Reciprocal Facilitation Between Mental And Visuomotor Rotations

doi:10.21203/rs.3.rs-1821484/v1

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Reciprocal Facilitation Between Mental And Visuomotor Rotations

https://doi.org/10.21203/rs.3.rs-1821484/v1

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published 16 Jan, 2023

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Humans exhibit remarkably complex cognitive abilities and adaptive behavior in daily life. Cognitive operation in the "mental workspace," such as mentally rotating an additional piece of luggage to fit into fixed trunk space, helps us maintain and manipulate information moment-to-moment basis. Skill acquisition in the "sensorimotor workspace," such as learning a new mapping between the magnitude of new vehicle movement and wheel turn, allows us to adjust our behavior to changing environmental or internal demands to maintain appropriate motor performance. While this cognitive and sensorimotor synergy is at the root of adaptive behavior in the real world, their interplay has been understudied due to a divide-and-conquer approach. We evaluated whether a separate domain-specific or common domain-general operation drives mental and sensorimotor rotational transformations. We observed that participants performed visual mental rotation faster after the visuomotor rotation training, and their learning rate for visuomotor adaptation improved after their mental rotation training. Such bidirectional transfer achieved through the two widely different tasks demonstrated remarkable reciprocal plasticity and supported a common transformation mechanism between two intertwined workspaces. It also urges the necessity of an explicitly integrated approach to enhance our understanding of the dynamic interdependence between cognitive and sensorimotor mechanisms.

Historically, the information-processing theory established a one-way information flow of acquiring perceptual input, processing the information with high-level cognition, and then planning and executing appropriate actions^1-4. Consequently, each domain has been primarily examined in isolation. On one hand, research on "mental workspace", including mental imagery, visuospatial ability, and working memory, has focused mainly on the ability to store, maintain, and manipulate information while neglecting issues of sensorimotor control, the means by which perceptual and cognitive ability is behaviorally expressed⁵. On the other hand, research on sensorimotor behavior, including skill acquisition, has focused on the neurophysiological, anatomical, and other implicit mechanisms that control simple motor outputs with little consideration for the roles of higher-order cognitive functions⁶. These focused approaches on well-defined isolated domains affords scientific rigor and has contributed to the development of coherent bodies of work in each subsystem replete with successful explanatory theories and a rich collection of paradigms, tasks, and analytic techniques. Yet, it can lose sight of the co-dependence between cognition and action, which may be fundamental to realistic adaptive behaviors in the complex real world^7-12.

Recent evidence has consistently shown the prevalent interplay between cognition and action^7,8,13-23. For instance, a series of recent studies have discovered that cognitive factors such as attention play a crucial role in developing and maintaining visuomotor skills within more complex environments^10,24-26. Guo and Song²⁷ also discovered that as actions became more fluent (e.g., as grasping errors decreased), perceptual discrimination performance also improved. Importantly, they observed that grasping training prior to discrimination enhanced subsequent perceptual sensitivity, supporting the notion of a reciprocal relation between perception and action. Extensive research also showed that prior mental practice of a motor task in the absence of overt physical activity enhances the movement performance (for reviews^28,29).

Furthermore, action-specific perception theory states that humans’ ability to act influences how they perceive the environment^30,31. Clearly, these findings could not be explained by the conventional "modular" sequential approach. Instead, it highlights the necessity of an explicitly integrated approach to determine how cognitive operations link with sensorimotor learning processes. Thus, in the present study, using a novel behavioral training-transfer paradigm to tap into cognitive and visuomotor domains simultaneously, we investigated whether separate domain-specific or common domain-general processing governs the transformations of rotation required in visual cognition (visual imagery) and sensorimotor (visuomotor rotation adaptation) domains.

Until recently, visuomotor rotation (VMR) adaptation has been exclusively formulated as a form of error-driven implicit sensorimotor learning. For instance, Mazzoni and Krakauer³² demonstrated that explicit cognitive strategies were unconsciously overridden during the adaptation process. However, the role of explicit cognitive strategies remained a topic of debate. Taylor and Ivry³³ demonstrated that participants could flexibly combine such instructed strategies with implicit adaptive processes to optimize performance with extensive practice. Taylor et al.³⁴ also dissociated explicit and implicit contributions to the visuomotor adaptation task by asking participants to report their aiming location directly. While the specific computations underlying cognitive strategies in sensorimotor learning are poorly understood, mental rotation has been considered a possible strategic transformation involved in VMR adaptation.

Mental rotation is the ability to transform a perceptual representation of an object to accurately predict how the object would look from a different angle, such as when people try to imagine how the living room would look with the furniture rearranged. The time to make judgments about a rotated object increases in a near-linear fashion with the amount of rotation required to bring the object to align with a comparison object or with a learned spatial template. Underlying operations for mental rotation include the active maintenance of a visual configuration, its transformation, and subsequent comparison to a target image^35-41. While mental rotation has been extensively studied within the visual imagery domain, a series of studies have shown that mental rotation can be of more general application to motor performance under transformed spatial mappings such as VMR. For example, prior studies showed that mental rotation could be facilitated by simulating manual rotation covertly, and this covert stimulation activates the motor cortex^42-44. Neurophysiological evidence showed that VMR activates the corresponding rotation of neuronal population vectors in the motor cortex^45,46. Previous studies also showed that processing rates of VMR and visual mental rotation, which were revealed by the linear relation between rotated angles and response times, were positively correlated^45-49. In addition, McDougle and Taylor⁵⁰ suggested that the complexity of a sensorimotor task influences whether to employ a rotation computation for VMR: when a large number of reach target locations (e.g., 12) is used, participants rely on a time-consuming parametric rotation computation, whereas when a small number of locations (e.g., 2) is used, they rely on a discrete caching of stimulus-response (i.e., a look-up table). These studies demonstrating a correlated processing rate between the two rotation tasks or parametric changes in VMR mimicking mental rotation suggest that similar rather than completely independent processing constraints underlie visuomotor and visual mental rotations^45,47-49.

However, this correlational or similarity-based evidence^46-50 has left open the question whether perceptual and visuomotor domains merely represent rotation in a similar fashion, or whether they are tightly coupled sharing a common mechanism. The domain-specific hypothesis postulates that the rotation transformation involves different neural systems depending on the perceptual or motor task. However, each dedicated brain area processes rotation itself in a similar fashion. For instance, while neurophysiological evidence showed that visuomotor rotation leads to the corresponding rotation of neuronal population vectors in the primary motor cortex (M1)^45,46, whether M1 is involved in visual imagery rotation is still a subject of ongoing debate^38,51. Alternatively, the domain-general hypothesis postulates that mental rotation, be it perceptual or visuomotor, involves a certain brain area that is jointly accessed by both the perceptual and the motor systems, and therefore the similar processing constraints observed would be due to processing features of that common brain area.

In the present study, we reasoned that direct behavioral evidence of the involvement of a common rotation operation can be obtained by examining whether performance improvement in one process can lead to changes in the other. In other words, the domain-general hypothesis predicts that 1) training in visuomotor rotation will facilitate mental rotation but other motor tasks without rotation will not (Experiment 1); reversely, 2) mental rotation training will enhance visuomotor rotation but other cognitive tasks without a rotation judgment will not (Experiment 2). Indeed, we observed a bidirectional transfer between the two widely different tasks, which supports the hypothesis that the operation of rotation across cognitive and sensorimotor domains is driven by a common domain-general rather than domain-specific transformation.

Experiment 1: Visuomotor Training Improves Mental Rotation

In Experiment 1, we developed a novel training-transfer paradigm (Fig. 1a) by combining mental rotation (Fig. 1b) and visuomotor training tasks (Fig. 1c) to evaluate whether a separate domain-specific or common domain-general rotational operation drives mental and visuomotor rotation. The domain-specific hypothesis predicts no transfer across domains, whereas the domain-general hypothesis predicts that VMR training will facilitate the efficiency of mental rotation, but other motor tasks without rotation operations will not.

To evaluates these hypotheses, all participants performed four sessions: a pre-test of mental rotation, visuomotor training, a post-test of mental rotation, and visuomotor washout (Fig. 1a). The two mental rotation sessions were identical for all participants, requiring participants to determine whether a tilted asymmetric letter (e.g., R) was a normal or a mirrored letter (Fig. 1b). Therefore, it enabled us to evaluate how mental rotation performance was affected by the different visuomotor training tasks (VMR, control reach and control SRT tasks) (Fig. 1c). The visuomotor washout session was designed to confirm that the visuomotor training effects were maintained during the post-test of mental rotation.

During the visuomotor training session, participants (n = 19) were randomly assigned to the VMR (red border) group or the other two control groups (Fig. 1c): a reach task (blue border) or a sequential reaction time task (SRT; cyan border). These two control groups enabled us to estimate test-retest improvement in mental rotation and parse out contributions led by movements without rotational operation. In the VMR task, participants moved the cursor (small black dot) from the starting base (open circle) toward the target (big black dot). Each target direction was located in 45° increments from 0° (12 o'clock) to 315°, for a total of eight possible target locations. There were two types of trials. In rotation trials, the cursor direction (solid line) was rotated 45° clockwise (CW) from the hand trajectory (dotted line). This perturbation creates movement errors, forcing the brain to learn or update new sensory-motor relationships to reestablish appropriate motor control. In no-rotation trials, the cursor direction (solid line) normally followed the hand trajectory (dotted line). Participants performed one baseline block (80 no-rotation trials) measuring inherent bias in the reaching movement toward each target and four training blocks (80 rotation trials/block). In the control reach task, participants moved the cursor from the starting base toward the target and the cursor direction normally followed the hand trajectory. In the control SRT task, participants pressed a button to indicate the corresponding target location and learned a repeating sequence of button-press (see Methods: Exp.1 Tasks and Procedures for details).

As shown in Fig. 2, we evaluated the motor performance of three groups during the visuomotor training session. In the visuomotor rotation (VMR) group (Fig. 2a: red), reach error was reduced across trial blocks in the visuomotor training (F(5.52, 99.43) = 40.72, p < .001, η² = .55), suggesting enhancement in visuomotor rotation. As a control, the control reach task without the rotated visual feedback (Fig. 2a: dark blue) showed uniformly small reach error (F(6.60, 118.7) = 0.75, p > .25). In the control SRT group (Fig. 2b), reaction time was reduced across blocks in the training session (F(2.55, 45.92) = 7.18, p < .001, η² = .07). A post-hoc linear trend analysis of reaction time across blocks was also significant (slope = -9.37, R² = .96, p < .001). These results indicated learning of the sequential motor skills.

Of primary interest was whether the mental rotation performance was differentially affected by VMR task training compared to the other control training tasks. To confirm all training groups performed the mental rotation task reasonably well, we first calculated the mean accuracy of the mental rotation task separately in the pre- and post-test sessions across the VMR (pre vs. post-test: 88.9% ± 1.4% s.e., vs. 90.2% ± 1.1%.), control reach (88.5% ± 1.2%.vs 91.4% ± 1.2%) and SRT groups (90.6% ± 1.6% vs 92.0% ± 1.6%). In a two-way repeated measures ANOVA, all participants showed higher accuracy in the post-test compared to the pre-test session (F(1, 54) = 16.14, p < .001, η² = .02) regardless of visuomotor training groups (F(2, 54) = 0.516, p > .250), with no interaction effect (F(2, 54) = 1.309, p > .250). Therefore, the accuracy was equated across three groups. In addition, we confirmed that the accuracy of pre-test across the three groups were equated by a one-way ANOVA (F(2, 54) = 0.62, p > .250).

Then, we shifted our focus to the mean reaction times of the mental rotation task in the pre- and post-test sessions (Fig. 2c) and a RT difference between the two test sessions (Fig. 2d). Larger RT reduction indicates larger mental rotation improvement. As shown in Fig. 2c, all participants performed the mental rotation task significantly faster in the post-test compared to the pre-test session (F(1, 54) = 121.4, p < .001, η² = .09) regardless of visuomotor training groups (F(2, 54) = 1.17, p > .250). More importantly, as shown in Fig. 2d, the VMR group (red) appeared to show significantly greater mental rotation improvement than the control reach (dark blue) and SRT (cyan) groups, leading to a significant interaction effect (F(2, 54) = 3.62, p = .034, η² = .01). We confirmed such observation by a one-way ANOVA: RT reduction across the three groups showed a significant main effect (F(2, 54) = 3.62, p = .034, η² = .01). Post-hoc independent t-tests show that the VMR group showed significantly larger RT reduction than the control reach (t(54) = 2.33, Dunnett-adjusted p = .022, d = .67) and SRT (t(54) = 2.33, Dunnett-adjusted p = .022, d = .72) groups. In addition, we confirmed that the RT of pre-test across the three groups were equated by a one-way ANOVA (F(2, 54) = 1.70, p = .193).

Note that by analyzing the motor performance of the three groups in the visuomotor washout session, we confirmed that participants maintained their motor learning until the end of mental rotation post-test. In the VMR group, the mean reach error of first trial block in visuomotor washout was − 25.77° ± 1.78° s.e. (Fig. 2a: red), demonstrating a strong after-effect of VMR adaptation and indicating that participants still maintained partial adaptation to the 45° tilted visual feedback until the end of the mental rotation post-test. In the control reach group, the reach error of first trial block was .30° ± .47° s.e. (Fig. 2a: dark blue), which was equivalent to reach error of last trial block (-.17° ± .40° s.e.) of the visuomotor training session (t(18) = 1.00, p > .025). This indicates that the control reach group maintained the same performance as in the visuomotor training session. In the SRT task, the averaged RTs for the repeated trials in visuomotor washout was 307.89 ms ± 14.23 ms s.e. (Fig. 2b), which was significantly shorter than the averaged RTs for the last training block (334.53 ms ± 13.47 ms s.e.) in the visuomotor training session (t(18) = 2.81, p = .006, d = .44) and the averaged RTs for the following random trials in visuomotor washout session (t(18) = 5.30, p < .001, d = 1.15). This indicates that the visuomotor learning in the SRT task was maintained until the end of mental rotation post-test.

In sum, we observed that after a short session of visuomotor rotation training, participants became faster in the mental rotation task compared to before, whereas training on the two control motor tasks without the operation of rotation resulted in significantly weaker transfer to the mental rotation task. Thus, Experiment 1 uncovered that VMR training could lead to improvements in mental rotation, in accord with the domain-general hypothesis.

Experiment 2: Mental Rotation Training Improves Visuomotor Rotation

In Experiment 2, we reversely examined whether mental rotation training enhances visuomotor rotation, while comparing it to other cognitive tasks without a rotation judgment. Such a bidirectional transfer between VMR and mental rotation provides strong converging evidence supporting a common domain-general operation. However, although we observed transfer from visuomotor rotation training to mental rotation in Experiment 1, if we do not observe the transfer in the opposite direction, it could indicate that rotation transfer is unidirectional from motor to cognition, or alternatively, visuomotor rotation requires more complex movement control beyond a rotational operation, and mental rotation training is not sufficient to modify the adaptative performance required for visuomotor rotation.

Here, all participants performed four sessions: pre-test of VMR, visuomotor washout, visual training, and post-test of VMR. During the visual training session, participants were randomly assigned to perform the mental rotation or the control color-discrimination task (see Methods: Exp.2 Tasks and Procedures for details). The two VMR sessions and visuomotor washout were identical for the two visual training groups. The washout session was designed to remove the adaptation to the 45° tilted visual feedback and brought back visuomotor performance at the baseline.

We confirmed that only the mental rotation task but not the control color-discrimination task involved mental rotation processes (Fig. 3a). In the mental rotation group, the RTs increased linearly with the rotation angles, suggesting that participants mentally rotated the letters from the rotation angle to upright. However, the RTs of the control color-discrimination task were the same across all letter angles, indicating that the task did not involve the mental rotation process. We performed a linear regression on the averaged RT across the absolute letter-rotation angles regardless of the rotation directions (clockwise vs. counterclockwise) for the mental rotation and control color-discrimination group, respectively. The linear regression slope is significantly different from zero in the mental rotation group (slope = 1.65, R² = .12, p < .001) but not in the control color-discrimination group (slope = − .04, R² < .001, p > .025). The accuracy of the mental rotation (90% ± 1.84% s.e.) and the control color-discrimination (91.54% ± 1.09% s.e.) task did not show a significant difference (t(34) = .72, p > .25).

Of interest was whether VMR performance is differentially affected by the two visual training tasks. Based on our previous work, the learning process of visuomotor rotation mainly happened in the first 10 trial blocks. We first calculated the reach errors of this period for the mental rotation group (Fig. 3b) and the control color-discrimination group (Fig. 3c) in the pre- and post-test sessions. Then we calculated the learning rate (LR) based on the reach errors in the pre- and post-test sessions (Fig. 3d) and the LR gain as a LR difference between the two test sessions (Fig. 3e). Larger LR gain indicates larger VMR improvement. The mental rotation group showed significantly larger LR gain than the control color-discrimination group.

A two-way group x test session repeated measures ANOVA shows a significant interaction effect (F(1, 34) = 5.15, p = .030, η² = .06), but no main effect of group (F(1, 34) = 1.53, p = .224) or test session (F(1, 34) = .81, p > .25). An independent t-test of LR gain showed that the mental rotation group had a significant higher LR gain than the control color-discrimination group (t(34) = 2.27, p = .015, d = .76). Post hoc paired t-tests show that only mental rotation group (t(34) = 2.48, Šidák-adjusted p = .018, d = .84) had LR improvement between pre- and post-test sessions but not for the control color-discrimination group (t(34) = .73, Šidák-adjusted p > .250). By a post hoc independent t-test, we confirmed that the LR in the pre-test session between the two groups were not significant different (t(68) = 0.87, Šidák-adjusted p > .250).

We also compared visuomotor washout performance across the two groups to assure that the better VMR post-test performance in the mental rotation group was not induced by the less visuomotor washout effect. We calculated the washout effects based on the level of performance at the end of the washout block (see Methods: Exp.2 Data analysis for details). By an independent t-test, we confirmed that the washout effects between the mental rotation (4.33° ± 3.12° s.e.) and the control color-discrimination (6.26° ± 4.32° s.e.) groups were not significant different (t(34) = 1.54, p = .133).

In sum, we observed that after a short mental rotation training, participants became faster in visuomotor adaptation compared to before, whereas training on the control color-discrimination task without the rotation operation did not facilitate the learning rate of visuomotor rotation. Such selective VMR improvement by mental rotation training provides strong converging evidence supporting the domain-general rotational operation hypothesis with Experiment 1.

In the mental rotation tasks, participants rotated the internal representations of tilted visual stimuli⁵². Similarly, in the VMR task, participants learned how to rotate the internal representation of tilted visual feedback to determine the appropriate direction of hand movements⁵³. In the present study, we provided a direct experimental link between visuomotor and mental rotations beyond correlations. Specifically, in Experiment 1, we showed that training on visuomotor rotation adaptation resulted in significantly larger improvement in a mental rotation task compared to training on other motor tasks without a rotational component, such as a reach task and a sequential reaction time (SRT) task. In Experiment 2, we reversely demonstrated that the learning rate of visuomotor adaptation was improved after training on a mental rotation but not after a control task that did not require a rotation judgment. Therefore, our results are consistent with the notion that the bidirectional transfer between visuomotor rotation and mental rotation depends on a common rotation operation on spatially mapped representations of stimuli.

Our results are largely consistent with a meta-analysis of neuroimaging studies on mental rotation^38,51 and visuomotor adaptation^54,55. There is a robust involvement of dorsal frontoparietal regions such as intraparietal sulcus areas (IPS) and superior parietal lobule area (SPL). These processes are likely to track the allocation of attention and implementation of spatial maps, contributing to visuospatial representation transformation. In addition, the supplementary motor area (SMA) and the pre-SMA were consistently activated. A role of SMA and pre-SMA is postulated in motor simulation and transforming spatial representations, which likely involves the sequential integration of elements into high-order representations. Pre-SMA projects to both M1 and the spinal cord, placing it in a good position to play a role in motor control and simulation. Bilateral inferior frontal gyri opercularis and triangularis (BA 44 and 45), which were recently associated with motor control, imitation, as well as spatial maps, were also found active. These areas are consistent with prior studies of VMR adaptation^54,55 .

Our observed bidirectional transfer can be understood together in the context of research under the umbrella of ‘embodied cognition’, which gives more credence to the motor system in playing a critical role in higher-level perceptual and cognitive functions^56–58. The tenet is that perceptual and cognitive processes are grounded in our bodily interactions with the environment^58–61. While transfer from perception to action is well documented, for instance in the form of observational learning, transfer from action to perception has received much less attention. However, as we showed in Experiment 1, empirical evidence supports that self-generated action and action experience can modulate perception and cognition^27,62−64. For example, blindfolded participants learned a novel coordinated upper-body movement based only on verbal and haptic feedback. The learned movement matched one of the visual test patterns, of which visual recognition was tested before and after the motor training session. Despite the absence of visual stimulation during training, participants displayed selective improvement of the visual recognition performance for the learned movement. This result, demonstrating that the plasticity of visual recognition led by motor learning without visual learning, supports a direct link between visual perception and motor systems⁶⁵.

Our mental-sensorimotor workspace transfer is also compatible with several learning theories. For instance, the ideomotor theory suggests that actions are represented by their associated perceived effects: to generate an action, the observer can simply activate that internal representation of those effects and the action will occur without any additional effort. A modern extension of the ideomotor theory is the theory of event coding (TEC)^66,67. According to this framework, the final stages of perception and the initial stages of action control share a domain of coding where planned actions are represented in the same format as perceived events. One of the implications of this approach is that, under appropriate conditions, perceived environmental events can induce certain actions by way of similarity or feature overlap.

Alternatively, the symbolic learning theory⁶⁸ postulates that mental practice, a training method by which an imagined motor act is mentally rehearsed multiple times in the absence of real movement execution^29,69, leads to rehearsal of the cognitive components of a motor task. According to this theory, training in mental rotation in Experiment 2 might induce a cognitive rehearsal of the rotational movement, and in turn, facilitate visuomotor rotation. Previous studies have also demonstrated that mental practice can improve motor skill performance^28,70,71. However, since the imagined movement associated with mental rotation on tilted objects is not the same as the rotational movement involved in VMR, a further study is needed to fully understand how and whether mental practice on rotating tilted letters could benefit VMR learning process.

To conclude, by bringing together methods and insights from cognitive psychology and motor control, our work clearly demonstrated remarkable reciprocal plasticity achieved through the two widely different tasks. This newly observed reciprocal enhancement between the cognitive and motor processes, which could not be explained by a unidirectional sequence of information processing^1–4, highlights human actions are not merely the outcomes of internal mental functions, but in fact they can influence cognitive processes. By demonstrating intertwined operations in mental and sensorimotor workspaces, the present work represents a substantive departure from the status quo and urges future studies to shift focus to a new interactive framework between cognitive and sensorimotor domains. Understanding their interrelation with an integrated approach will significantly enhance our understanding of the dynamic interdependence between cognitive and sensorimotor mechanisms. It will explain adaptive, real-world selection behavior eventually.

Participants. In Experiment 1, a total of 57 right-handed Brown University students participated in the one-hour study for course credit or monetary compensation, reported normal or corrected-to-normal color vision and were naïve to the aims of the experiment. Nineteen participants (15 females, mean age 21.26 years) were included in the visuomotor rotation group, 19 participants (19 females, mean age 19.58 years) were included in the reach group, and 19 participants (16 females, mean age 21.05 years) were included in the SRT group. The sample size (19 per group) was determined based on the effect size needed to achieve 75% statistical power (η² = 0.13, n = 18) as determined with an ordinary one-way ANOVA on preliminary data.

In Experiment 2, a total of 36 right-handed Brown University students participated in the one-hour study for course credit or monetary compensation. Each reported normal or corrected-to-normal color vision and were naïve to the aims of the experiment. Eighteen participants (9 females, mean age 20.78 years) were included in the mental rotation group, and 18 participants (12 females, mean age 19.33 years) were included in the control color group. The sample size (18 per group) was determined based on the effect size needed to achieve 80% statistical power (η² = 0.12, n = 15) as determined with a one-way ANOVA on preliminary data.

All experimental protocols were approved by the Brown University Institutional Review Board (IRB) in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. All research was performed in accordance with the approved IRB guidelines. Informed consent was obtained from all the participants.

Apparatus. All experiments were conducted in a dimly illuminated room. Participants sat in front of a 21.5-inch Apple iMac computer, and all stimuli were presented with 60Hz refresh rate and 1920 x 1080 pixel resolution. In the visuomotor rotation and reach tasks, participants held a stylus pen with their right hand and moved the pen across the surface of a touch screen (Magic Touch; Keytec, Garland TX), which lay flat on a table and aligned with the midlines of participants and the computer screen. The stylus movement controlled a corresponding cursor on the computer screen. Stimulus presentation and recording of cursor displacement were conducted using custom software designed with MATLAB (2011a, Mathworks) and Psychtoolbox⁷².

Experiment 1

Tasks. Mental rotation task. Four asymmetric upper-case letters (F, G, J, R) and their horizontally-flipped (mirrored) images were used as stimuli, for a total of eight characters (font: Courier New, height: 2.82 cm, color: white). Each stimulus was rotated in 45° increments from − 135° (counterclockwise from upright) to 135° (clockwise from upright), for a total of eight angles (Fig. 1b). On each trial a white fixation dot (0.24 cm diameter) appeared at the center of the screen (black) and stayed for 1,000 ms. Then, the stimuli were presented at the same location as the fixation dot. Participants were instructed to determine whether each stimulus was a normal letter or a mirrored image by pressing a button with their right hand (“1” for normal, “2” for mirror) and were required to respond as accurately and as quickly as possible. The stimuli stayed on the screen until a button was pressed, but no longer than 5,000 ms. Distinct auditory feedback was presented to inform the accuracy of button-press responses. One block consisted of 64 trials (8 stimuli x 8 angles) in random order.

Visuomotor rotation (VMR) task (Fig. 1c, red border). Participants were instructed to move a cursor on the screen from the starting base in the center of the screen toward a reach target. The starting base was a white circle with a diameter of 1 cm, and the reach target was a solid white dot with a diameter of 1 cm and located 5.5 cm from the starting base. At the beginning of each trial, the initial location of the cursor was outside the starting base. Participants moved the cursor into the starting base to trigger the appearance of the target, which stayed on the screen for 1500 ms. Each target direction was located in 45° increments from 0° (12 o'clock) to 315°, for a total of eight possible target locations.

Participants were asked to move the cursor toward the target as fast and as linear as possible and move it back to the starting base immediately after they reached the target. There were two types of trials. In rotation trials, the cursor direction (solid line) was rotated 45° clockwise (CW) from the hand trajectory (dotted line). This perturbation creates movement errors, forcing the brain to learn or update new sensory-motor relationships to reestablish appropriate motor control. In no-rotation trials, the cursor direction (solid line) normally followed the hand trajectory (dotted line). This task included 1) one practice block (24 no-rotation trials), 2) one baseline block (80 no-rotation trials), measuring inherent bias in the reaching movement toward each target, and 3) four training blocks (4 x 80 rotation trials, 10 trials for each of the 8 reach targets). Eight target locations were presented randomly within every 8 trials in each block.

Control reach task (Fig. 1c, blue border). Everything was the same as the VMR task except the cursor direction followed the hand trajectory normally without rotation (no-rotation trials). This task was composed of 1) one practice block (24 no-rotation trials), 2) one baseline block (80 no-rotation trials), and 3) four reach blocks (4 x 80 no-rotation trials, 10 trials for each of the 8 reach targets). Eight target locations were presented randomly within every 8 trials in each block.

Control sequential reaction time (SRT) task (Fig. 1c, cyan border). Four white open squares were arranged horizontally on the screen to indicate four possible target locations, designated 1–4, which corresponded to four horizontally arranged buttons (V, B, N, and M) on the keyboard (Fig. 1c, cyan border). The four squares, with 2.43 cm sides and a 0.15 cm border width, were presented along the horizontal meridian with 4.05 cm gaps between them. On each trial a white dot target (2.03 cm diameter) appeared at one of the four locations. Participants were instructed to press the corresponding button as accurately and as quickly as possible and were not given accuracy feedback. Targets appeared either at random locations (random trials) or at a target location within a 12-item repeating sequence ([2, 3, 1, 4, 3, 2, 4, 1, 3, 4, 2, 1]) (repeated trials). The task was composed of 1) one practice block (12 random trials), 2) one baseline block (80 random trials), measuring any inherent bias in the reaction time toward each target location, and 3) five training blocks (5 x 120 repeated trials).

Procedures. All participants performed four sessions: 1) pre-test of mental rotation task, 2) visuomotor training, 3) post-test of mental rotation task, and 4) visuomotor washout (Fig. 1a). After one practice block of the mental rotation task (Fig. 1b), all participants performed the pre-test of mental rotation task (4 blocks) to assess their baseline performance. During the visuomotor training session, participants were randomly assigned to perform the visuomotor rotation (VMR) task or one of the two control tasks (reach or SRT), followed by the post-test of mental rotation session (4 blocks). The visuomotor washout session was designed to assure that the visuomotor training effects were maintained during the post-test of mental rotation. The VMR and reach groups performed their baseline block (80 no-rotation trials) and the SRT group performed 48 repeated trials followed by 80 random trials.

Data analysis. Performance for the mental rotation task was measured by reaction time, the duration between the letter onset and button response, of all correct trials in each test session. Data analysis procedures for VMR and control reach tasks generally followed our previous studies ^10,24,26. We differentiated the position of the cursor to obtain tangential velocity. The onset and offset of the movement were defined as when the cursor speed exceeded and fell below 5% of peak velocity, respectively. Reach error was defined as the angle difference between the line joining the starting base to the target and the line jointing the cursor locations at movement onset and at peak velocity. Reach error was averaged across 8 successive trials as a trial block covering all target locations. For the SRT task, the performance was measured by reaction times, the duration between the target onset and button response, of correct trials. The reaction times were averaged in each block in the visuomotor training session. For the washout block, the reaction times were averaged for first 48 repeated trials and following 80 random trials, respectively.

We used MATLAB (2020a, Mathworks) and Prism 9 for macOS (GraphPad Software, La Jolla California USA, www.graphpad.com) to perform all data processing and statistical analysis. We used parametric ANOVA to analyze the data. If multiple post hoc comparisons followed, we used Šidák correction⁷³ for comparisons between multiple treatment groups and Dunnett correction⁷⁴ for comparison of treatments with a single experiment group. Greenhouse-Geisser correction⁷⁵ was applied when the assumption of sphericity was violated. All error bars represented standard error of the mean (SEM). The effect size of ANOVAs and t tests were measured by eta-squared (η²) and Cohen’s d, respectively. According to Cohen⁷⁶, η²s of .01, .06, and .14 and Cohen’s ds of 0.20, 0.50, and 0.80 are considered small, medium, and large effect sizes, respectively.

Experiment 2

Tasks. The same mental rotation and VMR tasks as in Experiment 1 were used except for the following modifications. In the mental rotation task, in each trial, letter stimuli were presented in one of the two colors: pink (RGB: 255, 240, 255) and purple (RGB: 240, 255, 255). The color of the stimulus was randomized within each block with the equal number. We also added one new control task by modifying a task instruction of the mental rotation task with the same display. The control color-discrimination task required participants to indicate whether the stimulus was pink or purple by pressing a button with their right hand (“1” for pink, “2” for purple) and were required to respond as accurately and as quickly as possible regardless of its rotation angle.

Procedures. To examine a reverse transfer from mental rotation training to VMR, all participants perform the following four sessions: 1) pre-test of VMR task, 2) visuomotor washout of VMR task, 3) visual training, and 4) post-test of VMR. The pre-test VMR sessions were identical to the VMR task of the VMR group in Exp. 1, including one practice block, one baseline block, and four training blocks. The post-test VMR sessions include four training blocks. The washout session (80 no-rotation trials) was designed to remove the adaptation to the 45° tilted visual feedback and brought back reach to the baseline level. During the visual training session, participants were randomly assigned to perform the mental rotation or the control color-discrimination task.

Data analysis. In additional to the analyses used in Experiment 1, we further calculated the learning rate (LR) in the VMR tasks to indicate the learning performance. The LR is quantified based on the reach errors in the first block of each session, using Eq. 1.

$$y= \text{a}+\text{b}\text{*}{\text{e}}^{(\text{c}\text{*} -\text{x})}$$

Where y is the averaged reach error of each trial block in the first block; a is the level of performance at the end of the block; b is the adjusted initial performance; c is the learning rate, representing the rate of visuomotor adaptation; x is the trial block index from 1 to 10, representing the time course of the visuomotor adaptation. The initial values of a and b for the model-fitting is the reach error in trial block 10 and the absolute reach error difference between trial block 1 and 10, respectively. The learning rate c was fitted from .5 leading to the range of [0, 1].

In addition, we used Eq. 1 to calculate the VMR washout effects in the washout session. Where y is the absolute averaged reach error of each trial block in the washout block; a is the absolute level of performance at the end of the block, representing the washout effect; b is the absolute adjusted initial performance; c is the decay rate; x is the trial block index from 1 to 10, representing the time course of the visuomotor washout. The initial values of a and b for the model-fitting is the absolute reach error in trial block 10 and the absolute reach error difference between trial block 1 and 10, respectively.

Acknowledgments

This work is supported by NSF BCS-1555006 and BCS-2043328 to J.H.S.

Author Contributions

J. Guo and J.-H. Song designed the tasks. J. Guo collected the data. J. Guo conducted the data analysis. J. Guo and J.-H. Song wrote the manuscript.

Data availability statement

All data have been made publicly available via the Open Science Framework and can be accessed at https://osf.io/r4p2n/.

Competing interests

None

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Reciprocal Facilitation Between Mental And Visuomotor Rotations

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