Autism spectrum disorders (ASDs) present a complex array of challenges, with sensory sensitivities, a recent addition to diagnostic criteria (Diagnostic and statistical manual of mental disorders: DSM-5, 2013), standing out as a dominant aspect of this condition. Autistic people often exhibit a general inflexibility in the motor domain and struggle to learn from motor errors, usually linked to restricted and repetitive behaviors (Lopez et al., 2005; Van Eylen et al., 2011; Pomè et al., 2023). This difficulty in understanding motor errors can hinder their ability to adapt to new sensory information, further exacerbating sensory overload (Pomè et al., 2023; Johnson et al., 2013; Mosconi et al., 2013).

Recent research has increasingly emphasized the role of predictive abilities in ASD (Sinha et al., 2014; van Boxtel and Lu, 2013; Van de Cruys et al., 2014; Pellicano and Burr, 2012). In their daily lives within an unpredictable and often chaotic world, humans are consistently engaged in anticipating what might happen next and preparing adaptively for potential threats in their surroundings (Diagnostic and statistical manual of mental disorders: DSM-5, 2013). Within this framework, recent theories propose that individuals with ASD may demonstrate a reduced reliance on prior information when making perceptual judgments. The Bayesian perspective suggests that the predictive system deficits in autism stem from a diminished Bayesian prior (Pellicano and Burr, 2012; Angeletos Chrysaitis and Seriès, 2023). From this perspective, the brain unconsciously infers information about the world by applying Bayes’ rule: it assesses the probability of a prediction given sensory data, considering the likelihood of sensory data given that prediction and the Bayesian prior. The Bayesian prior is essentially a probabilistic distribution representing the expectation of the environment being in a particular state before any observations are made (Pellicano and Burr, 2012). These predictive abilities include the role of perceptual priors, which play a crucial role in guiding individuals to make accurate predictions, especially when sensory data is limited. However, in cases of autism, the influence of prior knowledge is believed to be weakened, resulting in predictions that heavily rely on sensory input.

But how do these priors interact with the sensory and motor systems in autistic people, and how does this influence their perceptual stability? Predicting the consequences of one’s behavior is of vital importance when our movements produce changes on our sensory receptors. When we move our eyes, head, or body, we displace external stimuli on the retina. We must know about this self-produced displacement to avoid confusion with external stimulus motion. In other words, if we would not be able to predict the sensory consequences of our behavior, we would experience that the world is moving. The most frequent movements we perform are voluntary saccades. To rapidly direct the fovea, the area of highest resolution, toward regions of interest, we shift the eyeball with high speed about 3 times per second. Internal predictions about an imminent saccade ensure the smooth and stable continuity of our perception. This anticipation involves a signal incorporating information about the motor vector to predict the sensory consequences of the upcoming saccade. Such a signal is known as an efference copy, that is a copy of the motor command (for a review see Wurtz, 2018). During a tennis game, rapid oculomotor saccades are employed to track the high-velocity ball across the visual display. In the absence of a functional efference copy mechanism, the brain would encounter difficulty in anticipating the precise retinal location of the ball following each saccade. This could result in a transient period of visual disruption as the visual system adjusts to the new eye position. The efference copy, by predicting the forthcoming sensory consequences of the saccade, would bridge this gap and facilitate the maintenance of a continuous and accurate representation of the ball’s trajectory. A key pathway responsible for transmitting efference copy signals involves the superior colliculus (Sommer and Wurtz, 2002), a saccade command area, to the frontal eye field via the medio-dorsal nucleus of the thalamus. Lesions affecting this pathway can impair both motor and visual updating (Wurtz, 2018). We recently reported that in patients with lesions in thalamic nuclei, evidence for two pathways conveying efference copies exists. We found that patients with lesions in the medio-dorsal nucleus showed impairments in visual updating, while those with lesions in both the medio-dorsal and ventrolateral (VL) nuclei exhibited issues with motor updating (Bellebaum et al., 2005a; Bellebaum et al., 2005b; Bellebaum et al., 2006; Ostendorf et al., 2013; Ostendorf et al., 2010; Zimmermann et al., 2020).

Here, we hypothesized that the impairment of efference copy signals would result in an enduring lack of complete confirmation for pre-saccadic predictions through post-saccadic feedback. A failure in processing efference copy information might have consequences for perception around the execution of saccades. Their reliance on post-saccadic sensory feedback, rather than accurate efference copy predictions, results in a distorted perception of the visual world following eye movements (see Figure 1).

Figure 1

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First, intra-saccadic vision might be affected. We never perceive the self-produced motion stimulation on the retina produced by our own saccades. Some theories consider saccadic omission and saccadic suppression as resulting from an active mechanism. In this view, an efference copy would signal the occurrence of a saccade, yielding a transient decrease in visual sensitivity (Burr et al., 1994; Binda and Morrone, 2018; Zimmermann and Lange, 2022). Others however have pointed out the possibility that a purely passive mechanism suffices to induce saccadic omission (Castet et al., 2002). A recent study has found evidence for saccadic suppression already in the retina. Idrees et al., 2020 demonstrated that retinal ganglion cells in isolated retinae of mice and pigs respond to saccade-like displacements, leading to the suppression of responses to additional flashed visual stimuli through visually triggered retinal-circuit mechanisms. Importantly, their findings suggest that perisaccadic modulations of contrast sensitivity may have a purely visual origin, challenging the need for an efference copy in the early stages of saccadic suppression. However, the suppression they measured lasted much longer than time courses observed in behavioral data. An efference copy signal could thus be necessary to release perception from suppression.

Second, the internal updating of the spatial representation for the saccade vector would be affected. Since intra-saccadic vision is omitted, visual perception is confronted with a retinal image before the saccade and a different one after the performance of the saccade. Predicting the appearance of the post-saccadic image leads to a remapping of internal space and thereby to a seamless perception across the saccade. If the remapping process is not accurate, the appearance of the post-saccadic image would come as a surprise. Surprising stimuli appear much more salient than predicted ones. Failures in trans-saccadic remapping in individuals with autistic symptomatology could be the reason or at least contribute to the sensory overload they experience.

Third, in addition to problems in visual updating, a lack of proper efference copy transfer should produce problems in motor updating. In cases in which visual information is not available or reliable, the sensorimotor system must rely on internal estimates about its own movements.

In the laboratory, trans-saccadic updating can be investigated in motor behavior and in perceptual localization. In the double-step paradigm, two consecutive saccades are made to briefly displayed targets (Westheimer, 1954; Hallett and Lightstone, 1976). The first saccade occurs without visual references, relying on internal updating to determine the eye’s position. The accuracy of the second saccade quantifies the first saccade’s updating process.

Perceptual updating is tested similarly, with subjects remembering the position of a target and then indicating it relative to a second target after a single saccade. In this paradigm, one stimulus is shown before and another after saccade execution. Together these two stimuli produce the perception of ‘apparent motion’. If stimuli are placed such that the apparent motion path is orthogonal to the saccade path, then the orientation of the apparent motion path indicates how the saccade vector is integrated into vision. The apparent motion trajectory can only appear vertical if the movement of the eyes is perfectly accounted for, that is the retinotopic displacement is largely compensated, ensuring spatial stability. However, small biases of motion direction – implying under- (or over-) compensation of the eye movement – can indicate relative failures in this stabilization process. In a seminal study, Szinte and Cavanagh, 2012 found a slight over-compensation of the saccade vector leading to apparent motion slightly tilted against the direction of the saccade. More importantly, when efference copies are not available, that is localization occurring at the time of a second saccade in a double-step task, a strong saccade under-compensation occurs (Zimmermann et al., 2018).

This phenomenon cannot be explained by perisaccadic mislocalization of flashed visual stimuli (Fracasso et al., 2010; Szinte and Cavanagh, 2011), but the two phenomena may be related in that they may both depend upon efference copy information.

In the present study, we directly investigated whether the precision of efference copy signals influences the accumulation of sensory predictions over time and explored potential variations in this mechanism among individuals with a heightened autistic trait. The autistic traits of the whole population form a continuum, with ASD diagnosis usually situated on the high end (Ruzich et al., 2015; Lundström et al., 2012; Robinson et al., 2011). Moreover, autistic traits share a genetic and biological etiology with ASD (Bralten et al., 2018). Thus, quantifying autistic-trait-related differences in healthy people can provide unique perspectives as well as a useful surrogate for understanding the symptoms of ASD (Ruzich et al., 2015; Sucksmith et al., 2011). In two experiments, we focused on the role of efference copy in the pathophysiology of autistic traits, particularly in maintaining perceptual continuity during saccades. We hypothesized that individuals with a high autistic phenotype would exhibit decreased accuracy in second saccades during double-step tasks and under-compensation when localizing a stimulus based on efference copy signals. These findings could shed light on the visual stability and predictive challenges faced by autistic people.

In the current study, we examined oculomotor efference copy signals in a sample of healthy adults with variable autistic traits using both, a motor and visual updating task. We show that a disrupted efference copy mechanism can lead to inaccurate predictions about impending eye movements, forcing the visuo-motor system to rely more heavily on actual sensory input rather than the anticipated sensory consequences of self-generated eye movements.

In our first experiment, the double-step saccade task, we found an impairment in the capacity of participants exhibiting high levels of autistic traits to program double-step saccades which require efference copy information about the size of the previous saccade. Conversely, participants with relatively low autistic characteristic perform accurately in the double-step task. In the high-autism-like subsample, the final eye position after the second saccade markedly deviates from the position of the second target, and the magnitude of this error far surpasses that observed in the low-autism-like group. This specific impairment in the accuracy of second saccades lends support to the hypothesis that heightened autistic traits correlates with a deficit in constructing a spatial representation of the second target's location based on forward models of efference copy. Notably, the mislocalization of the second saccade target cannot be explained by differences in the dynamics of the saccade to the first target: The observation that the accuracy of the initial saccade in the double-step task remains consistent regardless of the degree of autism-like trait severity implies that individuals with more pronounced traits do not exhibit inherent challenges in the encoding and processing of spatial information for intended saccades. Instead, when preparing for a saccade that must compensate for a preceding eye movement that altered the retinotopic representation of the target, those with more severe traits encounter difficulties. Likewise, the bias in localizing the target cannot be attributed to working memory deficits within our high-autism-like-symptom subsample; it is improbable that the mislocalization of the second target results solely from documented working memory deficits in ASD (Han et al., 2022).

Our findings suggest that a general memory deficit is unlikely to fully explain the observed bias in high-AQ participants' second saccades. As highlighted in Figure 3A, the bias was specific to the horizontal dimension, weakening the argument for a global memory issue affecting both vertical and horizontal encoding of target location. A t-test between our group of participants revealed a difference in saccade accuracy for the x dimension (p = 0.03) but not in the y dimension (p = 0.88). However, it is important to acknowledge that even under non-darkness conditions, participants might rely on a combination of internal updating based on the initial target location and visual cues from the environment, such as screen borders. This potential use of visual references could contribute to the observed bias in the high-AQ group. If high-AQ participants differed in their reliance on visual cues compared to the low-AQ group, it could explain the specific pattern of altered remapping observed in the horizontal dimension. This possibility aligns with our argument for an abnormal remapping process underlying the results. While altered efference copy signals remain a strong candidate, the potential influence of visual cues on remapping in this population warrants further investigation. Future studies could incorporate a darkness condition to isolate the effects of internal updating on the first saccade, and systematically manipulate the availability of visual cues throughout the task. This would allow for a more nuanced understanding of how internal updating and visual reference use interact in the double-step paradigm, particularly for individuals with varying AQ scores.

Autistic people show remarkable similarities to the symptoms of schizophrenia patients (Barlati et al., 2020; Hommer and Swedo, 2015; De Crescenzo et al., 2019; Krieger et al., 2021). Both ASD and schizophrenia patients are considered to have impairments at both extremes, involving either an excessive or inadequate reliance on sensory signals, resulting in inflexible behavior and issues in recurrent neural network representation (Idei et al., 2017; Philippsen and Nagai, 2018). Using a double-step saccade task, previous research has reported multiple lines of evidence demonstrating disrupted efference copy signals in schizophrenia. Importantly, similar to our findings, this disruption has implications for perceptual and motor precision, highlighting the interconnectedness of corollary discharge deficits and motor inflexibility (Thakkar et al., 2015b; Thakkar et al., 2015a).

A dramatic consequence of altered efference copy signaling is the inability to account for own eye movements when judging the location of a target presented intra-saccadically. The trans-saccadic localization task yielded results that provide further insights into the role of efference copy in individuals with varying levels of autistic traits. In this task, all participants, regardless of their autistic trait levels, successfully executed accurate saccades to peripheral targets. However, the key factor emerged in the subsequent perceptual judgments made by these individuals. Individuals with high autistic traits exhibited a significant mislocalization of a remembered stimulus position across saccades. In essence, they perceived the visual stimulus as having shifted in space more than it actually did during the eye movement. This indicates that their brain is relying more on the actual sensory feedback than the efference copy prediction. The consequence is an inaccurate perception of the stimulus’ location in space.

The results of both experiments shed light on the relationship between efference copy failure and Bayesian theories of autism (Van de Cruys et al., 2014; Pellicano and Burr, 2012; Lawson et al., 2014). In the double-step task, individuals with higher autistic traits exhibited difficulties in utilizing efference copy signals to construct a spatial representation of the second target location. This impairment suggests that in high autistic characteristics pre-saccadic predictions will never be completely confirmed by the post-saccadic feedback. This constant lack of a successful match between predicted sensory consequences of self-generated eye movements and post-saccadic evidence can lead over time to the build-up of weak priors with high variance.

Similarly, in the trans-saccadic localization task, high autistic traits were associated with failures in spatial updating across saccade execution. These findings align with the Bayesian framework, where the brain continuously updates its internal model by combining sensory information with prior knowledge. In the context of autism, a failure in efference copy can further contribute to the unique sensory integration processes observed in individuals on the autism spectrum. Our results are supported by recent evidence on the clinical population. Using a blanking task in a group of children with and without Autism (Yao et al., 2021) suggested that that the ability to flexibly adjust the precision of priors may serve a protective function in autistic people, potentially influencing the severity of symptom presentation. Moreover, Park et al., 2021 highlighted a unique relationship between smooth pursuit quality and motion prediction in ASD: unlike typically developing children, those with ASD did not exhibit a central-tendency bias over time, suggesting deficits in integrating prior knowledge about environmental statistics.

We have recently suggested a disruption in causal inference in the high autistic characteristics, as these individuals tend to attribute post-saccadic errors to external factors – a dynamic and unstable world – rather than accurately linking them to their own motor actions (Pomè et al., 2023). Consequently, a bias in causal inference emerges, stemming from the efference copy disruption, where the brain struggles to correctly attribute the cause of motor errors, leading to a heightened reliance on sensory feedback instead of motor predictions. As previously postulated this skewed causal inference also interconnect with motor inflexibility. Inflexibility in the motor domain might also have consequences for visual stability. Here, participants with high autistic traits faced challenges in executing saccades with precision, particularly when compensating for previous eye movements that had altered target representations. In the trans-saccadic localization task, we observed that these individuals leaned more heavily on post-saccadic sensory feedback than efference copy predictions. This not only led to an inaccurate perception of stimuli location but also underscored their limited ability to update and adapt motor actions based on sensory information.

It is essential to note that our participant pool lacked pre-existing diagnoses before engaging in the experiments and we must address limitations associated with the AQ questionnaire. The AQ questionnaire demonstrates adequate test–retest reliability (Baron-Cohen et al., 2001), normal distribution of sum scores in the general population (Hurst et al., 2007), and cross-cultural equivalence has been established in Dutch and Japanese samples (Hoekstra et al., 2008; Kurita et al., 2005; Wakabayashi et al., 2006). The AQ effectively categorizes individuals into low, average, and high degrees of autistic traits, demonstrating sensitivity for both group and individual assessments (Tennant and Conaghan, 2007).

However, evolving research underscores many aspects that are not fully captured by the self-administered questionnaire: for example, gender differences in ASD trait manifestation (Lai et al., 2011). Autistic females may exhibit more socially typical interests, often overlooked by professionals (Simcoe et al., 2023). Camouflaging behaviors, employed by autistic women to blend in, pose challenges for accurate diagnosis (Cook et al., 2022). Late diagnoses are attributed to a lack of awareness, gendered traits, and outdated assessment tools (Zener, 2019). Moving forward, complementing AQ evaluations in the general population with other questionnaires, such as those assessing camouflaging abilities (Hull et al., 2019), or motor skills in everyday situation (MOSES-test Hillus et al., 2019) becomes crucial for a comprehensive understanding of autistic traits.

In conclusion, our study underscores the critical role of efference copy in maintaining perceptual stability during eye movements and highlights its potential relevance to the sensory processing differences observed in individuals with heightened autistic traits. These findings contribute to our understanding of the complex interplay between prior knowledge, sensory integration, and motor control in the context of ASD, opening avenues for future research and potential interventions aimed at enhancing perceptual stability in this population.

Data has been deposited in Zenodo (https://doi.org/10.5281/zenodo.11277648).

1. 1. Pomè A
   2. Zimmermann E

   (2024) Zenodo

   Visuo-motor updating in individuals with heightened autistic traits.

   https://doi.org/10.5281/zenodo.11277648

1. 1. Angeletos Chrysaitis N
   2. Seriès P

   (2023) 10 years of Bayesian theories of autism: A comprehensive review

   Neuroscience & Biobehavioral Reviews 145:105022.

   https://doi.org/10.1016/j.neubiorev.2022.105022

   • Google Scholar
2. 1. Barlati S
   2. Minelli A
   3. Ceraso A
   4. Nibbio G
   5. Carvalho Silva R
   6. Deste G
   7. Turrina C
   8. Vita A

   (2020) Social cognition in a research domain criteria perspective: a bridge between schizophrenia and autism spectra disorders

   Frontiers in Psychiatry 11:806.

   https://doi.org/10.3389/fpsyt.2020.00806

   • PubMed
   • Google Scholar
3. 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
4. 1. Bellebaum C
   2. Hoffmann KP
   3. Daum I

   (2005a) Post-saccadic updating of visual space in the posterior parietal cortex in humans

   Behavioural Brain Research 163:194–203.

   https://doi.org/10.1016/j.bbr.2005.05.007

   • PubMed
   • Google Scholar
5. 1. Bellebaum C
   2. Daum I
   3. Koch B
   4. Schwarz M
   5. Hoffmann KP

   (2005b) The role of the human thalamus in processing corollary discharge

   Brain 128:1139–1154.

   https://doi.org/10.1093/brain/awh474

   • PubMed
   • Google Scholar
6. 1. Bellebaum C
   2. Hoffmann KP
   3. Koch B
   4. Schwarz M
   5. Daum I

   (2006) Altered processing of corollary discharge in thalamic lesion patients

   The European Journal of Neuroscience 24:2375–2388.

   https://doi.org/10.1111/j.1460-9568.2006.05114.x

   • PubMed
   • Google Scholar
7. 1. Binda P
   2. Morrone MC

   (2018) Vision during saccadic eye movements

   Annual Review of Vision Science 4:193–213.

   https://doi.org/10.1146/annurev-vision-091517-034317

   • PubMed
   • Google Scholar
8. 1. Bralten J
   2. van Hulzen KJ
   3. Martens MB
   4. Galesloot TE
   5. Arias Vasquez A
   6. Kiemeney LA
   7. Buitelaar JK
   8. Muntjewerff JW
   9. Franke B
   10. Poelmans G

   (2018) Autism spectrum disorders and autistic traits share genetics and biology

   Molecular Psychiatry 23:1205–1212.

   https://doi.org/10.1038/mp.2017.98

   • PubMed
   • Google Scholar
9. 1. Burr DC
   2. Morrone MC
   3. Ross J

   (1994) Selective suppression of the magnocellular visual pathway during saccadic eye movements

   Nature 371:511–513.

   https://doi.org/10.1038/371511a0

   • PubMed
   • Google Scholar
10. 1. Castet E
    2. Jeanjean S
    3. Masson GS

    (2002) Motion perception of saccade-induced retinal translation

    PNAS 99:15159–15163.

    https://doi.org/10.1073/pnas.232377199

    • PubMed
    • Google Scholar
11. 1. Cook J
    2. Crane L
    3. Hull L
    4. Bourne L
    5. Mandy W

    (2022) Self-reported camouflaging behaviours used by autistic adults during everyday social interactions

    Autism 26:406–421.

    https://doi.org/10.1177/13623613211026754

    • PubMed
    • Google Scholar
12. 1. De Crescenzo F
    2. Postorino V
    3. Siracusano M
    4. Riccioni A
    5. Armando M
    6. Curatolo P
    7. Mazzone L

    (2019) Autistic symptoms in schizophrenia spectrum disorders: a systematic review and meta-analysis

    Frontiers in Psychiatry 10:78.

    https://doi.org/10.3389/fpsyt.2019.00078

    • PubMed
    • Google Scholar
13. Book

    1. Diagnostic and statistical manual of mental disorders: DSM-5

    (2013) Diagnostic and Statistical Manual of Mental Disorders: DSM-5

    American Psychiatric Publishing, Inc.

    https://doi.org/10.1176/appi.books.9780890425596

    • Google Scholar
14. 1. Fracasso A
    2. Caramazza A
    3. Melcher D

    (2010) Continuous perception of motion and shape across saccadic eye movements

    Journal of Vision 10:14.

    https://doi.org/10.1167/10.13.14

    • PubMed
    • Google Scholar
15. 1. Freitag CM
    2. Retz-Junginger P
    3. Retz W
    4. Seitz C
    5. Palmason H
    6. Meyer J
    7. Rösler M
    8. von Gontard A

    (2007) Evaluation der deutschen version des autismus-spektrum-quotienten (AQ) - die Kurzversion AQ-k

    Zeitschrift Für Klinische Psychologie Und Psychotherapie 36:280–289.

    https://doi.org/10.1026/1616-3443.36.4.280

    • Google Scholar
16. 1. Hallett PE
    2. Lightstone AD

    (1976) Saccadic eye movements towards stimuli triggered by prior saccades

    Vision Research 16:99–106.

    https://doi.org/10.1016/0042-6989(76)90083-3

    • PubMed
    • Google Scholar
17. 1. Han YMY
    2. Chan MC
    3. Chan MMY
    4. Yeung MK
    5. Chan AS

    (2022) Effects of working memory load on frontal connectivity in children with autism spectrum disorder: a fNIRS study

    Scientific Reports 12:1522.

    https://doi.org/10.1038/s41598-022-05432-3

    • PubMed
    • Google Scholar
18. 1. Hillus J
    2. Moseley R
    3. Roepke S
    4. Mohr B

    (2019) Action semantic deficits and impaired motor skills in autistic adults without intellectual impairment

    Frontiers in Human Neuroscience 13:256.

    https://doi.org/10.3389/fnhum.2019.00256

    • PubMed
    • Google Scholar
19. 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
20. 1. Hommer RE
    2. Swedo SE

    (2015) Schizophrenia and autism-related disorders

    Schizophrenia Bulletin 41:313–314.

    https://doi.org/10.1093/schbul/sbu188

    • PubMed
    • Google Scholar
21. 1. Hui J
    2. Wang Y
    3. Zhang P
    4. Tse PU
    5. Cavanagh P

    (2020) Apparent motion is computed in perceptual coordinates

    I-Perception 11:2041669520933309.

    https://doi.org/10.1177/2041669520933309

    • PubMed
    • Google Scholar
22. 1. Hull L
    2. Mandy W
    3. Lai MC
    4. Baron-Cohen S
    5. Allison C
    6. Smith P
    7. Petrides KV

    (2019) Development and validation of the camouflaging autistic traits questionnaire (CAT-Q)

    Journal of Autism and Developmental Disorders 49:819–833.

    https://doi.org/10.1007/s10803-018-3792-6

    • PubMed
    • Google Scholar
23. 1. Hurst RM
    2. Mitchell JT
    3. Kimbrel NA
    4. Kwapil TK
    5. Nelson-Gray RO

    (2007) Examination of the reliability and factor structure of the Autism Spectrum Quotient (AQ) in a non-clinical sample

    Personality and Individual Differences 43:1938–1949.

    https://doi.org/10.1016/j.paid.2007.06.012

    • Google Scholar
24. Conference

    1. Idei H
    2. Murata S
    3. Chen Y
    4. Yamashita Y
    5. Tani J
    6. Ogata T

    (2017) Reduced behavioral flexibility by aberrant sensory precision in autism spectrum disorder: A neurorobotics experiment

    2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob. pp. 271–276.

    https://doi.org/10.1109/DEVLRN.2017.8329817

    • Google Scholar
25. 1. Idrees S
    2. Baumann MP
    3. Franke F
    4. Münch TA
    5. Hafed ZM

    (2020) Perceptual saccadic suppression starts in the retina

    Nature Communications 11:1977.

    https://doi.org/10.1038/s41467-020-15890-w

    • PubMed
    • Google Scholar
26. 1. Johnson BP
    2. Rinehart NJ
    3. White O
    4. Millist L
    5. Fielding J

    (2013) Saccade adaptation in autism and Asperger’s disorder

    Neuroscience 243:76–87.

    https://doi.org/10.1016/j.neuroscience.2013.03.051

    • PubMed
    • Google Scholar
27. 1. Kleiner M
    2. Brainard DH
    3. Pelli D
    4. Ingling A
    5. Murray R
    6. Broussard C

    (2007) What’s new in Psychtoolbox-3

    Perception 36:1–16.

    https://doi.org/10.1068/v070821

    • Google Scholar
28. 1. Krieger I
    2. Grossman-Giron A
    3. Comaneshter D
    4. Weinstein O
    5. Kridin K
    6. Cohen AD
    7. Tzur Bitan D

    (2021) The co-occurrence of autistic spectrum disorder and schizophrenia: A nationwide population-based study

    Journal of Psychiatric Research 138:280–283.

    https://doi.org/10.1016/j.jpsychires.2021.04.012

    • PubMed
    • Google Scholar
29. 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
30. 1. Lai MC
    2. Lombardo MV
    3. Pasco G
    4. Ruigrok ANV
    5. Wheelwright SJ
    6. Sadek SA
    7. Chakrabarti B
    8. MRC AIMS Consortium
    9. Baron-Cohen S

    (2011) A behavioral comparison of male and female adults with high functioning autism spectrum conditions

    PLOS ONE 6:e20835.

    https://doi.org/10.1371/journal.pone.0020835

    • PubMed
    • Google Scholar
31. 1. Lawson RP
    2. Rees G
    3. Friston KJ

    (2014) An aberrant precision account of autism

    Frontiers in Human Neuroscience 8:302.

    https://doi.org/10.3389/fnhum.2014.00302

    • PubMed
    • Google Scholar
32. 1. Lopez BR
    2. Lincoln AJ
    3. Ozonoff S
    4. Lai Z

    (2005) Examining the relationship between executive functions and restricted, repetitive symptoms of Autistic Disorder

    Journal of Autism and Developmental Disorders 35:445–460.

    https://doi.org/10.1007/s10803-005-5035-x

    • PubMed
    • Google Scholar
33. 1. Lundström S
    2. Chang Z
    3. Råstam M
    4. Gillberg C
    5. Larsson H
    6. Anckarsäter H
    7. Lichtenstein P

    (2012) Autism spectrum disorders and autistic like traits: similar etiology in the extreme end and the normal variation

    Archives of General Psychiatry 69:46–52.

    https://doi.org/10.1001/archgenpsychiatry.2011.144

    • PubMed
    • Google Scholar
34. 1. Mosconi MW
    2. Luna B
    3. Kay-Stacey M
    4. Nowinski CV
    5. Rubin LH
    6. Scudder C
    7. Minshew N
    8. Sweeney JA

    (2013) Saccade adaptation abnormalities implicate dysfunction of cerebellar-dependent learning mechanisms in Autism Spectrum Disorders (ASD)

    PLOS ONE 8:e63709.

    https://doi.org/10.1371/journal.pone.0063709

    • PubMed
    • Google Scholar
35. 1. Ostendorf F
    2. Liebermann D
    3. Ploner CJ

    (2010) Human thalamus contributes to perceptual stability across eye movements

    PNAS 107:1229–1234.

    https://doi.org/10.1073/pnas.0910742107

    • PubMed
    • Google Scholar
36. 1. Ostendorf F
    2. Liebermann D
    3. Ploner CJ

    (2013) A role of the human thalamus in predicting the perceptual consequences of eye movements

    Frontiers in Systems Neuroscience 7:10.

    https://doi.org/10.3389/fnsys.2013.00010

    • PubMed
    • Google Scholar
37. 1. Park WJ
    2. Schauder KB
    3. Kwon OS
    4. Bennetto L
    5. Tadin D

    (2021) Atypical visual motion prediction abilities in autism spectrum disorder

    Clinical Psychological Science 9:944–960.

    https://doi.org/10.1177/2167702621991803

    • PubMed
    • Google Scholar
38. 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

    • Google Scholar
39. Conference

    1. Philippsen A
    2. Nagai Y

    (2018) Understanding the cognitive mechanisms underlying autistic behavior: a recurrent neural network study

    2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob. pp. 84–90.

    https://doi.org/10.1109/DEVLRN.2018.8761038

    • Google Scholar
40. 1. Pomè A
    2. Tyralla S
    3. Zimmermann E

    (2023) Altered oculomotor flexibility is linked to high autistic traits

    Scientific Reports 13:13032.

    https://doi.org/10.1038/s41598-023-40044-5

    • PubMed
    • Google Scholar
41. 1. Robinson EB
    2. Koenen KC
    3. McCormick MC
    4. Munir K
    5. Hallett V
    6. Happé F
    7. Plomin R
    8. Ronald A

    (2011) Evidence that autistic traits show the same etiology in the general population and at the quantitative extremes (5%, 2.5%, and 1%)

    Archives of General Psychiatry 68:1113–1121.

    https://doi.org/10.1001/archgenpsychiatry.2011.119

    • PubMed
    • Google Scholar
42. 1. Rouder JN
    2. Speckman PL
    3. Sun D
    4. Morey RD
    5. Iverson G

    (2009) Bayesian t tests for accepting and rejecting the null hypothesis

    Psychonomic Bulletin & Review 16:225–237.

    https://doi.org/10.3758/PBR.16.2.225

    • Google Scholar
43. 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
44. 1. Simcoe SM
    2. Gilmour J
    3. Garnett MS
    4. Attwood T
    5. Donovan C
    6. Kelly AB

    (2023) Are there gender-based variations in the presentation of Autism amongst female and male children?

    Journal of Autism and Developmental Disorders 53:3627–3635.

    https://doi.org/10.1007/s10803-022-05552-9

    • PubMed
    • Google Scholar
45. 1. Sinha P
    2. Kjelgaard MM
    3. Gandhi TK
    4. Tsourides K
    5. Cardinaux AL
    6. Pantazis D
    7. Diamond SP
    8. Held RM

    (2014) Autism as a disorder of prediction

    PNAS 111:15220–15225.

    https://doi.org/10.1073/pnas.1416797111

    • PubMed
    • Google Scholar
46. 1. Sommer MA
    2. Wurtz RH

    (2002) A pathway in primate brain for internal monitoring of movements

    Science 296:1480–1482.

    https://doi.org/10.1126/science.1069590

    • PubMed
    • Google Scholar
47. 1. Sucksmith E
    2. Roth I
    3. Hoekstra RA

    (2011) Autistic traits below the clinical threshold: re-examining the broader autism phenotype in the 21st century

    Neuropsychology Review 21:360–389.

    https://doi.org/10.1007/s11065-011-9183-9

    • PubMed
    • Google Scholar
48. 1. Szinte M
    2. Cavanagh P

    (2011) Spatiotopic apparent motion reveals local variations in space constancy

    Journal of Vision 11:4.

    https://doi.org/10.1167/11.2.4

    • PubMed
    • Google Scholar
49. 1. Szinte M
    2. Cavanagh P

    (2012) Apparent motion from outside the visual field, retinotopic cortices may register extra-retinal positions

    PLOS ONE 7:e47386.

    https://doi.org/10.1371/journal.pone.0047386

    • PubMed
    • Google Scholar
50. 1. Tennant A
    2. Conaghan PG

    (2007) The Rasch measurement model in rheumatology: What is it and why use it? When should it be applied, and what should one look for in a Rasch paper?

    Arthritis Care & Research 57:1358–1362.

    https://doi.org/10.1002/art.23108

    • Google Scholar
51. 1. Thakkar KN
    2. Schall JD
    3. Heckers S
    4. Park S

    (2015a) Disrupted saccadic corollary discharge in schizophrenia

    The Journal of Neuroscience 35:9935–9945.

    https://doi.org/10.1523/JNEUROSCI.0473-15.2015

    • PubMed
    • Google Scholar
52. 1. Thakkar KN
    2. Schall JD
    3. Logan GD
    4. Park S

    (2015b) Response inhibition and response monitoring in a saccadic double-step task in schizophrenia

    Brain and Cognition 95:90–98.

    https://doi.org/10.1016/j.bandc.2015.01.007

    • PubMed
    • Google Scholar
53. 1. van Boxtel JJA
    2. Lu H

    (2013) A predictive coding perspective on autism spectrum disorders

    Frontiers in Psychology 4:19.

    https://doi.org/10.3389/fpsyg.2013.00019

    • PubMed
    • Google Scholar
54. 1. Van de Cruys S
    2. Evers K
    3. Van der Hallen R
    4. Van Eylen L
    5. Boets B
    6. de-Wit L
    7. Wagemans J

    (2014) Precise minds in uncertain worlds: predictive coding in autism

    Psychological Review 121:649–675.

    https://doi.org/10.1037/a0037665

    • PubMed
    • Google Scholar
55. 1. Van Eylen L
    2. Boets B
    3. Steyaert J
    4. Evers K
    5. Wagemans J
    6. Noens I

    (2011) Cognitive flexibility in autism spectrum disorder: Explaining the inconsistencies?

    Research in Autism Spectrum Disorders 5:1390–1401.

    https://doi.org/10.1016/j.rasd.2011.01.025

    • Google Scholar
56. 1. Wakabayashi A
    2. Baron-Cohen S
    3. Wheelwright S
    4. Tojo Y

    (2006) The Autism-spectrum quotient (AQ) in Japan: a cross-cultural comparison

    Journal of Autism and Developmental Disorders 36:263–270.

    https://doi.org/10.1007/s10803-005-0061-2

    • PubMed
    • Google Scholar
57. 1. Westheimer G

    (1954) Eye movement responses to a horizontally moving visual stimulus

    A.M.A. Archives of Ophthalmology 52:932–941.

    https://doi.org/10.1001/archopht.1954.00920050938013

    • PubMed
    • Google Scholar
58. 1. Wurtz RH

    (2018) Corollary discharge contributions to perceptual continuity across saccades

    Annual Review of Vision Science 4:215–237.

    https://doi.org/10.1146/annurev-vision-102016-061207

    • PubMed
    • Google Scholar
59. 1. Yao B
    2. Rolfs M
    3. McLaughlin C
    4. Isenstein EL
    5. Guillory SB
    6. Grosman H
    7. Kashy DA
    8. Foss-Feig JH
    9. Thakkar KN

    (2021) Oculomotor corollary discharge signaling is related to repetitive behavior in children with autism spectrum disorder

    Journal of Vision 21:9.

    https://doi.org/10.1167/jov.21.8.9

    • PubMed
    • Google Scholar
60. 1. Zener D

    (2019) Journey to diagnosis for women with autism

    Advances in Autism 5:2–13.

    https://doi.org/10.1108/AIA-10-2018-0041

    • Google Scholar
61. 1. Zimmermann E
    2. Morrone MC
    3. Binda P

    (2018) Perception during double-step saccades

    Scientific Reports 8:320.

    https://doi.org/10.1038/s41598-017-18554-w

    • PubMed
    • Google Scholar
62. 1. Zimmermann E
    2. Ghio M
    3. Pergola G
    4. Koch B
    5. Schwarz M
    6. Bellebaum C

    (2020) Separate and overlapping functional roles for efference copies in the human thalamus

    Neuropsychologia 147:107558.

    https://doi.org/10.1016/j.neuropsychologia.2020.107558

    • PubMed
    • Google Scholar
63. 1. Zimmermann E
    2. Lange J

    (2022) Saccade suppression of displacements, but not of contrast, depends on context

    Journal of Vision 22:10.

    https://doi.org/10.1167/jov.22.10.10

    • PubMed
    • Google Scholar

Author details

1. Antonella Pomè

   Institute for Experimental Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   antonella.pom@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1116-5399

2. Eckart Zimmermann

   Institute for Experimental Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

   Contribution

   Supervision, Validation, Writing – review and editing

   Competing interests

   No competing interests declared

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