Action Perception, Motor Imagery and Execution of Hand Movements in Autistic and Non-Autistic Adults

doi:10.21203/rs.3.rs-4883009/v1

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Action Perception, Motor Imagery and Execution of Hand Movements in Autistic and Non-Autistic Adults

https://doi.org/10.21203/rs.3.rs-4883009/v1

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Action perception, execution, and imagery share motor-cognitive processes. Given prevalent motor coordination difficulties in autism, the processes of action perception and imagery may also be altered. This study investigated whether autistic adults can engage in motor imagery by testing differences in executing, perceiving, and imagining hand movements between autistic and non-autistic adults. Twenty autistic individuals and twenty age- and IQ-matched controls completed execution, imagination, and perception tasks using a Fitts’ Law paradigm in an online session. For the execution and imagination tasks, participants performed or imagined making aiming movements between two targets. For the action perception task, participants indicated whether they could perform as accurately as the movements in presented videos. Target size and distance were manipulated into three difficulty levels and systematically varied across all tasks. Results showed a similar Fitts’ Law relationship for both groups, with significant positive correlations between movement times and difficulty level. Movement times were longest in the imagination task and shortest in the perception task for both groups. These findings suggest motor imagery processes are relatively intact in autistic adults, opening the possibility of using motor imagery as a therapy for motor coordination difficulties in autistic individuals.

Biological sciences/Neuroscience/Cognitive neuroscience

Biological sciences/Neuroscience

Biological sciences/Neuroscience/Sensory processing

Health sciences/Diseases/Psychiatric disorders/Autism spectrum disorders

Autism

Motor control

Perception-action coupling

Explicit motor imagery

Action perception

Autism Spectrum Condition (ASC) is a neurodevelopmental disorder characterized by difficulties in communication and social interaction, as well as repetitive behaviours¹. Alongside these challenges, approximately 80% of autistic individuals experience altered motor coordination, including difficulties with fine motor control, hand-eye coordination, balance, and gait^2–6. Persisting from infancy to adulthood, these motor challenges can have a negative impact on autistic individuals’ lives by reducing their ability to carry out daily tasks such as navigating cluttered environments, preparing food, getting dressed, or tying shoelaces⁴. Such motor challenges can also produce anxiety, fear of judgment, and frustration and reduce social opportunities through exclusion by others or self-exclusion⁴.

Motor coordination difficulties may also impact social ability through motor-cognitive mechanisms associated with action simulation. Simulation theory suggests that when an individual observes another person, the observer will internally simulate and mirror that other person’s actions by activating their own motor, cognitive, and emotional representations^7,8. Simulation provides a basic framework in which motor processes can influence various aspects of social interaction, including action perception (e.g., action understanding), imitation, theory of mind, empathy, and language^7,9–11. Altered motor coordination may impact upon simulation ability affecting these social processes because simulation processes are thought to rely on the same neural networks and perception-action representations that enable motor execution. Thus, if motor coordination challenges may associated with weak or disrupted perception-action codes, then simulation (and resulting action perception and imagery) that rely on those same codes will also be challenged.

To elucidate, action perception and execution are thought to be linked because these abilities rely on a shared set of motor-cognitive processes and integrated perception-action coding systems. According to Ideomotor Coding or Common Coding Theories^12–14 (see also⁸), perception and motor systems store and transfer abstract information in linked neural codes and networks. Specifically, it is suggested that the codes representing the perceptual consequences of an action (e.g., a letter appearing on the computer screen) are tightly linked or coupled to the motor codes representing the muscle contractions that would bring about an action (e.g., the flexion of a finger over a specific key). It is further proposed that the internal activation of one of the perception (or action) codes necessarily activates the linked action (or perception) codes. Research testing predictions of such ideomotor theories has generally provided evidence consistent with these predictions. For example, in support of the prediction that action perception activates motor codes, numerous studies have shown that observing another individual’s actions can influence the observer’s own movements, either enhancing imitation or interfering with planned actions^15–18. These facilitation or interference effects emerge because the perception-evoked response codes are compatible or incompatible with the goal actions, respectively. Hardwick et al.¹⁹ conducted a meta-analysis and identified a common brain network, including premotor, parietal, and somatosensory areas, that are activated during various motor-related tasks, such as action observation, movement execution, and motor imagery (imagining movement; described in greater detail below).

Because of the potential links between action execution, action perception, and social interactions, investigating possible differences between autistic and non-autistic individuals in these abilities is an active area of autism research. Historically, action perception and imitation being a primary focus of many studies (likely because of the more direct associations between these processes and social interactions). In regards to action perception, compared with non-autistic individuals, autistic participants generally demonstrate proficiency in simpler tasks, such as the detection of human motion or direction of motion^20–23. However, they tend to have more challenges with complex tasks that demand a deeper understanding of social cues, especially emotions^24–29. Altered imitation is also observed with autistic individuals showing a reduced ability to accurately replicate the kinematic style of observed actions^30–33.

A separate motor-cognitive ability that might be related to action execution and perception through perception-action coding is motor imagery (MI). MI is a specialized form of mental simulation that occurs when an individual imagines performing an action without physically executing that action³⁴. Like action perception, MI is thought to occur via simulation involving the sub-threshold activation of perception-action codes for a specific movement^35,36. Indeed, there is a substantial literature illustrating that MI involves activation of the motor system^19,37–39. MI can be performed in the visual or kinesthetic modality with the former involving the visualisation of an action and the latter consisting of imagining the sensations of the action⁴⁰. Jeannerod⁴⁰ introduced the concept of explicit and implicit imagery, differentiated by the degree of cognitive involvement during the task. Explicit imagery tasks require participants to consciously engage in imagining movements and subsequently report or rate their imagery experience, whereas implicit imagery tasks do not necessitate conscious engagement in the imagery process.

A recent systematic review found evidence that autistic individuals can use implicit MI, while research on explicit MI remains insufficient⁴. Implicit MI in autism has been examined in several studies using the hand rotation task where participants are presented with images of right and left hands at different angles and asked to indicate the laterality of the hand. Autistic children and adolescents displayed greater variability in biomechanically challenging positions compared to non-autistic individuals, suggesting that autistic individuals may be using different MI strategies or might be less likely to adopt MI in the task^4,41–43. Explicit imagery is often tested using questionnaires. The Kinesthetic and Visual Imagery Questionnaire (KVIQ) is a widely used tool in which participants are asked to perform simple movements, then to imagine themselves executing the same movements, and then to rate the clarity of their images (visual subscale) and intensity of their sensations (kinesthetic subscale) on a 5-point scale (1 – no image/no sensations to 5 – very clear image/very intensive sensations)^44,45. Only one study has used this approach with autistic individuals, finding no group differences between autistic and non-autistic adults on either subscale of the KVIQ⁴⁶. In contrast, other explicit imagery studies that have asked autistic adults to imagine themselves performing a spatial bimanual task⁴⁷ or recall a series of actions following imagining performing those actions⁴⁸ have shown explicit MI to be absent in the autistic group. However, it remains uncertain whether this lack of explicit MI reflects an inherent characteristic or a strategic choice by autistic participants to abstain from using imagery.

Another approach to assessing explicit MI is mental chronometry⁴⁹. This method involves comparisons of the movement times (MTs) of participants who have executed and imagined the same action task. The (in)consistencies of MTs on both tasks are used to infer the properties of MI – the closer the imagined MTs are to executed MTs is thought to indicate the accuracy of the simulation. It is posited that the time taken to actually execute versus imagine the same movement should be similar, otherwise an impairment of motor imagery may be indicated. Mental chronometry has yet to be explored in the context of autism, yet it is a useful approach to examining MI.

The aim of the current study was to develop a deeper understanding of the potentially linked abilities of action execution, perception, and imagination in autistic and non-autistic people using mental chronometry. The Fitts’ Law reciprocal aiming task is a well-established behavioural task for investigating the relationships among action execution, perception, and imagination. In this task, participants are asked to move a stylus or their finger between two targets as quickly as possible while maintaining accuracy⁵⁰. Both the width of the targets and the distance between them are manipulated to create varying levels of movement difficulty, referred to as the Index of Difficulty (ID). According to Fitts’ Law, as task difficulty increases, MT should increase to maintain accuracy, reflecting a speed-accuracy trade-off. Evidence supports the applicability of Fitts’ Law across execution, perception, and imagination tasks^38,51–55. For example, Wong et al.⁵⁴ found that relationships between MT and ID did not differ in non-autistic individuals across execution, imagination, and perception. Further, the consistency between MTs across action perception and imagination increased after the participants gained experience executing the movements. These findings are consistent with predictions based on common coding accounts of these abilities. However, imagined MTs were longer than both executed and perceived MTs, which did not differ. This may indicate that additional cognitive resources are required to imagine movements due to the mental effort of maintaining the active imagination⁵⁶.

In summary, it remains unclear whether autistic individuals can perform explicit MI, and how such MI processes relate to action execution and perception. Therefore, the present research project aimed to investigate mental chronometry for the first time in autistic individuals using a Fitts’ Law paradigm to examine whether there are differences in executing, perceiving, and imagining actions between autistic and non-autistic adults. Previous studies examining execution in the Fitts’ aiming tasks in autistic individuals have shown the existence of a relationship between task difficulty and executed MTs⁵⁷. First, we predicted that if simulation processes are present in autistic individuals, a positive relationship between MT and ID would emerge across the perception and imagination tasks. Second, if simulation processes are affected in the autistic group, they may show lower correlation coefficients between MT and ID in the perception and imagery tasks than the non-autistic group. Third, if simulation processes are affected in the autistic group, they may show a significantly greater difference in MTs between execution, perception, and imagination tasks compared to the non-autistic group.

This study was pre-registered using the open science framework: https://osf.io/26azs/.

Participants

A total of 20 autistic and 20 non-autistic participants of similar age, sex, handedness and full-scale intelligence quotient (IQ) were recruited through the laboratory database, the Autism@Manchester mailing list, local support groups and volunteer advertisements (see Table 1 for participant demographics). Because there were no data from previous studies examining the imagination of a Fitts’ Law task in autistic participants, sample size was based on an earlier study demonstrating Fitts’ Law relationships in action execution, perception, and imagination among 20 non-autistic participants⁵⁴. Specifically, a series of linear regressions between the group mean MTs and the IDs for the related combinations under the action execution, perception, and imagination tasks from Wong et al.⁵⁴ were used to create data for 20 participants across 100 simulated experiments in R (version 3.5.3). An ID x Task repeated measures ANOVA was run on each of these experiments and the number of times the p-value fell below 0.05 was calculated. Power was 100% for the main effects of Task and ID and 10% for the interaction.

All autistic participants had received a professional diagnosis of autism. Autistic participants also scored above the cut-off for autism (≥ 60) on the Social Responsiveness Scale (SRS-2)⁵⁸. None of the participants from either group reported any psychological or neurological disorders (e.g., Parkinson’s) or learning disabilities, and all reported normal or corrected-to-normal vision. The autistic group scored higher than the non-autistic group on both the SRS-2, the Adult Developmental Coordination Disorders/Dyspraxia Checklist (ADC)⁵⁹, but scored lower on both the Visual imagery and Kinesthetic imagery subscales on the Kinesthetic and Visual Imagery Questionnaire (KVIQ)⁴⁴ (Table 1). Participants gave informed consent via an online survey platform link (Qualtrics), and the study was approved by the University of Manchester Research Ethics Committee in accordance with the University’s Code of Good Research Conduct (Review Reference: 2021-11748-20016).

Table 1

Participant demographics
	Autistic (n = 20)	Non-autistic (n = 20)	Group comparison
Age	29.30 ± 7.66	28.95 ± 6.44	t (38) = 0.16, p = 0.88
Sex	15 Female	13 Female	X² (1, N = 40) = 0.48, p = 0.49
Handedness	17 right-handed	19 right-handed	X² (1, N = 40) = 0.28, p = 0.60
FSIQ-2	120.75 ± 13.74	113.00 ± 14.56	t (38) = 1.73, p = 0.09
KVIQ^*	VI: 14.90 ± 6.33 KI: 10.95 ± 3.83	VI: 19.45 ± 4.21 KI: 16.35 ± 3.70	t (38) = -2.68, p = 0.01 t (38) = -4.53, p < 0.001
SRS-2^*	110.95 ± 21.57	44.50 ± 15.65	t (38) = 11.15, p < 0.001
ADC^*	93.10 ± 14.82	65.20 ± 10.36	t (38) = 6.90, p < 0.001

FSIQ-2: Full scale IQ with 2 subtest scores (Matrix Reasoning and Vocabulary), KVIQ: Kinesthetic and Visual Imagery Questionnaire (VI: Visual Imagery, KI: Kinesthetic Imagery), SRS-2: Social Responsiveness Scale, Second Edition, ADC: Adult Developmental Coordination Disorders/Dyspraxia Checklist. Mean is shown with ± standard deviation. “*” indicates statistically significant group difference.

Apparatus, stimuli and task

The design of the tasks in the present study was based on previous studies^38,54,55 and adapted to the online data collection platform PsyToolkit^60,61. Online data collection was performed as the study was carried out during the COVID-19 pandemic when lockdown restrictions were still in place.

Apparatus and stimuli

Participants completed the experiment on their own computer or laptop in their chosen location, with the researcher communicating via Zoom. Each stimulus was presented on the screen of the computer and consisted of a pair of identical black strips that served as a target pair against a white background. A total of six stimulus images were used for the three tasks. The target pairs in these stimuli varied based on their widths—either small (WSmall) or large (WLarge)—and the distance between them, categorized as short (DShort), medium-short (DMedium1), medium-long (DMedium2), or long (DLong) (see Table 2 and Fig. 1). Across all tasks (execution, perception, and imagination), these six combinations of movement distance and target width were utilized to produce three relative index of difficulty (ID) values, namely 1, 2, and 3, as calculated by Fitts’ Law⁵⁰. Each ID had two combinations of target width and movement distance (e.g., ID1 consisted of combinations WSmall-DShort and WLarge-DMedium1). The stimulus images were adaptable to any screen size, and each image was designed with specific “width:distance” ratios: 1:2 for ID1, 1:4 for ID2, and 1:8 for ID3 (see Table 2 and Fig. 1). Because of differences in the screen sizes across participants, we focussed on consistency in the ratios of target width to movement distance instead of having consistent exact absolute values. Thus, the IDs of 1, 2, and 3 do not refer to the specific calculated ID for each individual, but indicate relative increases in ID for each participants’ own individual context. Nonetheless, we asked participants to physically measure and record the movement distances and target widths so we could confirm the ratios on each screen for each participant.

All participants began with the action execution task, then proceeded to the perception and imagination tasks. This order was chosen because experience with the execution task was shown to enhance action perception and imagination^38,54 and we wanted to provide the conditions in which participants had the best opportunity to demonstrate intact action imagination. The order of the latter two tasks (action perception and imagination) was counterbalanced among participants.

Table 2

Combinations of width and distance to produce the 3 index of difficulty (ID) levels.
IDs		ID 1	ID 2	ID 3
Combination 1	Width	Small	Small	Small
Combination 1	Distance	Short	Medium1	Medium2
Combination 2	Width	Large	Large	Large
Combination 2	Distance	Medium1	Medium2	Long
“Width:Distance” ratio		1:2	1:4	1:8

Tasks

• Action execution task

The execution task required participants to perform pointing movements between two target strips. Detailed instructions were displayed, followed by three practice trials. During this phase, participants had the opportunity to ask questions to the experimenter (who was available via Zoom) to ensure their understanding of the task requirements. For each test trial, one of the six combinations of target pairs appeared on the screen, accompanied by a “Ready” sign. Participants were instructed to perform ten rapid and accurate back-and-forth pointing movements between the two target strips, starting from the side corresponding to their dominant hand, using their dominant index finger. Thus, right-handed participants began movements from the right target moving to the left and then back from left to right. Participants were asked to press the spacebar to signal the beginning and end of the ten movements (see Fig. 2). Specifically, on each trial, participants pressed the space bar, then moved as quickly and accurately as possible between the right and left target on the screen 10 times, and then moved back to and pressed the space bar. Emphasis was placed on accuracy, with instructions to land precisely on the middle section of the target while moving as quickly as possible. This sequence was performed three times per target pair, for a total of 18 trials (6 trials per ID level). Target pairs were presented in a random order.

In addition to the main experimental trials in which reciprocal movements were executed between the 2 targets, participants were also asked to perform 3 control trials in which they only pressed the space bar, then touched one target, and immediately returned to and pressed the space bar (i.e., without executing 10 movements between the targets). These control trials were completed for each of the target width conditions and were used to calculate the MTs for the reciprocal movements. Specifically, to compute the average MT for movements between targets alone, the average time to move from the space bar to the screen and back to the space bar (indexed via control trials) was first subtracted from the total movement time during the experimental trials (which includes both movements to and from the space bar to the screen, and the 10 movements between the targets). This approach helped to isolate movement time for moving between the targets which was then divided by ten to get an average movement time for each individual movement segment between each target.

• Action imagination task

The imagination task was structured similar to the execution task. However, in this task, participants were instructed to rest their hand on the table and only to position their index finger over the spacebar, without performing any pointing movements. They were asked to imagine themselves making ten accurate pointing movements between the displayed target pairs, as in the previous execution task but without including the movements from the space bar to the screen and back. To denote the beginning and end of these imagined movements, participants pressed the space bar once to indicate the start of their imagined movements, and then a second time to indicate the end of their imagined ten movements (see Fig. 2A). After being presented with the instructions, participants undertook three practice trials during which they were asked to count aloud the number of their imagined movements to confirm their understanding. For the actual 18 test trials (6 trials per ID), participants could choose to count the movements either out loud or silently. The duration between the initial and final space bar presses was recorded. Similar to the execution task, participants also completed 3 control trials, during which they imagined touching one target and pressed the spacebar at the start and end of the imagination. To compute the average MT for imagining between targets alone, the average time of the control trials was first subtracted from the total imagination time during the experimental trials. This adjusted time was then divided by 10 to calculate the average MT for an individual imagined movement between the targets, using the same method as for the execution task.

• Action perception task

For the action perception task, participants were asked to watch a series of videos of a hand with an extended index finger (corresponding to their dominant hand) moving back and forth ten times between two targets. After viewing each video, they were prompted to judge whether they could replicate the speed and accuracy of the hand in the video. They could respond by selecting one of two options shown on the screen following the video: “Yes, I can do it” or “No, I cannot do it” (see Fig. 2B). The video was composed of the alternation of a pair of images of a hand presented with the index finger on the left or on the right target in a first-person view. On a given trial, the images of the hand alternated to make it appear as though the hand and finger moved with an apparent MT (the time between each image) of 60, 120, 180, 240, 300, 360, 420, 480, 540, or 600 ms. Participants completed 120 trials of the perception task (40 trials per ID and presented in both increasing and decreasing orders of MT). The lowest apparent MT at which participants responded with “Yes, I can do it” across both increasing and decreasing sequences was taken as the measure of average MT for perceived movements.

Procedure

Participants were asked to position themselves in a quiet room and in front of a computer. They received a Zoom meeting invitation from the experimenter with instructions to enable video for the entirety of the session. All participants agreed and joined the Zoom meeting with screen sharing during the whole experiment session. The primary purpose of screen sharing was to monitor progress and answer questions about the tasks, rather than to review results.

At the start of the Zoom meeting, the experimenter provided an overview of the study and subsequently shared a link for participants to give their electronic informed consent. Additionally, the experimenter checked with participants whether they preferred that the experimenter turned their camera on during the questionnaires and experiment sessions. If not, the experimenter turned their video off to minimize distractions while still keeping track of participant progress and being available for questions. Once consent was obtained, demographic details such as age, sex, and educational background were collected via Qualtrics (online platform). Then, hand dominance was ascertained using the Edinburgh Laterality Inventory⁶². To familiarize participants with the study’s context, a brief video explaining the concept of motor imagery was presented (see from: https://osf.io/vky9d). Following the video, participants had the opportunity to ask the researcher questions, and then they answered questions confirming their comprehension of MI and indicating any prior experience with MI (analysis of the answers to these questions are reported in a separate paper; see⁶³). The study utilized an online version of the KVIQ⁴⁴. This online version featured pre-recorded demonstrations of movement tasks, replacing the typical live demonstrations seen in standard in-person KVIQ administration. Participants were guided through the first KVIQ item by the experimenter to ensure that they understood the procedure, then completed the remaining items without assistance (unless requested).

After completing the KVIQ, participants were asked to complete two subscales (Vocabulary and Matrix Reasoning) of the Wechsler Abbreviated Scale of Intelligence (WASI-II)⁶⁴, as well as the SRS-2 and ADC questionnaires. These questionnaires were collected through a Qualtrics survey link. Following the questionnaires, participants were sent another link (via Zoom) to the Fitts’ Law experimental tasks. In these tasks, the participants completed the action execution, perception, and imagination tasks with their dominant hand. At the end of these tasks, participants were instructed to measure and report the exact widths and distances of the targets on their screens, as well as their screen dimensions. Although stimuli were automatically adjusted according to screen size, the measurements were also collected to ensure accurate ID ratio calculations for each individual. The entire session took between 3.5 to 4 hours to complete, and participants had the option to complete it within a single session (n = 33) or to divide it over two consecutive days (n = 7).

Analysis

Data were analysed using R (version 4.3.0). The normality distribution of variables was tested using Shapiro-Wilk Test (p ≥ 0.05). The dependent measure was MT for all three tasks – the average length of time it takes for the real (execution task), imagined (imagination task), and observed (perception task) hand to move between target pairs.

Analyses of the three hypotheses presented in the introduction were performed at the group level, and a separate individual-level analysis was carried out as a secondary examination of hypothesis 1. To address the first hypothesis, we sought to determine if the MTs were significantly positively correlated with the three ID conditions for each group for each Fitts’ Law task (action execution, imagination, and perception). Pearson’s correlation coefficients were calculated separately for each task and each group. For the second hypothesis, we aimed to determine if the two groups showed different performance levels for each task. The correlation coefficients were then transformed into Z scores, and t-tests were subsequently used to compare these Z scores between the two groups for each task. Linear regression analyses were then performed on MTs for each task—execution, imagination, and perception—within each participant group. The components of the regressions between the three IDs and MTs (slope and y-intercept) were compared between the execution, imagination, and perception tasks as in Wong et al.⁵⁴. Y-intercepts were only compared when slopes were not statistically different. For the third hypothesis, a mixed ANOVA was conducted to investigate any main effects or interaction effects across Group (autistic and non-autistic group), Task (execution, imagination, perception), and ID (1, 2, 3) in relation to MT. In the mixed ANOVA, Group was treated as a between-subjects variable whereas Task and ID were treated as a within-subjects variable. In an additional analysis, the SRS, ADC, KVIQ-KI and KVIQ-VI were added as covariates to four separate ANOVAs (Task x ID) to test whether these measures affected any potential Group differences in Task performance. Post hoc testing of any significant effects involved pairwise comparisons with Bonferroni correction to control for multiple comparisons, specifying the direction and magnitude of the effects for each level of Task and ID within each Group.

Assessment of Fitts’ Law in three tasks

To determine if the MTs conformed to Fitts’ Law, Pearson’s correlation coefficients were separately computed for the execution, perception, and imagination tasks for the autistic and non-autistic groups. Consistent with previous studies ⁵⁴, MTs were highly and significantly positively correlated with the three ID conditions in all tasks for both groups (see Table 3).

Table 3

The relationship between MT (in ms) and ID in all tasks for the autistic and non-autistic groups. MT = movement time, ID = Index of Difficulty.
Task	Group
Task	Autistic group	Non-autistic group
Execution	MT = 295 + 39.5(ID),	MT = 293.3 + 47.3(ID),
Execution	R = 0.85, p < 0.01	R = 0.88, p < 0.01
Perception	MT = 156 + 55.5(ID),	MT = 181 + 33(ID),
Perception	R = 0.94, p < 0.001	R = 0.90, p < 0.05
Imagination	MT = 387 + 28.8(ID),	MT = 334 + 35.9(ID),
Imagination	R = 0.87, p < 0.05	R = 0.85, p < 0.05

Given the replication of Fitts’ Law across all tasks, linear regression analyses were conducted to compare the correlation coefficients between the autistic and non-autistic groups for each task individually. The correlation coefficient scores were converted to Z scores first and then compared between groups for each condition with unpaired t-tests. The analyses revealed there was no significant difference in the correlation scores between the two groups in all tasks (Execution, t (6) = 0.098, p = 0.93; Perception, t (6) = 0.29, p = 0.78; Imagination, t (6) = 0.06, p = 0.95). Additional analyses were conducted to compare the components of the regression lines to determine if and how the relationship between MT and ID differed across the tasks, for each group. In these analyses, the components of the regression lines of the group performance on the different tasks were compared to one another based on ratios of the residuals for each line using separate ANOVAs. The results of these analyses for the group level data revealed that the slopes of the regression lines for the different tasks did not significantly differ in the non-autistic group, F (2, 12) = 0.5, p = 0.62, nor in the autistic group, F (2, 12) = 1.8, p = 0.21. The y-intercepts of the lines, however, were statistically different in both the non-autistic group, F (2, 14) = 107.8, p < 0.001, and the autistic group, F (2, 14) = 105.2, p < 0.001. Thus, the nature of the differences in the elevations of the lines were elucidated in both groups: MTs were longest in the imagination task, intermediate in the execution task, and shortest in the perception task (see Fig. 3).

Group comparisons across execution, perception, and imagination tasks

To examine differences between the autistic and non-autistic groups across execution, perception, and imagination, a mixed ANOVA was performed on MTs with a between-subjects factor of Group and within-subjects factors of Task and ID. There was a significant main effect for Task, F (2, 76) = 58.3, p < 0.001, partial η² = 0.43; and ID, F (2, 76) = 9.81, p < 0.001, partial η² = 0.82, but no significant main effect of Group, F (1, 38) = 0.98, p = 0.32, partial η² < 0.001. Post hoc analysis of the main effect of Task (using ANOVA) revealed that MTs in the imagination task (425 ± 147.8 ms) were longer than those in the execution task (381 ± 127.4 ms), while MTs for the perception task (257 ± 93.4 ms) were shorter than those for the other two tasks. Consistent with Fitts’ Law, the main effect for ID (using ANOVA) revealed that MTs for ID 1 (316 ± 135.3 ms) were significantly shorter than MTs for ID 2 (352 ± 143.1 ms), which in turn were significantly shorter than MTs for ID 3 (396 ± 141.7 ms). There were no significant interactions between Group and Task (Greenhouse-Geisser correction were used), F (1.76, 67.07) = 0.66, p = 0.50, partial η² = 0.02, Group and ID, F (1.75, 67.57) = 0.20, p = 0.79, partial η² < 0.001, or Task and ID, F (3.24, 122.96) = 1.57, p = 0.20, partial η² = 0.04. There was, however, a significant three-way interaction among Group, Task, and ID, F (3.24, 122.96) = 2.79, p = 0.04, partial η² = 0.07. Subsequent post-hoc analysis using two-way repeated measures (Task x ID) ANOVA separately for two groups revealed a Task x ID interaction for the autistic group only (autistic group: F (2.90, 55.23) = 3.84, p = 0.02; non-autistic group: F (2.76, 52.48) = 0.93, p = 0.43) (see Fig. 3). Specifically, a notable difference was found between the perception and imagination tasks when the ID increased from 1 to 2 (see Fig. 4).

The covariance effects on Fitts’ Law from questionnaires

A secondary analysis was conducted to assess the impact of scores from the KVIQ, SRS-2, and ADC on performance in tasks adhering to Fitts’ Law. Separate Analysis of Covariance (ANCOVAs) were employed for this purpose. The results revealed that the inclusion of the KVIQ sub-scores as covariates in the ANOVA did not lead to significant changes in main and interaction effects (see Table 4). However, the earlier observed interaction between Group, Task, and ID was no longer significant after integrating the centred SRS-2 scores and the centred ADC scores as covariates (see Table 4). These analyses suggest that SRS-2 and ADC scores partly explained the interaction effect, indicating that Fitts’ Law performance might be influenced by individuals’ autistic and motor characteristics.

Table 4

ANCOVA results. Changed significant levels indicated in bold.
Questionnaires	ANCOVA results
KVIQ (VI)	Main effect: Task	F (1.77, 65.67) = 28.76, p < 0.001
	Main effect: ID	F (1.77, 65.67) = 175.00, p < 0.001
	Interaction: Group × Task × ID	F (3.25, 120.13) = 2.70, p = 0.04
KVIQ (KI)	Main effect: Task	F (1.75, 64.57) = 29.90, p < 0.001
	Main effect: ID	F (1.75, 64.57) = 175.00, p < 0.001
	Interaction: Group × Task × ID	F (3.24, 119.80) = 2.93, p = 0.03
SRS-2	Main effect: Task	F (1.76, 25.26) = 28.37, p < 0.001
	Main effect: ID	F (1.75, 64.43) = 169.44, p < 0.001
	Interaction: Group × Task × ID	F (3.19, 118.02) = 2.03, p = 0.11
ADC	Main effect: Task	F (1.73, 64.16) = 29.25, p < 0.001
	Main effect: ID	F (1.75, 64.63) = 169.05, p < 0.001
	Interaction: Group × Task × ID	F (3.23, 119.46) = 0.95, p = 0.43

KVIQ: Kinesthetic and Visual Imagery Questionnaire (VI: Visual Imagery, KI: Kinesthetic Imagery), SRS-2: Social Responsiveness Scale, Second Edition, ADC: Adult Developmental Coordination Disorders/Dyspraxia Checklist.

Individual analysis of execution, perception, and imagination

Consistent with the Group level analysis of the slopes and y-intercepts of the regression lines, we tested whether the group level pattern of effects was also present on the individual level. Results revealed while there was some variation between individuals, the overarching trend was that the processes underlying action execution, perception, and imagination are largely consistent among participants, regardless of their group status. Full details are reported in the Supplementary materials.

The goal of this work was to investigate whether autistic adults can engage in explicit MI, and how MI processes relate to action execution and perception both in autistic and non-autistic individuals. This study is the first to employ the Fitts’ Law paradigm across execution, imagination, and perception tasks and to test explicit MI with mental chronometry in autistic individuals. Fitts’ Law relationships were observed in both groups across all three tasks. Subsequent mixed ANOVA analysis showed longer MTs for the imagination task and the most difficult level (highest ID) and shorter MTs for the perception task and easiest level (lowest ID) across both groups. A three-way interaction among Group, Task, and Difficulty Level (ID) was observed, indicating a steeper slope as the index of difficulty increased from easy to medium level for the perceptual compared to imagination task within the autistic group. In sum, these data provide evidence that action imagination is relatively intact in autistic individuals.

The current study built upon ⁵⁴ by comparing executed, perceived, and imagined MTs in a Fitts’ Law paradigm and extending it to include both autistic and non-autistic groups. Consistent with Wong et al.’s⁵⁴ findings, our study demonstrated a similar Fitts’ Law relationship across all three tasks for both groups, with MTs increasing as the ID levels increased. Despite observing variability among participants, our data showed similar patterns of performance at the individual level across both groups. Importantly, this replication supports the use of the Fitts’ Law paradigm in an online environment. These results are consistent with the common coding hypothesis, which posits that action execution, perception, and imagination are related through shared, abstract neural codes, that facilitate a bidirectional response that enables individuals to interact with their environment appropriately^12,14.

The ANOVA analysis revealed a significant main effect for Task. The imagination task resulted in the longest MTs for both groups, corroborating Wong et al.’s⁵⁴ findings. In line with Glover et al.’s⁵⁶ proposition, the extended MTs in imagination tasks may be attributed to the additional cognitive load required to maintain imagined movements, which involves working memory demands^19,65. This conclusion is further supported by neural activation patterns, where the dorsolateral prefrontal cortex, a region implicated in movement inhibition, is recruited during motor imagination but not during action execution or perception. This activation may underlie the increased effort to suppress actual movement during imagination^19,66.

A significant three-way interaction between Group, Task, and ID was also observed; an interaction driven by the Task and ID effects within the autistic group. The autistic group displayed a more pronounced increase in movement duration between ID 1 and 2 for perception tasks relative to execution tasks. This discrepancy indicates that, in comparison to referencing actual movements, the autistic group may utilize alternative strategies or cognitive processes for completing perception tasks, echoing the suggestions of previous studies^9,41,43,47. Nonetheless, caution should be exercised when interpreting these results due to their unpredicted nature and the potential influence of group characteristics, such as SRS-2 and ADC scores. These measures, which assess social ability and motor coordination, may partially explain the observed group differences. When these scores were accounted for as covariates, the interaction effects in Fitts’ task performance disappeared, indicating that autistic and motor characteristics might influence performance in perception and imagination tasks.

The overarching findings of similarities in performance between the autistic and non-autistic group for the perception and execution tasks is consistent with literature indicating that autistic individuals have comparable capabilities in movement speed perception tasks^21,67,68 and Fitts’ Law relationship for simple hand aiming movements⁵⁷. The current study utilized mental chronometry to assess explicit MI, and despite lower ADC scores, individuals with autism exhibited proficiency in the imagination task. Previous research exploring explicit MI in autism, using self-reported questionnaires or experimental tasks, has yielded mixed results^9,46–48. The current study contributes new findings in this domain by employing both questionnaires and behavioural measures. Earlier investigations into explicit MI in autistic adults suggested a discrepancy in their ability to use MI. For instance, a study using a spatial bimanual task, where participants drew a line with their right hand while imagining drawing a circle with their left hand, found that autistic individuals were unaffected by the imagination condition during dual tasks, unlike the non-autistic individuals⁴⁷. Another study reported that autistic individuals did not benefit from imagining a sequence in a sequence recall task compared to a matched group⁴⁸. However, the current study observed a similar Fitts’ Law relationship in both groups for the imagination task. The disparity could be attributed to the simplicity of our task design, requiring participants to focus solely on the MI, without engaging in higher cognitive processes such as remembering sequences or performing a concurrent task. Alternatively, a limitation of the previous studies (see^47,48) is that it is unclear whether autistic individuals were actually using MI and they did not need to use it to complete the tasks. In contrast, MI is more central and “less optional” in chronometry tasks, as participants are asked to report the duration of the imagined movement. This study may have encouraged the use of MI in the Fitts’ Law task. Another potential reason is the exclusive use of MT as a measure in our study, which had reduced accuracy due to the online nature of conducting the experiment.

Moreover, in contrast to the Fitts’ tasks performance, significant group differences were found in both the VI and KI subscales of the KVIQ, with the autistic group reporting less vivid imagery. This result contrasts with a study using the same questionnaire but finding no significant differences between autistic and non-autistic groups⁴⁶. One potential explanation for this discrepancy may relate to the version of the KVIQ used in the studies. The current study utilized a video-recorded short version (5-item) of the KVIQ, whereas Gowen et al.⁴⁶ employed an in-person full version. In the in-person setting, having participants sit side by side with the experimenter may have been more conducive to facilitating MI, as opposed to the online version where movements were demonstrated in a third-person perspective. Some participants in the current study reported that they could only imagine the actor in the video performing the movements, rather than visualizing themselves executing the tasks⁶³. This difference in perspective could significantly impact how participants engage with and perform in MI tasks.

Comparing the imagination results from the Fitts’ task and KVIQ questionnaire, the different results may also be due to the different focus: the Fitts’ task measures relatively objective MT, while the KVIQ assesses subjective experiences on a 5-point scale, probing the vividness and intensity of imagined movements. These differing methodologies assess different aspects of MI, with the self-reported KVIQ focusing more on how participants generate motor images, and the Fitts’ task emphasizing the ability to maintain motor images⁶⁹. Meanwhile, participants may utilize alternative strategies during the imagination task, such as moving their eyes between targets (such eye movements were anecdotally observed by the researcher, but not recorded during the experiment). However, participants may be less able to employ such eye movement strategies in the KVIQ, resulting in lower reported vividness or intensity. Further studies could explore if autistic individuals can demonstrate MI when asked to not move their eyes (note that restricting eye movements impacted imagined MTs in non-autistic individuals⁷⁰). Additionally, autistic individuals might express their internal experiences less proficiently or exhibit increased caution when responding to subjective scales^71,72. A further reason for the divergent findings may be the different movements involved in each task. The KVIQ includes a variety of movements, such as lifting an arm, bending the upper body, or lifting a leg, whereas the imagination task in our study involved only the dominant hand in an aiming task. These mixed results suggest that while autistic individuals may be adept at simple movements, they may struggle with more complex tasks and potentially employ different strategies compared to non-autistic individuals^4,63. Finally, it is important to acknowledge individual differences in MI ability; when KVIQ scores were considered as a covariate, they did not significantly alter performance on Fitts’ task. This indicates that MI ability may consistently affect individuals’ performance regardless of the task type.

Our study’s findings suggest that individuals with autism are capable of effectively utilizing MI. This discovery opens the door for the application of MI as a therapeutic intervention as demonstrated and used in Developmental Coordination Disorder⁷³, Parkinson’s disease^74,75, and post stroke⁷⁶, which could potentially enhance the performance of daily life tasks in autistic individuals. Such interventions could be tailored to leverage the strengths in MI that this group exhibits, thus offering practical benefits in skills training and rehabilitation.

Although our study successfully replicated the findings of Fitts’ tasks in non-autistic groups, conducting the experiment in an online setting introduced certain limitations. The lack of kinematic data collection of the actual hand movements during execution and imagination meant that our analysis was confined to movement time, excluding other crucial aspects of motor performance. Previous work has explored additional non-goal hand movements (motor overflow) during imagination and found that these movements increased as the amplitude of the imagined movements increased^54,70. Additionally, participants needed to have access to internet and a certain comfort level with computers and one-on-one interactions with the experimenter, and as such the autistic individuals who chose to volunteer for the study were relatively young and verbal. Future research in a controlled laboratory environment would enable more comprehensive data collection, including metrics such as movement accuracy and smoothness, and motor overflow. Introducing more complex tasks in future studies could also provide deeper insights into whether the observed performance equivalence between autistic and non-autistic individuals persists under more demanding conditions.

Due to the constraints of our study’s settings and procedures, we were unable to thoroughly investigate the specific strategies employed by individuals during the tasks. The exploration of these personal MI strategies and their impact on task performance remains a significant area for future research. It is particularly important to investigate the alternative strategies that autistic individuals might use during MI tasks (e.g., counting, moving eyes, small finger movements, etc.)⁶³. Understanding these strategies could be instrumental in developing MI-based interventions tailored to improve motor functions in individuals with autism, potentially leading to innovative therapeutic approaches that leverage the unique cognitive profiles of this population.

In conclusion, the current study has demonstrated that individuals with autism can complete Fitts’ Law tasks and effectively engage in explicit MI. These findings open potential avenues for MI-based therapeutic interventions to improve their daily life. Further research should delve deeper into explicit MI strategies used in autism, particularly in more controlled experimental settings, to validate these findings and refine the intervention.

Author contributions

Y.B., M.B., A.K., E.P., T.W., and E.G. conceived and designed the experiments. Y.B. conducted data collection, analysed the data, and led the original manuscript writing. M.B. and A.K. assisted in data collection preparation, data analysis and provided feedback on the original manuscript. E.G., T.W., and E.P. supervised the data collection and data analysis, and provided critical review, commentary, and revision of the manuscript. All authors discussed the results and contributed to the final manuscript.

Data availability statement

The data that support the findings of this study are available from the corresponding author, YB, upon reasonable request.

Additional Information (including a Competing Interests Statement)

This research was supported by the University of Manchester-University of Toronto Joint Research Fund. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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No competing interests reported.

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Action Perception, Motor Imagery and Execution of Hand Movements in Autistic and Non-Autistic Adults

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Abstract

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Introduction

Materials and methods

Participants

Apparatus, stimuli and task

Apparatus and stimuli

Tasks

• Action execution task

• Action imagination task

• Action perception task

Procedure

Analysis

Results

Assessment of Fitts’ Law in three tasks

Group comparisons across execution, perception, and imagination tasks

The covariance effects on Fitts’ Law from questionnaires

Individual analysis of execution, perception, and imagination

Discussion

Declarations

Author contributions

Data availability statement

Additional Information (including a Competing Interests Statement)

References

Additional Declarations

Supplementary Files

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