The pupils control how much light reaches the eye. They become smaller in bright light and larger in darkness to let more light in. Other factors can also affect pupil size. For example, the pupils slightly constrict when a person focuses on brighter objects and they enlarge when focusing on a darker object.

Tracking changes in pupil size can tell scientists what someone is focusing on. This can be helpful because different people distribute their attention differently. Some tend to focus on the big picture, others tune into individual details. People with autism spectrum disorders (ASD) tend to focus on the details.

Now, Turi et al. showed that measuring pupil size changes during a simple visual task could identify typical people who have milder versions of the characteristics seen in people with ASD. In the experiments, 50 young adults without a diagnosis of an ASD filled out a questionnaire designed to assess how many ASD-like traits they have. Next, the participants watched an illusion of a cylinder with a light and dark side rotating. As they watched, the pupil size of people with more ASD-linked behaviors fluctuated more than the pupil size of those with few such characteristics.

The pupils of people with ASD-type traits became larger when they perceived the dark side of the cylinder to be forward, and smaller when the light side appeared. This suggests they are focusing on the front of the cylinder. Future studies are needed to see if similar pupil fluctuations occur in people diagnosed with ASD. Turi et al. predict that pupil changes will be even more dramatic in people with ASD. If this is the case, these pupil measurements could be used to help diagnose ASD or determine the severity of symptoms.

A visual scene can be perceived at various hierarchical levels of structure, from the most local elements to the global organization. A dense patch of trees is perceived as a forest at a global level, while a progressively more local analysis reveals the individual trees, then their leaves, bark, etc. The basis of global perception is the structuring into larger units the ‘bits and pieces’ of visual information in order to perceive objects and their relations (de-Wit and Wagemans, 2015). The ability to form a whole from parts, ignoring details to form meaningful classes, is a major developmental step in childhood and a component of how we define intelligence – one of the tests for IQ in the standard Wechsler Scale. Different authors have developed relatively simple perceptual tasks that have become established indexes of the preference for local or global: the block design task (Kohs, 1920; Wechsler, 1955), the Rod-and-Frame Test (Witkin and Asch, 1948); the Embedded Figures Test (EFT: 5); and Navon’s hierarchical letters (Navon, 1977). The preference for local or global is also part of a general way of feeling and behaving, as described by personality traits. Perhaps, the clearest example is the tendency for local or detail-oriented perception in Autistic Spectrum Disorders (Happé and Frith, 2006; Mottron et al., 2006; Plaisted et al., 1999), compared with the more global or holistic perception in typical adults (Navon, 1977): autistic observers show slower responses to global structure (Van der Hallen et al., 2015) and enhanced processing of local features (Mottron et al., 2006; Muth et al., 2014). There is increasing interest in determining whether differences in the preference for local or global styles are also associated with autistic traits in the typical population, supporting the dimensional view of autistic disorders, where people with and without ASD diagnosis lay along a continuum and differ only quantitatively, not qualitatively (Baron-Cohen et al., 2001; Constantino and Todd, 2003; Ruzich et al., 2015; Skuse et al., 2009; Wheelwright et al., 2010). However, not all attempts have been successful at showing a local-global difference in neurotypical individuals (Cribb et al., 2016), which may a result from the limited reliability and validity of the tests, which correlate little with each other and probably measure very different combinations of abilities and constructs (Milne and Szczerbinski, 2009).

Here, we report on a pupillometry-based paradigm that yields an objective index of local-global processing. We tested 50 randomly selected neuro-typical adults with variable degrees of autistic traits, as measured by the Autism-Spectrum Quotient (AQ), a tool developed to characterize the normal spectrum of subclinical autistic behaviours in the general population (Baron-Cohen et al., 2001). Our psychophysical task – simply reporting the apparent direction of rotation of a bistable stimulus – can be performed equally well with both local and global perceptual styles: either by attending to only the front surface, or by attending to the whole global rotation. By tagging the front and back surfaces with different luminance levels, the two styles should have different modulatory effects on pupil-size, which is has shown to be modulated by cognitive and top-down factors, such as perceived rather than physical luminance, and shifts in attention from dark to bright surfaces (Binda and Murray, 2015; Binda et al., 2014). Specifically, attending to a bright or dark surface is sufficient to evoke pupil constrictions or dilations respectively, tracking the focus of ‘feature-based’ attention (Binda et al., 2014). We therefore hypothesized that pupil-modulations would depend on whether participants attend locally to the front surface, or globally to both – implying that pupil-modulations in this task effectively index differences in the local-global preference across our participants.

We tracked pupil-size while participants viewed an ambiguous dynamic stimulus comprising 150 leftward-moving white dots and 150 rightward-moving black dots, giving the immediate impression of a cylinder rotating in depth at 10 revolutions per minute (see Figure 1A and Video 1). As the depth of the dots was ambiguous, either dot-group (tagged by luminance and direction) could be seen in front, resulting in either clockwise or counter-clockwise rotation. Subjects continuously reported the perceived direction of rotation by keypress or joystick for three sessions, each lasting 10 min. The 50 participants were recruited in two groups of 25, and the two sessions were intended as self-replications of the experiment (see below, Figure 3). On average, perceived direction alternated every 5.5 s (0.21 ± 0.01 perceptual switches per second); the between-subject variability of switch rate did not correlate with autistic traits (Pearson’s r = 0.02 [-0.26 0.29], p=0.901, Bayes Factor [BF]=0.1).

Figure 1

Download asset Open asset

Figure 2

Download asset Open asset

Figure 1B shows the timecourse of pupil-size, synchronized to the perceptual switch and averaged over all subjects, separately for perceptual phases with black or white foreground (blue and red, respectively). Two distinct kinds of pupil modulations are apparent. First, as reported previously for binocular rivalry (Einhäuser et al., 2008), the pupil dilated transiently at or just after each perceptual switch; this effect is seen on both blue and red traces, meaning that the dilation occurs irrespective of whether perception switches toward a black or a white foreground. However, there is also a second kind of pupil modulation that depends on the direction of the shift: the pupil was more dilated when the foreground was black and more constricted when it was white. This is similar to the pupillary modulation that occurs during binocular rivalry between stimuli of different luminances, leading to pupillary constriction when the brighter stimulus dominates (Naber et al., 2011).

Video 1

Download asset

This luminance-dependent modulation is a purely perceptual effect, as the stimulus luminance is constant throughout the experiment. The constriction when the foreground was white probably results from participants attending more to the foreground than the background, as it is known that attending to white constricts the pupil (Binda et al., 2013). On the other hand, if participants were to distribute attention evenly between the back and the front surfaces (or rapidly switch attention between them), no pupil difference would be expected. As these two strategies correspond well to the local and global perceptual styles associated with high and low autistic traits (Happé and Frith, 2006; Grinter et al., 2009), pupil modulation should be more prominent in participants with higher AQ scores. Figure 1D shows that, in our sample of typical young adults, there was considerable variation in luminance-dependent pupil change pupil modulation. The signed amplitude of the modulation was significantly positive in 20 out of the 50 participants. Crucially, the amplitude of modulation, which we suggest is an index of perceptual style, was highly correlated with AQ scores (r = 0.70,[0.52:0.82] p<10⁻⁵, BF >10⁵). Importantly, the correlation was specific to this particular pupillometric index: AQ scores did not correlate with the general dilation that followed all perceptual switches (r = 0.06,[-0.22:0.33], p=0.678, BF = 0.1: Figure 1C).

To test the idea that the variability of pupil responses emerges from different (local-global) perceptual styles, we performed two further experiments. Firstly, we attempted to induce changes in viewing strategy by changing the instructions given to the participants, and looked for concomitant changes of pupil modulation. We retested twice more a small subset of 10 participants, under two different instruction sets: ‘try to attend to both surfaces and see the cylinder rotate as a single unit’; or ‘focus attention on the front surface alone’. Most subjects reported that they found the instructions difficult to follow, and that they tended to lapse into their more natural viewing style. Nevertheless, the instructions significantly affected the luminance-dependent pupil modulation (0.005 ± 0.007 mm when instruction favoured the global strategy; 0.029 ± 0.008 mm when they favoured the local strategy), which was greater when the local strategy was encouraged (paired t-test: t:(9) = −2.72, p=0.024). This supports the suggestion that pupil modulation is driven by perceptual style, and further also shows that the pupil can be a more sensitive (as well as more objective) probe of perceptual style than are subjective reports.

In a second experiment, we modified the procedure to encourage all participants to view the stimulus in the same local manner, attending to only one surface. Instead of the continuous presentation used before, we presented brief (6 s) bursts of the stimulus and instructed subjects to which surface they should attend (Figure 2A). To monitor compliance, participants reported the number of speed changes in the dots of the cued surface (ignoring changes in the other surface), which they did with an average of 62 ± 2% correct responses (uncorrelated with AQ, r = 0.24 [-0.04 0.49], p=0.090, BF = 0.5). Under these conditions, there was a strong modulation of pupil size in the averaged data, depending on whether the white or the dark surface was attended (Figure 2B), consistent with the reported effects of feature-based attention on pupil size (Binda et al., 2014). However, in this experiment, where viewing style was constrained by the task, all subjects tended to show front-color dependent pupil modulation, and it was not correlated with AQ (r = 0.20 [-0.08:0.46], p=0.156, BF = 0.3, Figure 2C).

Having established that the correlation between pupil difference and AQ scores is specific to the free-viewing of our bistable stimulus, we went on to check the reliability of the effect. We measured the correlation separately in the two groups of 25 participants who participated in the two sessions of the experiment (and are combined in the plots of Figure 1B–D). The correlation between AQ and pupil difference was strong and significant in both groups (Figure 3A, first subset: r = 0.75, p<10⁻⁴, BF >10³; Figure 3B, second subset: r = 0.64, p<0.001, BF = 54.3). We further replicated the effect in a slightly different condition, tested in 26 participants (swapping the motion directions of white and black dots, Figure 3C, r = 0.66 p<0.001, BF = 85.3); the three correlation coefficients are statistically indistinguishable (Fisher's Z test; all p-values>0.23), implying that the results are robust.

Figure 3

Download asset Open asset

As a further check on the reliability of the results, we also analyzed the correlations between pupil difference in the 50-participant group (Figure 1D), and each of the five subscales of the AQ questionnaire (normally distributed Figure 3D). All but one correlation coefficients were positive and significant (Figure 3E). The strongest correlations were with the Communication and Social Skills subscales, and weakest with ‘Attention to Detail’ scores (discussed later). The bottom row of Figure 3E shows the correlation of each subscale with the rest of the questionnaire (the sum of the other four subscales), and also the pupil-difference index with the overall AQ score. Note that the correlation of the pupil-difference index with the total AQ score was higher than those between any subscale and the other four subscales.

The results reported in Figures 1–3 show that pupil modulation reliably indexes attention to the front or both surfaces, depending on the participant’s AQ score. In principle, perceptual style should also be measurable by psychophysical techniques, with enhanced performance on the attended surface(s) (Carrasco, 2011). To compare our novel pupillometry approach with more standard psychophysical methods, we measured correlations between AQ and ability to detect subtle changes on the front or rear surface. As in the main experiment, participants continuously tracked the rotation of the cylinder over 10 min long sessions; but here they also reported brief changes in speed that occurred randomly on the surface formed either by white or black dots, at the front or the rear depending to the perceived rotation of the cylinder. As in the main experiment, pupils dilate more when the foreground is black compared with when it is white (Figure 4A). Speed-change detection performance was generally better when targets occurred on the front than on the rear surface (Figure 4C paired t-test on d-prime values: t(24)=4.43, p<0.001). However, AQ correlated neither with the difference in performance (Figure 4D, r = −0.03 [-0.42 0.37], p=0.869, BF = 0.2) nor with the pupil difference (Figure 4B r = −0.27 [-0.60 0.14], p=0.187, BF = 0.4). This shows that the double-task setting (necessary to obtain a psychophysical index of attention distribution) interfered with participants’ natural viewing style, biasing all towards a ‘local’ processing style, focusing on one surface at a time to detect the speed changes. In most cases, the surface was the front one. This clearly undermined the possibility of estimating the inter-individual differences that spontaneously emerge in the free-viewing conditions of the main experiment.

Figure 4

Download asset Open asset

We measured pupil size while subjects viewed a structure-from-motion stimulus perceived as a cylinder rotating in 3D with bistable direction. In some but not all participants, pupil-size modulated in synchrony with their bistable perception, more constricted when the white surface was perceived in front. The magnitude of the modulation was tightly correlated with the Autistic Quotient scores, consistent with the view that stronger autistic traits accompany a preference for focusing on local detail, as opposed to globally attending the whole stimulus configuration. Some participants spontaneously reported their perceptual experience: of these some reported that they attending locally to only the front surface, while others reported that they had to attend globally to the whole configuration, both front and rear, in order to perceive the rotation. These spontaneous reports (respectively from participants with high and low AQ) provide qualitative support for our interpretation.

Although preference for local versus global has been extensively investigated and has come to be a defining feature of Autistic Spectrum Disorders (Van der Hallen et al., 2015; Muth et al., 2014), its underlying mechanisms are essentially unknown. It could be a perceptual style, depending on differences in the bottom-up transmission of sensory information (Mottron et al., 2006; Mottron et al., 2003; Plaisted et al., 2003), but it could also have a cognitive or attentional basis, depending on a preference to maintain a narrow focus of attention (Happé and Frith, 2006; Plaisted et al., 1999) or a relative inability to divide or switch attention between multiple targets (Behrmann et al., 2006; Frith and Happé, 1994; Happé and Frith, 1996; Happé and Booth, 2008). Our results are equally supportive of all three possibilities, as are the classic tests for global-local preference, such as Navon letters or embedded figures. While our results do not distinguish between the different possible explanations of perceptual styles, they clearly show that autistic traits are associated with a more local perceptual style.

There is an ongoing dispute about whether autistic traits are distributed continuously amongst the population with ASD diagnosis at an extreme, or whether the distinction is more categorical (Baron-Cohen et al., 2001; Constantino and Todd, 2003; Ruzich et al., 2015; Skuse et al., 2009; Wheelwright et al., 2010). The hypothesis of a ‘Broader Autistic Phenotype’ strongly predicts that local-global preference should be continuously distributed and correlated with autistic traits in the neurotypical population (Cribb et al., 2016). However, many of the classic tests show very low or insignificant correlations with autistic traits (Cribb et al., 2016). Our experiment describes a pupillometry index with the strongest correlation with autistic traits reported so far in a large group of participants, explaining about 50% of the variance in AQ scores.

This correlation may appear surprisingly high, given that it is measured between two different tasks. However, each of these separate tasks has high reliability. The test-retest reliability of the autistic questionnaire has been reported to be around 0.75 (Baron-Cohen et al., 2001; Hoekstra et al., 2008; Kurita et al., 2005). In the 22 participants who performed both the main experiment and the swapped-direction, the correlation of pupil modulation in the two tasks was 0.68 ([0.36:0.86], p<0.001, BF = 62.7). Considering the 95% confidence interval range (r = 0.70, [0.52:0.82]), the obtained correlations are not unrealistic, and certainly highly significant.

Although the autistic questionnaire is well validated (Baron-Cohen et al., 2001), not all items have the same validity. We also observed variability in the strength of correlation of the pupil index and the AQ subscales, which was strongest for the Communication and Social Skills subscales and, somewhat surprisingly, weakest for the ‘Attention to Detail’ scores. However, the ‘Attention to Detail’ subscale seems to have the lowest construct validity of the five subscales: it had the lowest performance in classifying individuals with and without ASD diagnosis in a sample of 350 adults, with 130 ASD diagnoses (Lundqvist and Lindner, 2017), it had the lowest correlation with the general AQ score in a sample of 2343 typical individuals (Palmer et al., 2015), and, in our sample, scores on the ‘Attention to Detail’ subscale did not correlate significantly with the overall AQ score. It is also worth noting that among the items contributing to the ‘Attention to Detail’ subscale, only four actually relate to perceptual phenomena (e.g. ‘I usually concentrate more on the whole picture, rather than the small details.”), the others being focused on memory and cognition (e.g. ‘I am fascinated by numbers.”). All this highlights a general difficulty in measuring local-global preference, either with self-report or with laboratory tests. However, by combining the objectivity of pupillometry and the flexibility of a perceptual task involving bistable perception, we can achieve a very reliable and precise measure of inter-individual differences.

There is growing interest in relating differences among individuals in their performance in perceptual tasks, with states or traits of their personality (Tadin, 2015; Eldar et al., 2013; Antinori et al., 2016; Wilson et al., 2016; Maclean and Arnell, 2010). However, the amount of explained variance is usually lower than in our experiment, which is a rare case where a physiological measure obtained from a laboratory experiment explains a large portion of inter-individual differences in the way we feel and behave.

To obtain the clear correlation with AQ, free-viewing of the bistable stimulus was essential: the correlation between pupillometry and AQ scores was eliminated when cueing attention, either explicitly, by instructing participants to attend globally/locally or by indicating that the surface formed by white or black dots alone is task-relevant, or implicitly, by introducing a task that is best performed by attending a single surface at a time. Although previous work has shown differences in bistable viewing that are related to autism [(Robertson et al., 2013); but see also (Said et al., 2013)], we found that the dynamics of perceptual oscillations between the two directions of motion were uncorrelated with autistic traits. The correlation with AQ was only revealed only when taking pupillometry measures to index how attention was distributed during the bistable perception.

The current study strengthens previous claims that pupil size is modulated not only by light, but also by perceptual ‘top-down’ processes (Binda and Murray, 2015; Mathôt and Van der Stigchel, 2015; Laeng and Endestad, 2012). The neural substrates of these modulatory mechanisms are beginning to be unveiled, involving pre-frontal input into the circuit of the pupillary response to light (Ebitz and Moore, 2017), which in turn may include the visual cortex (Binda and Gamlin, 2017). These multiple influences converge into a simple subcortical system that controls a unidimensional variable: pupil size. As we show here, under appropriate conditions (free viewing a bistable stimulus with brighter and darker components), this variable can provide an objective physiological correlate of conscious perception, so reliable that it can successfully reveal subtle inter-individual differences that are hard to study psychophysically. The method is quick, easy, and objective, and requires only relatively simple, portable apparatus. We hope it can form the basis for tests suitable for clinical populations, possibly providing a new powerful tool for identifying anomalies in perception that are predictive of ASD.

1. 1. Turi M
   2. Burr DC
   3. Binda P

   (2017) Data from: Pupillometry reveals perceptual differences that are tightly linked to autistic traits in typical adults

   Available at Dryad Digital Repository under a CC0 Public Domain Dedication.

   http://dx.doi.org/10.5061/dryad.b3ng3

1. 1. Antinori A
   2. Smillie LD
   3. Carter OL

   (2016) Personality measures link slower binocular rivalry switch rates to higher levels of self-discipline

   Frontiers in Psychology 7:2008.

   https://doi.org/10.3389/fpsyg.2016.02008

   • PubMed
   • Google Scholar
2. 1. Baron-Cohen S
   2. Wheelwright S
   3. Skinner R
   4. Martin J
   5. Clubley E

   (2001) The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians

   Journal of Autism and Developmental Disorders 31:5–17.

   https://doi.org/10.1023/A:1005653411471

   • PubMed
   • Google Scholar
3. 1. Behrmann M
   2. Avidan G
   3. Leonard GL
   4. Kimchi R
   5. Luna B
   6. Humphreys K
   7. Minshew N

   (2006) Configural processing in autism and its relationship to face processing

   Neuropsychologia 44:110–129.

   https://doi.org/10.1016/j.neuropsychologia.2005.04.002

   • PubMed
   • Google Scholar
4. 1. Binda P
   2. Gamlin PD

   (2017) Renewed attention on the pupil light reflex

   Trends in Neurosciences 40:455–457.

   https://doi.org/10.1016/j.tins.2017.06.007

   • PubMed
   • Google Scholar
5. 1. Binda P
   2. Murray SO

   (2015) Keeping a large-pupilled eye on high-level visual processing

   Trends in Cognitive Sciences 19:1–3.

   https://doi.org/10.1016/j.tics.2014.11.002

   • PubMed
   • Google Scholar
6. 1. Binda P
   2. Pereverzeva M
   3. Murray SO

   (2013) Attention to bright surfaces enhances the pupillary light reflex

   Journal of Neuroscience 33:2199–2204.

   https://doi.org/10.1523/JNEUROSCI.3440-12.2013

   • PubMed
   • Google Scholar
7. 1. Binda P
   2. Pereverzeva M
   3. Murray SO

   (2014) Pupil size reflects the focus of feature-based attention

   Journal of Neurophysiology 112:3046–3052.

   https://doi.org/10.1152/jn.00502.2014

   • PubMed
   • Google Scholar
8. 1. Brainard DH

   (1997) The psychophysics toolbox

   Spatial Vision 10:433–436.

   https://doi.org/10.1163/156856897X00357

   • PubMed
   • Google Scholar
9. 1. Carrasco M

   (2011) Visual attention: the past 25 years

   Vision Research 51:1484–1525.

   https://doi.org/10.1016/j.visres.2011.04.012

   • PubMed
   • Google Scholar
10. 1. Constantino JN
    2. Todd RD

    (2003) Autistic traits in the general population: a twin study

    Archives of General Psychiatry 60:524–530.

    https://doi.org/10.1001/archpsyc.60.5.524

    • PubMed
    • Google Scholar
11. 1. Cribb SJ
    2. Olaithe M
    3. Di Lorenzo R
    4. Dunlop PD
    5. Maybery MT

    (2016) Embedded figures test performance in the broader autism phenotype: a meta-analysis

    Journal of Autism and Developmental Disorders 46:2924–2939.

    https://doi.org/10.1007/s10803-016-2832-3

    • PubMed
    • Google Scholar
12. Book

    1. de-Wit L
    2. Wagemans J

    (2015)

    Individual Differences in Local and Global Perceptual Organization

    Oxford: The Oxford Handbook of Perceptual Organization, Oxford University Press.

    • Google Scholar
13. 1. Ebitz RB
    2. Moore T

    (2017) Selective modulation of the pupil light reflex by microstimulation of prefrontal cortex

    The Journal of Neuroscience 37:5008–5018.

    https://doi.org/10.1523/JNEUROSCI.2433-16.2017

    • PubMed
    • Google Scholar
14. 1. Einhäuser W
    2. Stout J
    3. Koch C
    4. Carter O

    (2008) Pupil dilation reflects perceptual selection and predicts subsequent stability in perceptual rivalry

    PNAS 105:1704–1709.

    https://doi.org/10.1073/pnas.0707727105

    • PubMed
    • Google Scholar
15. 1. Eldar E
    2. Cohen JD
    3. Niv Y

    (2013) The effects of neural gain on attention and learning

    Nature Neuroscience 16:1146–1153.

    https://doi.org/10.1038/nn.3428

    • PubMed
    • Google Scholar
16. 1. Frith U
    2. Happé F

    (1994) Autism: beyond "theory of mind"

    Cognition 50:115–132.

    https://doi.org/10.1016/0010-0277(94)90024-8

    • PubMed
    • Google Scholar
17. 1. Grinter EJ
    2. Maybery MT
    3. Van Beek PL
    4. Pellicano E
    5. Badcock JC
    6. Badcock DR

    (2009) Global visual processing and self-rated autistic-like traits

    Journal of Autism and Developmental Disorders 39:1278–1290.

    https://doi.org/10.1007/s10803-009-0740-5

    • PubMed
    • Google Scholar
18. 1. Happé F
    2. Frith U

    (1996) The neuropsychology of autism

    Brain 119:1377–1400.

    https://doi.org/10.1093/brain/119.4.1377

    • PubMed
    • Google Scholar
19. 1. Happé F
    2. Frith U

    (2006) The weak coherence account: detail-focused cognitive style in autism spectrum disorders

    Journal of Autism and Developmental Disorders 36:5–25.

    https://doi.org/10.1007/s10803-005-0039-0

    • PubMed
    • Google Scholar
20. 1. Happé FG
    2. Booth RD

    (2008) The power of the positive: revisiting weak coherence in autism spectrum disorders

    Quarterly Journal of Experimental Psychology 61:50–63.

    https://doi.org/10.1080/17470210701508731

    • PubMed
    • Google Scholar
21. 1. Hoekstra RA
    2. Bartels M
    3. Cath DC
    4. Boomsma DI

    (2008) Factor structure, reliability and criterion validity of the Autism-Spectrum Quotient (AQ): a study in Dutch population and patient groups

    Journal of Autism and Developmental Disorders 38:1555–1566.

    https://doi.org/10.1007/s10803-008-0538-x

    • PubMed
    • Google Scholar
22. 1. Kohs SC

    (1920) The block-design tests

    Journal of Experimental Psychology 3:357–376.

    https://doi.org/10.1037/h0074466

    • Google Scholar
23. 1. Kurita H
    2. Koyama T
    3. Osada H

    (2005) Autism-Spectrum quotient-japanese version and its short forms for screening normally intelligent persons with pervasive developmental disorders

    Psychiatry and Clinical Neurosciences 59:490–496.

    https://doi.org/10.1111/j.1440-1819.2005.01403.x

    • PubMed
    • Google Scholar
24. 1. Laeng B
    2. Endestad T

    (2012) Bright illusions reduce the eye's pupil

    PNAS 109:2162–2167.

    https://doi.org/10.1073/pnas.1118298109

    • PubMed
    • Google Scholar
25. 1. Lundqvist LO
    2. Lindner H

    (2017) Is the autism-spectrum quotient a valid measure of traits associated with the autism spectrum? a rasch validation in adults with and without autism spectrum disorders

    Journal of Autism and Developmental Disorders 47:2080–2091.

    https://doi.org/10.1007/s10803-017-3128-y

    • PubMed
    • Google Scholar
26. 1. Maclean MH
    2. Arnell KM

    (2010) Personality predicts temporal attention costs in the attentional blink paradigm

    Psychonomic Bulletin & Review 17:556–562.

    https://doi.org/10.3758/PBR.17.4.556

    • PubMed
    • Google Scholar
27. 1. Mathôt S
    2. Van der Stigchel S

    (2015) New light on the mind's eye: the pupillary light response as active vision

    Current Directions in Psychological Science 24:374–378.

    https://doi.org/10.1177/0963721415593725

    • PubMed
    • Google Scholar
28. 1. Milne E
    2. Szczerbinski M

    (2009) Global and local perceptual style, field-independence, and central coherence: An attempt at concept validation

    Advances in Cognitive Psychology 5:1–26.

    https://doi.org/10.2478/v10053-008-0062-8

    • PubMed
    • Google Scholar
29. 1. Mottron L
    2. Burack JA
    3. Iarocci G
    4. Belleville S
    5. Enns JT

    (2003) Locally oriented perception with intact global processing among adolescents with high-functioning autism: evidence from multiple paradigms

    Journal of Child Psychology and Psychiatry 44:904–913.

    https://doi.org/10.1111/1469-7610.00174

    • PubMed
    • Google Scholar
30. 1. Mottron L
    2. Dawson M
    3. Soulières I
    4. Hubert B
    5. Burack J

    (2006) Enhanced perceptual functioning in autism: an update, and eight principles of autistic perception

    Journal of Autism and Developmental Disorders 36:27–43.

    https://doi.org/10.1007/s10803-005-0040-7

    • PubMed
    • Google Scholar
31. 1. Muth A
    2. Hönekopp J
    3. Falter CM

    (2014) Visuo-spatial performance in autism: a meta-analysis

    Journal of Autism and Developmental Disorders 44:3245–3263.

    https://doi.org/10.1007/s10803-014-2188-5

    • PubMed
    • Google Scholar
32. 1. Naber M
    2. Frässle S
    3. Einhäuser W

    (2011) Perceptual rivalry: reflexes reveal the gradual nature of visual awareness

    PLoS One 6:e20910.

    https://doi.org/10.1371/journal.pone.0020910

    • PubMed
    • Google Scholar
33. 1. Navon D

    (1977) Forest before trees: The precedence of global features in visual perception

    Cognitive Psychology 9:353–383.

    https://doi.org/10.1016/0010-0285(77)90012-3

    • Google Scholar
34. 1. Palmer CJ
    2. Paton B
    3. Enticott PG
    4. Hohwy J

    (2015) 'Subtypes' in the presentation of autistic traits in the general adult population

    Journal of Autism and Developmental Disorders 45:1291–1301.

    https://doi.org/10.1007/s10803-014-2289-1

    • PubMed
    • Google Scholar
35. 1. Pelli DG

    (1997) The VideoToolbox software for visual psychophysics: transforming numbers into movies

    Spatial Vision 10:437–442.

    https://doi.org/10.1163/156856897X00366

    • PubMed
    • Google Scholar
36. 1. Plaisted K
    2. Saksida L
    3. Alcántara J
    4. Weisblatt E

    (2003) Towards an understanding of the mechanisms of weak central coherence effects: experiments in visual configural learning and auditory perception

    Philosophical Transactions of the Royal Society B: Biological Sciences 358:375–386.

    https://doi.org/10.1098/rstb.2002.1211

    • PubMed
    • Google Scholar
37. 1. Plaisted K
    2. Swettenham J
    3. Rees L

    (1999) Children with autism show local precedence in a divided attention task and global precedence in a selective attention task

    Journal of Child Psychology and Psychiatry 40:733–742.

    https://doi.org/10.1111/1469-7610.00489

    • PubMed
    • Google Scholar
38. 1. Robertson CE
    2. Kravitz DJ
    3. Freyberg J
    4. Baron-Cohen S
    5. Baker CI

    (2013) Slower rate of binocular rivalry in autism

    Journal of Neuroscience 33:16983–16991.

    https://doi.org/10.1523/JNEUROSCI.0448-13.2013

    • PubMed
    • Google Scholar
39. 1. Ruta L
    2. Mazzone D
    3. Mazzone L
    4. Wheelwright S
    5. Baron-Cohen S

    (2012) The autism-spectrum quotient--italian version: a cross-cultural confirmation of the broader autism phenotype

    Journal of Autism and Developmental Disorders 42:625–633.

    https://doi.org/10.1007/s10803-011-1290-1

    • PubMed
    • Google Scholar
40. 1. Ruzich E
    2. Allison C
    3. Smith P
    4. Watson P
    5. Auyeung B
    6. Ring H
    7. Baron-Cohen S

    (2015) Measuring autistic traits in the general population: a systematic review of the Autism-Spectrum Quotient (AQ) in a nonclinical population sample of 6,900 typical adult males and females

    Molecular Autism 6:2.

    https://doi.org/10.1186/2040-2392-6-2

    • PubMed
    • Google Scholar
41. 1. Said CP
    2. Egan RD
    3. Minshew NJ
    4. Behrmann M
    5. Heeger DJ

    (2013) Normal binocular rivalry in autism: implications for the excitation/inhibition imbalance hypothesis

    Vision Research 77:59–66.

    https://doi.org/10.1016/j.visres.2012.11.002

    • PubMed
    • Google Scholar
42. 1. Skuse DH
    2. Mandy W
    3. Steer C
    4. Miller LL
    5. Goodman R
    6. Lawrence K
    7. Emond A
    8. Golding J

    (2009) Social communication competence and functional adaptation in a general population of children: preliminary evidence for sex-by-verbal IQ differential risk

    Journal of the American Academy of Child & Adolescent Psychiatry 48:128–137.

    https://doi.org/10.1097/CHI.0b013e31819176b8

    • PubMed
    • Google Scholar
43. 1. Tadin D

    (2015) Suppressive mechanisms in visual motion processing: From perception to intelligence

    Vision Research 115:58–70.

    https://doi.org/10.1016/j.visres.2015.08.005

    • PubMed
    • Google Scholar
44. 1. Van der Hallen R
    2. Evers K
    3. Brewaeys K
    4. Van den Noortgate W
    5. Wagemans J

    (2015) Global processing takes time: A meta-analysis on local-global visual processing in ASD

    Psychological Bulletin 141:549–573.

    https://doi.org/10.1037/bul0000004

    • PubMed
    • Google Scholar
45. Book

    1. Wechsler D

    (1955)

    Manual for the Wechsler Adult Intelligence Scale

    Oxford, England: Psychological Corp.

    • Google Scholar
46. 1. Wheelwright S
    2. Auyeung B
    3. Allison C
    4. Baron-Cohen S

    (2010) Defining the broader, medium and narrow autism phenotype among parents using the Autism Spectrum Quotient (AQ)

    Molecular Autism 1:10.

    https://doi.org/10.1186/2040-2392-1-10

    • PubMed
    • Google Scholar
47. 1. Wilson KE
    2. Lowe MX
    3. Ruppel J
    4. Pratt J
    5. Ferber S

    (2016) The scope of no return: Openness predicts the spatial distribution of Inhibition of Return

    Attention, Perception, & Psychophysics 78:209–217.

    https://doi.org/10.3758/s13414-015-0991-5

    • Google Scholar
48. 1. Witkin HA
    2. Asch SE

    (1948) Studies in space orientation; perception of the upright in the absence of a visual field

    Journal of Experimental Psychology 38:603–614.

    https://doi.org/10.1037/h0055372

    • PubMed
    • Google Scholar
49. Book

    1. Witkin HA

    (1971)

    A Manual for the Embedded Figures Tests

    Palo Alto, California: Consulting Psychologists Press.

    • Google Scholar

Author details

1. Marco Turi

   1. Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
   2. Fondazione Stella Maris Mediterraneo, Potenza, Italy

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4495-0804

2. David Charles Burr

   1. Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
   2. Department of Neuroscience, Psychology, Pharmacology and Child Health, University of Florence, Firenze, Italy
   3. School of Psychology, University of Sydney, Sydney, Australia

   Contribution

   Conceptualization, Supervision, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   dave@in.cnr.it

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1541-8832

3. Paola Binda

   1. Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
   2. CNR Neuroscience Institute, Pisa, Italy

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   paola1binda@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7200-353X

• 18,170

  views

• 647

  downloads

• 51

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.