., & Welsh, R. (2004).
Difficulties in comprehending causal relations among children with ADHD: The role of
cognitive engagement. Journal of Abnormal Psychology, 113(1), 56-63.
McCall, R. B., & Carriger, M. S. (1993). A meta-analysis of infant habituation and recognition
memory performance as predictors of later IQ. Child Development, 64, 57–79.
Pêcheux, M.-G., & Lécuyer, R. (1983). Habituation rate and free exploration tempo in 4-month-old
infants. International Journal of Behavioral Development, 6, 37–50.
Pempek, T. A., H. L. Kirkorian, J. E. Richards, D. R. Anderson, A. F. Lund and M. Stevens (2010).
Video Comprehensibility and Attention in Very Young Children. Developmental Psychology
46(5): 1283-1293.
Reck, S. G. and A. M. Hund (2011). Sustained attention and age predict inhibitory control during
early childhood. Journal of Experimental Child Psychology 108(3): 504-512.
Richards, J. E. & Anderson, D.R. (2004). Attentional inertia in children's extended looking at
television. Advances in Child Development and Behavior, Vol 32. 32: 163-212.
Richards, J.E. (2009). Attention in the brain and early infancy. In S.P. Johnson (Ed.),
Neoconstructism: The new science of cognitive development.
Richards, J. E. (2010). The development of attention to simple and complex visual stimuli in
infants: Behavioral and psychophysiological measures. Developmental Review 30(2): 203-
219.
Richards, J. (2011). Infant Attention, Arousal and the Brain. In L. M. Oakes, C. H. Chason, M.
Casasola & D. H. Rakison (Eds.), Infant Perception and Cognition Oxford, UK: Oxford
University Press.
Richards, J. E., & Cronise, K. (2000). Extended visual fixation in the early preschool years: Look
duration, heart rate changes, and attentional inertia. Child Development, 71, 602 – 620.
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
28
Rose, S. A., J. F. Feldman and J. J. Jankowski (2002). Processing speed in the 1st year of life: A
longitudinal study of preterm and full-term infants. Developmental Psychology 38(6): 895-
902.
Rose, S. A., Feldman, J. F., & Jankowski, J. J. (2003a). The building blocks of cognition. Journal of
Pediatrics, 143, S54!S61.
Rose, S. A., Feldman, J. F., & Jankowski, J. J. (2003b). Infant visual recognition memory:
Independent contributions of speed and attention. Developmental Psychology, 39(3),
563!571.
Rose, S. A., Feldman, J. F., & Jankowski, J. J. (2004). Dimensions of cognition in infancy.
Intelligence, 32(3), 245-262.
Rose, S. A., Feldman, J. F., & Jankowski, J. J. (2005). The structure of infant cognition at 1 year.
Intelligence, 33(3), 231-250.
Rose, S. A., J. F. Feldman, J. J. Jankowski and R. Van Rossem (2008). A cognitive cascade in
infancy: Pathways from prematurity to later mental development. Intelligence 36(4): 367-378.
Rosenholtz, R., Li, Y., & Nakano, L. (2007). Measuring visual clutter. Journal of Vision, 7(2).
Rothbart, M. K., L. K. Ellis, M. R. Rueda and M. I. Posner (2003). Developing mechanisms of
temperamental effortful control. Journal of Personality 71(6): 1113-1143.
Rueda, M. R., M. I. Posner and M. K. Rothbart (2005). The development of executive attention:
Contributions to the emergence of self-regulation. Developmental Neuropsychology 28(2):
573-594.
Sarid, M. and Z. Breznitz (1997). Developmental aspects of sustained attention among 2- to 6-year-
old children. International Journal of Behavioral Development 21(2): 303-312.
Shaddy, D. J. and J. Colombo (2004). Developmental changes in infant attention to dynamic and
static stimuli. Infancy 5(3): 355-365.
Sheese, B. E., M. K. Rothbart, M. I. Posner, L. K. White and S. H. Fraundorf (2008). Executive
attention and self-regulation in infancy. Infant Behavior & Development 31(3): 501-510.
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
29
Sokolov, E. N. (1963). Perception and the conditioned reflex. New York, MacMillan.
Tami-LeMonda, C. S. and M. H. Bornstein (1989). Habituation and maternal encouragement of
attention in infancy as predictors of toddler language, play and representational competence.
Child Development 60(3): 738-751.
Wass, S.V. (2011). Gaze-contingent concentration training for infants. Birkbeck, University of
London. Unpublished PhD thesis.
Wass, S.V., K. Porayska-Pomsta and M. H. Johnson (2011). Training Attentional Control in
Infancy. Current Biology 21(18): 1543-1547.
Wass., S. V., Scerif, G., & Johnson, M. H. (2012). Training attentional control and working
memory: is younger, better? Developmental Review, 32(4), 360-387.
Wass, S.V. & Smith, T.J. (under review). Individual differences in infant oculomotor behaviour
during the viewing of complex naturalistic scenes. Manuscript under review.
Welsh, J. A., R. L. Nix, C. Blair, K. L. Bierman and K. E. Nelson (2010). The Development of
Cognitive Skills and Gains in Academic School Readiness for Children From Low-Income
Families. Journal of Educational Psychology 102(1): 43-53.
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Figures
Figure 1: Summary of viewing materials presented.
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
31
Figure 2. Comparison of paradigms presented from infant’s perspective. Top row shows the screen-
based looking tasks; bottom row shows the naturalistic looking task. In each row, the left column
shows a photo of the stimulus array from the infant’s perspective. The active target for the coding
of look durations is indicated using a red rectangle. In the central column the luminance of the
images is shown. On the right, feature congestion is shown. Feature congestion quantifies local
variability across different first-order features such as color, orientation and luminance (Rosenholz
et al., 2007) and has been shown to influence gaze allocation in contexts in which no motion is
present in the field of view (Henderson et al., 2009).
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
32
Figure 3 - Histograms and log normal distribution fittings. a)-f) show histograms of all peak looks
observed across the different assessments we administered. g) shows lognormal distributions of
peak looks observed.
0"
0.1"
0.2"
0.3"
0.4"
0.5"
0.6"
0"to"5" 5"to"10" 10"to"15" 15"to"20" 20"to"25" >25"
Sta$c&non)complex&
0"
0.1"
0.2"
0.3"
0.4"
0.5"
0.6"
0"to"5" 5"to"10" 10"to"15" 15"to"20" 20"to"25" >25"
Sta$c&complex&
Look duration (secs)
0"
0.05"
0.1"
0.15"
0.2"
0.25"
0.3"
0.35"
0.4"
0"to"5" 5"to"10" 10"to"15" 15"to"20" 20"to"25" >25"
Mixed&dynamic,sta/c&
0"
0.05"
0.1"
0.15"
0.2"
0.25"
0.3"
0.35"
0.4"
0.45"
0"to"5" 5"to"10" 10"to"15" 15"to"20" 20"to"25" >25"
Videos'during'EEG'
0"
0.1"
0.2"
0.3"
0.4"
0.5"
0.6"
0"to"5" 5"to"10" 10"to"15" 15"to"20" 20"to"25" >25"
Free$play$1$object$
0"
0.1"
0.2"
0.3"
0.4"
0.5"
0.6"
0.7"
0.8"
0.9"
0"to"5" 5"to"10" 10"to"15" 15"to"20" 20"to"25" >25"
Free$play$4$objects$
Look duration (secs)Look duration (secs)
Prop
. tot
. loo
ksPr
op. t
ot. l
ooks
Prop
. tot
. loo
ks
a) b)
c) d)
e) f)
g)
Saturday, 7 September 13
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
33
Figure 4 - Correlation matrix showing the relationship between the dependent variables entered
into the factor analyses. Histograms showing the distribution of each variable are shown
diagonally on the 1:1 line. Below this line, scatterplots show the relationships between variables.
For those variables showing a bivariate correlation at p(2-tailed)>.05, a linear regression line has
been drawn in black. Above the 1:1 line, the Pearson’s product moment correlation shows the
relationship between the two variables. The stars show the significance levels of the bivariate
correlation: ** - p(2-tailed)<.01, * - p<.05, (*) - p<.10.
Free
pla
y 4
obje
ct
Free play 1 object
Videos during EEG
Mixed dynamic-static
Static non-complex
Static complex
Free
pla
y 1
obje
ctV
ideo
s du
ring
EEG
Mix
ed d
ynam
ic-s
tatic
Stat
ic
non-
com
plex
0.43** 0.33* 0.24(*) -0.28(*) -0.20
0.120.41* 0.49** -0.04
0.050.56** -0.09
0.11-0.14
0.03
Stat
ic
com
plex
Free play 4 object
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
34
Tables
Table 1 - Descriptive statistics.
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
35
Table 2: Two-factor solution. Bold indicates principal loading. Italicized bold indicates secondary
loading
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
36
Supplementary materials
Supplementary methods
Section 1 – static non-complex, static complex and mixed static/dynamic assessments
In order to confirm eyetracker contact, a small re-fixation target subtending c. 0.4˚ of visual angle
was briefly presented every 15 seconds. In order to assess the possibility that the presence of this re-
fixation stimulus might have influenced our results we plotted a cumulative frequency distribution
of the peak look duration of all of the individual trials (four per infant - two complex, two non-
complex) that were recorded. We reasoned that, if the presence of this target had influenced the
peak look duration measure, then peaks would be observable in this frequency distribution
immediately after the target was presented (i.e. at 15, 30, 45 seconds and so on). Indeed, small
peaks are detectable at these times, consistent with this hypothesis. However, it can also be seen
that these peaks are small relative to the range of response times observed. We concluded therefore
that there was no evidence to suggest that the presence of this re-fixation target invalidates our use
of this measure as an assessment of peak look duration to static images.
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
37
Figure S1: Cumulative frequency distribution of all trials presented during the assessment of
looking time to static images.
Feature congestion calculations
Feature congestion was calculated using Matlab scripts that are described in detail by Rosenholz
and colleagues (Rosenholtz et al. 2007). These were chosen in preference to the more widely used
salience models (e.g. Itti & Koch, 2001) since these models make a lot of assumptions about how
attention is controlled, such as the inclusion of inhibition of return and winner takes all in the
salience computation. Briefly, the input image was converted into the CIELAB color space and then
processed at three scales by creating a Gaussian pyramid by alternately smoothing and subsampling
the image (Burt & Adelson, 1983). Features were then identified based on luminance contrasts (by
filtering the luminance band by a center-surround filter formed from the difference of two
Gaussians and squaring the outputs); color, performed at each scale by pooling with a Gaussian
filter; and orientation (using a two-vector, (k cos(2θ), k sin(2θ)), at each image location and scale,
where θ is the local orientation and k is related to the extent to which there is a single strong
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
38
orientation at the given scale and location. Then the local (co)variance for each feature is
calculated; these are then combined, scaling the clutter value in each feature dimension by the range
of possible clutter values for that feature (Rosenholtz et al., 2007).
Figure S2: Feature congestion calculations used to quantify simulus complexity. The images on the
left show the original image. From top to bottom, the top two images were selected as ‘non-
complex’ and the bottom two images were selected as ‘complex’. The images on the right are those
outputted by scripts from Rosenholz and colleages. Areas drawn white are areas with relatively
high feature congestion, defined via differentials of a combination of first-order stimulus features
such as luminance, colour and edge density (see text).
WASS - PEAK LOOK TO SCREEN VS NATURALISTIC
39
Section 2 - Structured free play
Figure S3: Stimuli used in the free play tasks. Stimulus c) is similar but not identical to that used. a)
lion mask, c.25cm x 20cm. b) rabbit mask, c.30cm x 20cm. c) plastic figure, c. 10cm x 8cm. d)
basting pipette, c. 25cm x 5cm. e) glitter lamp, c. 15cm x 5cm.