Many images can be perceived at either a global or local level, as in the classic example of the forest and the trees. Typical development brings about a gradual shift from local to global focus (see Goodenough, 1976; Kimchi, 2015; Wagemans et al., 2012 for reviews). However, individuals differ in their tendency to focus locally or globally, and there is growing evidence that individuals with autism show biases toward local processing (for meta-analysis, see Van der Hallen et al., 2015). Autistic individuals outperform neurotypicals in tasks where fine-grained visual features must be abstracted from their global context, such as the embedded figure task (Shah and Frith, 1983) or the Navon figures (Plaisted et al., 1999). However, it has been pointed out that many of these are sensitive to task instructions, and may also be affected by cognitive strategy (see Baisa et al., 2019; Horlin et al., 2016 for reviews). Therefore, there is a pressing need for objective and quantitative indices of the local-global preference.

In our previous study (Turi et al., 2018), we introduced a pupillometry-based index that revealed a strong relationship between local-global processing styles and autistic-like traits, assessed in neurotypical adults by the Autism-Spectrum Quotient (AQ) questionnaire (Baron-Cohen et al., 2001). Observers viewed a classic bistable motion illusion (Andersen and Bradley, 1998; Treue et al., 1991), comprising transparent fields of black and white dots moving in opposite directions, giving the impression of a cylinder rotating about its vertical axis, with the direction of rotation and the color of the front surface changing periodically. Pupil size modulated in synchrony with perceptual reports, dilating when the front surface was black, constricting when white. Importantly, the magnitude of pupillary modulation varied with AQ, stronger for participants with higher AQ scores, consistent with attention being focused on the front surface (local style). Participants with lower AQ scores showed smaller or no pupil modulations, consistent with attention being distributed over the front and rear surface of the cylinder at all times (global style).

That study introduced a simple pupillometric measure that could potentially provide a quantitative and objective index of local-global preference in both neurotypical and autistic individuals. However, the illusory and bistable nature of the stimulus could be a limitation of the technique. Firstly, the neural dynamics underlying these phenomena are complex and only partially understood (Brascamp et al., 2018), and it is unclear whether any of these perceptual or decisional factors drive the pupil modulations. In addition, susceptibility to visual illusions also correlates with autistic traits (Chouinard et al., 2016; Happé, 1996; Mitchell et al., 2010; Pellicano and Burr, 2012), making it difficult to judge whether the differences in pupil modulation reflect differences in susceptibility to illusions rather than differences in perceptual style. Finally, although the pupillometric measurements are objective, their analyses relied on the subjective reports of perceptual alternations, which may be unreliable and difficult to obtain from some participants, particularly young children and clinical groups.

To address these concerns, we adapted the technique so it does not rely on illusory reversals, by disambiguating the 3D rotation of the cylinder with binocular disparity cues. Observers viewed the display through a stereoscope, and stereo-depth was periodically swapped to bring either the black or white dots to the foreground, which also changed the direction of rotation of the cylinder. This modified stimulus allowed us to analyze changes in pupil size based on objective changes of stereo-depth, rather than perceptual reports. It also allowed us to eliminate completely the need for participants to report their perception, simply observing the alternating stimulus for a period of time.

We hypothesized that if the effect were driven by differences in perceptual style, the 3D layout of the stimulus should evoke a similar pupil modulation to that we found in bistable perception, and this should correlate with AQ scores in neurotypical adults, irrespectively of whether they were required to make perceptual reports. We also tested whether other voluntary and involuntary responses (including spontaneous slow eye movements) could provide alternative indices of perceptual style, correlated with AQ. Our results confirmed the previous report that pupillometry measures correlate well with AQ, revealing a strong association between perceptual styles and autistic traits. Spontaneous eye-drift tracked the direction of motion, but these were unrelated to AQ.

We used a modified version of the stimulus used in Turi et al., 2018, where clouds of white dots and black dots moved in opposite directions to generate the bistable illusion of a cylinder rotating in 3D about its vertical axis, with direction of rotation alternating between clockwise and counterclockwise. In the present study, the direction of rotation was unambiguous, defined by periodically changing stereo-cues, following similar dynamics to the perceptual switches observed in our previous study. The association between motion direction and color remained constant within each trial, but varied across trials; consequently, switches in stereo-depth over a trial implied changes in both color and motion direction of the foreground. Fifty-three participants reported the changes in rotation direction during 60 s trials, while we monitored pupil size and eye movements (Figure 1).

Figure 1

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Figure 2A shows the average timecourse of pupil size synchronized to the stereo-depth swap and averaged over all participants, separately for phases with stereo-defined black or white foreground (blue and red, respectively). This figure has the same format as Figure 1B in Turi et al., 2018, but while Turi et al., 2018 aligned the traces to the perceptual reports of a bistable stimulus, here they are aligned to physical changes in the stimulus, ignoring perceptual reports. We define zero as the end of the stereo-switch, which lasted 1.2 s. This procedural difference leads to differences in the overall shapes of the pupil timecourses, but the basic result is the same: pupil size modulates with the brightness of the foreground, dilating when black (blue traces of Figure 2A) and constricting when white (red traces). The black trace at the top of Figure 2A shows the difference in pupil size for the two directions of rotation, which begins to emerge after the depth swap is complete, and remains for several seconds.

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The pupil-size modulations are consistent with attention being focused on the front surface, as a relative pupil dilation or constriction is expected from attending to black or white, respectively (Binda et al., 2013; Mathôt et al., 2013). It is important to reiterate that here, as in Turi et al., 2018, pupil-size modulations cannot be explained by changes in luminance, as it was constant during the whole experiment, nor by changes in the luminance distribution over space, as the white and black dots formed two overlapping transparent surfaces.

As in our previous study, there were large interindividual differences in the amplitude of pupil-size modulations, and again a large portion of the variance could be accounted for by the participants’ AQ score. Figure 2B plots the pupil modulation (difference in size for the two directions of motion) against AQ of each participant. The correlation is positive, strong, and highly significant (r = 0.45, p<0.001, base-10 logarithm of the Bayes Factor [lgBF] = 1.49), showing that the magnitude of the effect increases with AQ. This is consistent with the hypothesis that the magnitude of pupil-size modulation depends on the tendency to focus attention locally on the front surface (hence driven by surface color) or globally across both surfaces, and that these two strategies correspond well to the local and global perceptual styles associated with high and low autistic traits (Grinter et al., 2009; Happé and Frith, 2006).

That the association between pupil-size modulation and AQ holds in spite of the differences of the two studies has important implications. Firstly, it implies that the illusory bistable nature of the stimulus in Turi et al., 2018 is not the primary force driving the modulations in pupil size (although it might play a secondary role, accounting for the much higher correlation coefficient observed in our previous study). Secondly, it suggests that modulation of pupil size is not yoked to processes of perceptual decision and report, given that it can be driven by information about the physical stimulus only (see also below).

How specific is pupil-size modulation in revealing these AQ-linked interindividual differences? As in our previous study, we verified that the overall pupil responsivity could not account for this effect, as the mean pupil response (the size of the overall pupil dilation in the 1–2 s window following the depth swap) was reliably uncorrelated to AQ scores (r = −0.12, p=0.39, lgBF = −0.81, Figure 3—figure supplement 1, left column). Moreover, in line with the previous findings, perceptual performance was uncorrelated with AQ (r = 0.12, p=0.42, lgBF = −0.82; here we measured performance as accuracy, by cross-correlating perceptual reports with stimulus stereo-depth timecourses and taking the peak of the function).

We also monitored eye movements during the experiment, to see if they too may act as a predictor of perceptual style. Although participants were instructed to maintain fixation continuously on a salient mark throughout the experiment, most made periodic horizontal eye movements, with a slow phase in the direction of the cylinder rotation and a fast return phase (antagonistic OKN behavior, typical of transparent motion displays [Niemann et al., 1994]: see Figure 1B for example). We analyzed the slow phases of these gaze shifts and found them generally consistent with the direction of the foreground dots (Figure 2C). Although there was interindividual variability in this behavior (with some tracking the rear dots), this was not correlated with AQ scores (Figure 2D), with Bayes factor indicating that the lack of correlation was significant (r = 0.10, p=0.485, lgBF = −0.86). Consistently, factoring out gaze-velocity differences did not attenuate the correlation between pupil-size modulations and AQ (partial correlation: r = 0.44, p<0.01).

Although behavioral performance did not correlate with AQ, it is possible that the pupil modulations are driven, at least in part, by the participants’ keypress responses. We investigated this possibility with additional analyses and data. First, we re-analyzed our pupil-size data synchronized to the perceptual switches, rather than the stereo-depth swaps (Figure 2—figure supplement 1). The difference between pupil-size traces still correlated significantly with AQ (r = 0.39, p<0.01, lgBF = 0.72), indicating that our pupil-size modulation index is robust and relatively independent of the method used for parsing traces (Figure 3—figure supplement 1, compare the left and the middle columns).

More informatively, we measured a subsample of about half of our participants with a ‘no-report’ paradigm, where they simply viewed the stimulus for the same amount of time, without reporting their perception. The detailed form of the phase-locked timecourses of pupil size (Figure 3A, individual blue and red traces) differs from those in Figure 2, probably reflecting the lack of a pupil-dilation component linked to making and reporting perceptual decisions (Einhäuser et al., 2008). However, the timecourse of the pupil-size difference was very similar across experiments (compare the top black trace in Figures 2A and 3A), and its magnitude was clearly correlated with AQ score (Figure 3B). Correlation coefficients for the pupil-size difference and the other parameters were similar to the main experiment, where participants made active reports (Figure 3—figure supplement 1, compare leftmost and rightmost columns). The correlation between pupil-size difference and AQ scores did not differ across experiments (Fisher test: F = 0.3, p=0.76).

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These results support the hypothesis that there are multiple independent components of the pupil-size modulations related to observation of our complex stimulus. There is a transient dilation related to the key-press action (absent in the no-report experiment), best seen when time-locking pupil traces to the perceptual switches as in Figure 2—figure supplement 1: this component was not correlated with AQ (r = –0.15, p=0.29, lgBF = –0.72). There was also a transient constriction related to the perceptual change, isolated in the no-report experiment in Figure 3, which again did not correlate with AQ (r = –0.32, p=0.13, lgBF = –0.31). Finally, there was the relatively sustained pupil-size modulation, measured by the difference between traces for the two possible 3D layouts of the stimulus, and similarly observed in the two experiments. AQ scores were specifically correlated with this component (r = 0.38 or larger, p<0.05 and lgBF >0.6 in all cases), irrespective of whether perceptual reports were used for analysis or dispensed with altogether (see Figure 3—figure supplement 1 for a summary of the AQ-correlation analyses).

Data from the no-report experiment also provide a lower-bound estimate of the test-retest reliability of our pupillometric and gaze-tracking indices. Figure 4 shows the correlation between the main and no-report experiment (middle column) for all parameters. In addition, we estimated their internal consistency, by computing split-half reliabilities (left column). Although all parameters are significantly correlated across experiments, pupil-size difference has the highest test-retest reliability, in spite of having the lowest split-half reliability. Indeed the disattenuated correlation between experiments (right column of Figure 4), computed with the Spearman-Brown formula (Spearman, 1910), is particularly high (r = 0.93). This indicates that pupil-size modulations are noisy but consistent over time, suggesting that they are driven by stable individual differences. In contrast, the other indices, although more consistent within any experimental session, are far less consistent over time, possibly affected by the participants’ time-varying state, or the task requirements.

Figure 4

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In two experiments reported here, we built on our earlier demonstration (Turi et al., 2018) that pupillometry can be a useful tool to monitor perceptual style, which we show to be associated with autistic traits in neurotypical adults. We generalized this method to a simpler paradigm that does not rely on perceptual reports, and demonstrated its specificity relative to other indices of voluntary and spontaneous behavior.

Participants observed a 3D rotating cylinder whose direction of rotation and color of front and rear surfaces alternated periodically. Pupil size modulated with the alternations, constricting when the front surface was white, dilating when black. As in our previous work, pupil-size modulation was greater in high AQ individuals, suggesting that they attended more locally to the front surface, and less in individuals with low AQ, consistent with attention spread more globally over the whole cylinder. Whereas in the previous study the 3D layout of the stimulus was illusory and bistable and pupil-size modulation revealed by aligning pupil recordings with perceptual reports, here we disambiguated the 3D layout with stereo-depth, and aligned the pupil recordings to the physical changes. The correlation with AQ remained strong and significant, even when participants observed passively the stimuli without making any perceptual judgments. These results show that neither the complex neural dynamics underlying bistable perception (Brascamp et al., 2018) nor the act of reporting a perceptual decision is critical for inducing a modulation in pupil size. The reproducibility of pupil-size differences across our two experiments is testimony to its stability over time (which may be as high as r = 0.93 when the noise in each individual measurement is taken into account).

By contrasting data collected with and without perceptual reports, and across different methods of analysis, we were able to highlight at least three components contributing to the pupil-size modulations. One is a transient pupil dilation, time-locked to the reporting of a perceptual decision, and previously associated with increased arousal and norepinephrine release (Einhäuser et al., 2008). Another is a transient pupil constriction, time-locked to the occurrence of a stimulus change that may be interpreted as an ‘onset’ or ‘grating response’ (Barbur et al., 1992), associated with visual cortical activity (Sahraie et al., 2013). Finally, there is a more sustained pupil-size difference depending on the 3D layout of the stimulus; this is compatible with the effect of attending to bright against dark (Binda et al., 2013; Mathôt et al., 2013), mediated by a cortical circuit (Binda and Gamlin, 2017). Only the third of these, the stimulus-dependent modulation of pupil size, correlated significantly with participants’ AQ. Note that the pupil-size difference is not easily explained as a pupillary near response (the pupil constriction that accompanies near vision, Marg and Morgan, 1949). For this to influence the results, participants (especially those with higher AQ) would need to follow a particular color continuously, whether in front or behind. However, the gaze-velocity data shows that most participants (irrespectively of their AQ) tended to track the horizontal motion of the front surface (Figures 2D and 3D), suggesting there was no stimulus-driven change in vergence.

This OKN-like behavior (Niemann et al., 1994) could in principle have been informative of how participants distributed attention (Mestre and Masson, 1997). However, unlike pupil-size changes, gaze shifts are at least partially under voluntary control and were explicitly discouraged by asking participants to maintain fixation on a central mark. The tendency or ability to follow the instructions depends on a number of factors (e.g. motivation), which may be unrelated to AQ and might have masked any underlying correlation with AQ scores. On the other hand, the finding that gaze behavior is unrelated to AQ also indicates that differences in gaze behavior could not have generated differences in pupil-size modulations, as these were not necessarily stronger in participants who tended to track the foreground dots more systematically.

In summary, besides replicating (twice) the association between pupil-size modulations and autistic traits, the two experiments reported here confirm that pupillometry provides an index of local-global preference, which is reliable and stable over time, as may be expected for a parameter driven by genuine individual differences. In addition, data from the no-report experiment may be considered proof of principle that pupil measurements are informative even in a situation that requires minimal cooperation of the participants, which could pave the way for its implementation in a clinical sample with Autism Spectrum Disorder (ASD).

Experimental data have been uploaded to Zenodo at the following https://doi.org/10.5281/zenodo.4486576.

1. 1. Tortelli C
   2. Turi M
   3. Burr DC
   4. Binda P

   (2021) Zenodo

   Objective pupillometry shows that perceptual styles covary with autistic-like personality traits.

   https://doi.org/10.5281/zenodo.4486576

1. 1. Andersen RA
   2. Bradley DC

   (1998) Perception of three-dimensional structure from motion

   Trends in Cognitive Sciences 2:222–228.

   https://doi.org/10.1016/S1364-6613(98)01181-4

   • PubMed
   • Google Scholar
2. 1. Baisa A
   2. Mevorach C
   3. Shalev L

   (2019) Can performance in Navon letters among people with autism be affected by saliency? reexamination of the literature

   Review Journal of Autism and Developmental Disorders 6:1–12.

   https://doi.org/10.1007/s40489-018-0150-8

   • Google Scholar
3. 1. Barbur JL
   2. Harlow AJ
   3. Sahraie A

   (1992) Pupillary responses to stimulus structure, colour and movement

   Ophthalmic and Physiological Optics 12:137–141.

   https://doi.org/10.1111/j.1475-1313.1992.tb00276.x

   • PubMed
   • Google Scholar
4. 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
5. 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
6. 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
7. 1. Brainard DH

   (1997) The Psychophysics Toolbox

   Spatial Vision 10:433–436.

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

   • Google Scholar
8. 1. Brascamp J
   2. Sterzer P
   3. Blake R
   4. Knapen T

   (2018) Multistable Perception and the Role of the Frontoparietal Cortex in Perceptual Inference

   Annual Review of Psychology 69:77–103.

   https://doi.org/10.1146/annurev-psych-010417-085944

   • Google Scholar
9. 1. Chouinard PA
   2. Unwin KL
   3. Landry O
   4. Sperandio I

   (2016) Susceptibility to optical illusions varies as a function of the Autism-Spectrum quotient but not in ways predicted by Local-Global biases

   Journal of Autism and Developmental Disorders 46:2224–2239.

   https://doi.org/10.1007/s10803-016-2753-1

   • PubMed
   • Google Scholar
10. 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
11. 1. Goodenough DR

    (1976) The role of individual differences in field dependence as a factor in learning and memory

    Psychological Bulletin 83:675–694.

    https://doi.org/10.1037/0033-2909.83.4.675

    • PubMed
    • Google Scholar
12. 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
13. 1. Happé FG

    (1996) Studying weak central coherence at low levels: children with autism do not succumb to visual illusions. A research note

    Journal of Child Psychology and Psychiatry 37:873–877.

    https://doi.org/10.1111/j.1469-7610.1996.tb01483.x

    • PubMed
    • Google Scholar
14. 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
15. 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
16. 1. Horlin C
    2. Black M
    3. Falkmer M
    4. Falkmer T

    (2016) Proficiency of individuals with autism spectrum disorder at Disembedding figures: a systematic review

    Developmental Neurorehabilitation 19:54–63.

    https://doi.org/10.3109/17518423.2014.888102

    • PubMed
    • Google Scholar
17. Book

    1. Kimchi R

    (2015) The perception of hierarchical structure

    In: Wagemans J, editors. Oxford Handbook of Perceptual Organization. Oxford University Press. pp. 129–149.

    https://doi.org/10.1093/oxfordhb/9780199686858.001.0001

    • Google Scholar
18. 1. Marg E
    2. Morgan MW

    (1949)

    The pupillary near reflex; the relation of pupillary diameter to accommodation and the various components of convergence

    American Journal of Optometry and Archives of American Academy of Optometry 26:183–198.

    • PubMed
    • Google Scholar
19. 1. Mathôt S
    2. van der Linden L
    3. Grainger J
    4. Vitu F

    (2013) The pupillary light response reveals the focus of covert visual attention

    PLOS ONE 8:e78168.

    https://doi.org/10.1371/journal.pone.0078168

    • PubMed
    • Google Scholar
20. 1. Mestre DR
    2. Masson GS

    (1997) Ocular responses to motion parallax stimuli: the role of perceptual and attentional factors

    Vision Research 37:1627–1641.

    https://doi.org/10.1016/S0042-6989(96)00314-8

    • PubMed
    • Google Scholar
21. 1. Mitchell P
    2. Mottron L
    3. Soulières I
    4. Ropar D

    (2010) Susceptibility to the Shepard illusion in participants with autism: reduced top-down influences within perception?

    Autism Research 3:113–119.

    https://doi.org/10.1002/aur.130

    • PubMed
    • Google Scholar
22. 1. Niemann T
    2. Ilg UJ
    3. Hoffmann KP

    (1994) Eye movements elicited by transparent stimuli

    Experimental Brain Research 98:314–322.

    https://doi.org/10.1007/BF00228419

    • PubMed
    • Google Scholar
23. 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
24. 1. Pellicano E
    2. Burr D

    (2012) When the world becomes 'too real': a Bayesian explanation of autistic perception

    Trends in Cognitive Sciences 16:504–510.

    https://doi.org/10.1016/j.tics.2012.08.009

    • PubMed
    • Google Scholar
25. 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
26. 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
27. 1. Sahraie A
    2. Trevethan CT
    3. MacLeod MJ
    4. Urquhart J
    5. Weiskrantz L

    (2013) Pupil response as a predictor of blindsight in hemianopia

    PNAS 110:18333–18338.

    https://doi.org/10.1073/pnas.1318395110

    • PubMed
    • Google Scholar
28. 1. Shah A
    2. Frith U

    (1983) An islet of ability in autistic children: a research note

    Journal of Child Psychology and Psychiatry 24:613–620.

    https://doi.org/10.1111/j.1469-7610.1983.tb00137.x

    • PubMed
    • Google Scholar
29. 1. Spearman C

    (1910) Correlation calculated from faulty data

    British Journal of Psychology 3:271.

    https://doi.org/10.1111/j.2044-8295.1910.tb00206.x

    • Google Scholar
30. 1. Treue S
    2. Husain M
    3. Andersen RA

    (1991) Human perception of structure from motion

    Vision Research 31:59–75.

    https://doi.org/10.1016/0042-6989(91)90074-F

    • PubMed
    • Google Scholar
31. 1. Turi M
    2. Burr DC
    3. Binda P

    (2018) Pupillometry reveals perceptual differences that are tightly linked to autistic traits in typical adults

    eLife 7:e32399.

    https://doi.org/10.7554/eLife.32399

    • PubMed
    • Google Scholar
32. 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
33. 1. Wagemans J
    2. Feldman J
    3. Gepshtein S
    4. Kimchi R
    5. Pomerantz JR
    6. van der Helm PA
    7. van Leeuwen C

    (2012) A century of gestalt psychology in visual perception: ii. conceptual and theoretical foundations

    Psychological Bulletin 138:1218–1252.

    https://doi.org/10.1037/a0029334

    • PubMed
    • Google Scholar
34. 1. Wetzels R
    2. Wagenmakers EJ

    (2012) A default bayesian hypothesis test for correlations and partial correlations

    Psychonomic Bulletin & Review 19:1057–1064.

    https://doi.org/10.3758/s13423-012-0295-x

    • PubMed
    • Google Scholar

Author details

1. Chiara Tortelli

   Department of Surgical Medical Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy

   Contribution

   Data curation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4921-5005

2. Marco Turi

   Fondazione Stella Maris Mediterraneo, Chiaromonte, Italy

   Contribution

   Conceptualization, Writing - review and editing

   Competing interests

   No competing interests declared

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

3. David Charles Burr

   Department of Neuroscience Psychology Pharmacology and Child Health, University of Firenze, Firenze, Italy

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing - original draft, 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

4. Paola Binda

   Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

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

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