Ten volunteers participated in this experiment (31 ± 12 years old, 6 male and 4 female). Nine subjects were right-handed according to the Edinburgh handedness test, one subject was left-handed. Prior to data collection, the participants were informed about the experimental procedure and signed an informed consent form approved by MIT's Institutional Review Board.

The participant was seated in front of a table, with the sternum close to the table edge (Figure 1). To fixate the shoulder position, two belts tied the upper body to the back of the chair. A mark on the table was used to locate each subject in a comparable position. The marked spot was in the subject's mid-sagittal plane, 23 cm away from the edge of the table (in the anterior-posterior direction) and ~26 cm distant from the subject's sternum. The height of the chair was adjusted to position the subject's upper arm to be ~45° to horizontal; the forearm rested on the table. From this neutral position, the subject could perform a reaching movement forward and backward in the sagittal direction, involving both shoulder and elbow joints without reaching the limits of their workspace. The forearm was mounted on a low-friction skid that reduced the static and kinetic friction on the surface during the movements. A brace stabilized the wrist to discourage wrist joint rotation.

Two circular targets were shown on a screen in a vertical arrangement to signal the amplitude of the movements (Figure 1). The two targets were at a distance of 14 cm from the neutral position, specifying a movement amplitude of 28 cm. The targets had a radius of 5 cm, which was relatively large so that accuracy requirements were minimal. The monitor was placed ~65 cm in front of the subjects to display the targets and a cursor showing their movements. The display gain was 0.5, showing the targets and movement amplitude at half of their real size. The subjects were asked to move from target to target by moving the handle back and forth on the table in the sagittal direction. A computer-generated metronome signal prescribed the timing for the movements.

The subject grasped a handle onto which a magnetic Flock of Birds sensor was attached (Ascension Technologies, Burlington, VT). The static accuracy and resolution were 0.25 and 0.08 cm, respectively. The combined weight of the sensor and handle was ~70 g, which is about 1/8 of the mass of the hand. The sampling frequency was 100 Hz. This frequency was sufficient as the frequency content of the motion was significantly below 50 Hz and anti-aliasing was not required. The position was zeroed with the handle in a neutral position shown by a mark on the tabletop. Data collection was controlled by a custom-made software routine written in Tcl on a computer running Linux Ubuntu operating system.

At the beginning of each trial participants placed their hand in the neutral position. All participants used their dominant hand. They were then instructed to move between targets in synchrony with the metronome sounds. The metronome sequence took the following form for all trials: the trial began with 20 sounds separated by an interval of 2 s, presenting a constant-period signal for 40 s. Subsequently, 50 sounds were produced where each interval decreased by 36 ms, ending at an interval of 200 ms. This short period was sustained for 20 sounds, equivalent to a duration of 4 s. After this constant-period interval, another 50 sounds with an increasing interval of 36 ms followed. The trial ended with 20 sounds of 2 s duration. The total trial duration was 194 s for a sequence of 160 moves. Figure 2 shows the sequence of periods as a function of time and also as a function of the number of metronome sounds. The figure also includes the change of inter-sound-interval as a percent of the previous inter-sound-interval to highlight that the changes were initially small, below 2%, but then grew to a maximum of 18%.

Subjects were instructed to perform point-to-point movements with an explicit dwell time to separate the movements into discrete movements. This dwell time should last for 50% of the metronome interval as accurately as possible. Subjects were instructed to maintain this discretization of movements for as long as possible, even when the pace increased and made the task more difficult.

This same timing sequence was presented under three different perceptual conditions. In the “vision” condition V-short, subjects had their eyes open and executed their movements to the displayed targets on the monitor. All metronome sounds had a duration of 50 ms, a short “beep.” In the “no-vision” condition, NV-short, subjects were asked to close their eyes, which removed the amplitude specification; the metronome sounds were still short with a 50 ms duration. In a third condition, NV-long, the sound duration was longer and adjusted at each interval to last 50% of the period, giving exact auditory information about the instructed dwell duration. Subjects again kept their eyes closed to encourage focus on the auditory timing information. These three conditions were repeated twice, both times in the same sequence. Comparison of the two trials allowed a test whether the performance features changed with practice. The total duration of these six experimental trials was ~25 min. Prior to data collection, each participant performed several moves with the metronome to familiarize him/herself with the task.

Of the 3D signals from the Flock-of-Birds sensor only displacements in the sagittal direction were processed. Figure 3 shows a complete time-series of one trial (condition V-short), divided into five segments due to the length of the trial. The time-series reveals the dwell times at the extreme positions in the initial and final slow-paced sections of the trial. As the pace increased, the dwell times steadily decreased and then disappeared. The dwell times reappeared with increasing metronome interval. The vertical lines denote the onset of the metronome sound; the circles mark the onset and offset of each movement.

To extract the changes of kinematics with the changing metronome pace and to identify whether a transition in control occurred, the kinematic signals were analyzed as follows. Before extracting quantitative markers, the position data were smoothed using a five-sample moving average filter, with centered filtering that did not introduce a lag (smooth function in Matlab®). Velocity was obtained numerically from the two-sample difference of the position signal, and was smoothed again with the same five-sample moving average filter. The acceleration signal was obtained by spline fitting and differentiation of the position signal (see below).

For all analyses, the continuous kinematic data were parsed into single movements, delimited by t_onset and t_end (Figure 4A). Both onset and end of a movement were defined by the time when velocity crossed a threshold, defined as 3% of the peak velocity of the same movement. Given that all subsequent measures depended on this temporal demarcation, alternative thresholds of 1 and 5% were compared for their influence on subsequent analyses. As no significant differences were identified, we used 3% as the threshold for all subsequent analyses. Another option was to apply a fixed threshold, for example based on a threshold velocity. However, with decreasing interval and increasing velocity, a fixed threshold would favor earlier parsing. As this might have biased the calculations of dwell times (see below) in favor of our hypothesis, we chose the percent-based criterion.

This parsing analysis, however, faced a challenge when the movements merged to become approximately sinusoidal: the velocity decreased to zero but there was no longer any dwell time (Figure 4B). To eliminate false detections, a linear fit was applied to the velocity samples that were below the 3% threshold; the number of samples for this regression varied between 3 for fast movements up to 100 for slow movements (depending on the individual). t_onset and t_end were considered coincident when the R² of the linear fit was above 0.99. The sample with the lowest velocity defined the time separating adjacent movements. To test the robustness of the onset and end times, we also ran the algorithm with an R² cut-off of 0.95. The small differences that resulted did not have any effect on the subsequent analyses, most notably on onset and offset of dwell time.

Movement time MT_i, dwell time DT_i, and total movement time TMT_i were defined as    MTi=tend,i−tonset,i   DTi=tonset,i+1−tend,iTMTi=tonset,i+1−tonset,i where i and i + 1 denote the movement index. The duty cycle DC_i for each movement was defined as: DCi=MTi/​(MTi+DTi)

By instruction, DC_i should be equal to 0.5; it became 1.0 when dwell time became zero.

The criterion used to define a transition between discrete and continuous rhythmic movements was when dwell time became zero. In the accelerating portion of the trial, this time DT = 0_Accel was defined by the movement i that had zero DT and was followed by at least two more movements with zero DT. Similarly, for the decelerating portion, where rhythmic movements transitioned to discrete movements, DT = 0_Decel was defined as the last movement i with zero DT preceded by at least two movements with zero DT. This criterion proved to be very robust, as the onset and offset times remained unaffected by small changes in the parsing algorithm (see above). Figure 5 shows an exemplary trial with MT and DT displayed as a function of metronome number. For statistical comparisons we examined the movement times MT and metronome numbers associated with DT = 0.

To evaluate the hypothesized transition from discrete to continuous rhythmic movements, it was also necessary to determine the minimum movement duration that subjects could perform, which was typically longer than the shortest metronome interval. Inspection of the data showed that the region of minimum movement duration was not coincident with the trial segment of the 200 ms metronome intervals. Hence, to obtain a robust estimate of this interval when subjects reached and left their minimum movement duration, a window was defined using the following threshold (Figure 5): MT≤MTmin+0.05×(MTmax−MTmin) where MT_min and MT_max was the absolute minimum and maximum in the same trial, respectively. Successive MTs below this threshold defined the window length, which was typically shorter than the 20 metronome sounds that defined the constant-period interval. This window is shown in Figure 5 as a horizontal red line in the center of the trial. The mean MT in this interval was used as a robust estimate of MT-min.

These defined landmarks served to quantify the relation between DT = 0 and when minimum movement duration was reached. In particular, we defined ΔT_Accel as the interval between DT = 0_Accel and the start of MT-min, and ΔT_Decel as the interval between the end of MT-min and DT = 0_Decel. Δ​TAccel=start(MT-min)−DT=0AccelΔ​TDecel=DT=0Decel−end(MT-min)

It was expected that with changing pace, the shape of the kinematic profile of each movement would change. To capture this modulation, a discreteness index DI was defined for each movement i based on the position and acceleration profiles (Figure 6). The DI was defined as the relative timing of the first peak in the acceleration profile t_acc with respect to the movement time: DIi=(tacc,i−tonset,i)​/MTi

Figures 6A,B illustrate this measure for discrete and rhythmic movements, using simulated profiles for clarity. A discrete movement DI had a non-zero value, while for the sinusoidal movement, DI was equal to zero. For reference, the DI for a cycloidal movement is 0.25. A movement that minimizes jerk has a DI of about 0.20 (Hogan, 1984). Figures 6C,D shows data. As acceleration is notoriously noisy, the position signal of each movement (in the interval between onset and offset, not considering dwell time) was approximated via a quintic spline. The second derivative of the spline served as the acceleration profile needed for the calculation of the discreteness index. As the figure for the fast rhythmic movement shows, the quintic spline was different for the first and second half of the movement. Importantly, though, the DI was zero.

To quantify how movements were synchronized with the metronome sounds, a measure of onset asynchrony was defined as the temporal difference between each movement's onset and the corresponding metronome sound's onset (Figure 4A): OAi=tonset,i−tmetronome,i

Positive numbers indicated that the movement onset lagged the onset of the metronome sound. Determining this difference needed care as in the faster portions of the trial subjects occasionally skipped cycles. To avoid erroneous matching yielding onset asynchronies longer than a cycle, the algorithm matched the metronome and movement times with increasing time for the first accelerating part of the trial; for the second decelerating part of the trial, the algorithm started at the end of the trial and worked backwards to determine correspondence between metronome and movement times.

Comparison of the different measures in the three conditions was performed using two-way analysis of variance, with condition and trial as fixed factors and subjects as random factor. If variables of the accelerating and decelerating portion were compared, a three-way 3 (condition) × 2 (Accel, Decel) × 2 (trial) ANOVA was conducted. If different segments of the same trial were compared, a 3 (condition) × 2 (segment) × 2 (trial) ANOVA was used. Student t-tests were used to compare time estimates with metronome times. The significance level was always set at α = 0.05.

To evaluate whether subjects followed the task instructions and produced the movement times as specified, Figure 7 presents the total movement time, TMT, defined as the temporal difference between two consecutive movement onsets, plotted for each movement and for all subjects in condition V-short. The green band is an envelope around the instructed intervals with a width of ±350 ms that encompassed 99% of all values. The results are displayed as a function of metronome number. This convention avoided inconsistent plotting across individuals when movement cycles were skipped, which happened frequently during the short-interval section of the trial.

To test the subjects' synchronization to the instructed time, TMT was regressed against the metronome interval. For perfect synchronization, the slope of this regression should be the identity line. For all 10 subjects in all three conditions the mean slope was 0.98 ± 0.025 (one standard deviation), with R²-values ranging between 0.89 and 0.99. There was no significant difference between the three conditions. Hence, it can be concluded that overall, subjects maintained synchronization with the metronome.

To assess whether subjects satisfied the task instructions and produced discrete movements separated by dwell times, the TMT was split into its component times, dwell time DT, and movement time MT. Figure 8 shows one subject's data in the three conditions (first trial), with DT in green, MT in blue, and the duty cycle DC in red; the black line represents the metronome-specified movement interval. As is clear from the duty cycle, the instructed dwell time was not always achieved, neither in the steady state portions of the trial, nor in the accelerating and decelerating portions. This subject did not display a duty cycle of 1.0 in the beginning of conditions NV-short and NV-long. Other subjects showed similar deviations in this part of the trial but no systematic pattern or dependency on condition could be identified.

When the movement intervals changed after movement number 20, the duty cycle was not maintained for long. Following a steady decrease, dwell time DT vanished at about metronome number 60 in the decelerating portion of the trial. In the accelerating portion of the trial, DT re-appeared approximately at metronome number 100, followed by a declining duty cycle. Figure 9 summarizes these observations for the condition V-short. The figure shows the average of all 10 subjects; the shaded error bands represent ± one standard deviation across subjects.

The important observation in Figures 8, 9 is that dwell times vanished before movement durations had reached their minimum in the center portion of the trial; in the accelerating portion of the trial, dwell times re-appeared after movement time had already increased. According to the task instructions, dwell times should have declined continuously proportional to movement duration until minimum movement time was reached. In contrast, Hypothesis 1 predicted that discrete movements would be abandoned early in favor of computationally less demanding rhythmic movements.

To test Hypothesis 1, we first determined minimum movement time, MT-min, as reached in the center of the trial (see Figure 5 and methods). All subjects' values in the three perceptual conditions were submitted to a 3 (condition) × 2 (trial) ANOVA. Results revealed that the condition V-short showed a significantly longer MT-min of 248 ms, compared to 220 and 217 ms in the no-vision conditions NV-short and NV-long, F_{(2, 47)} = 5.76, p < 0.001. Post-hoc tests showed that the 248 ms in the V-short condition was significantly different from the other two conditions, which did not differ from each other (p < 0.01). The two trials showed no significant difference. Pair-wise student t-tests compared MT-min against the instructed movement interval of 200 ms and detected a significant difference for all subjects in all three conditions, t₍₅₉₎ = 4.82, p < 0.0001. This result again corroborated that, as intended, subjects had reached their limit of movement duration in all conditions. This conclusion was further supported by the fact that this effect was not influenced by practice. The shorter times in the no-vision conditions correlated with a decrease in amplitudes. As expected from Hypothesis 4, subjects complied better with timing demands of the task when visual information was removed.

We next determined the times when dwell time disappeared and reappeared in the decelerating and accelerating portions and the corresponding movement times, MT_{DT = 0} (see Figure 5). Subjecting MT_{DT = 0} to a 3 (condition) × 2 (Accel-Decel) × 2 (trial) ANOVA revealed a significant difference between conditions, F_{(2, 80)} = 4.77; p < 0.01, while there was no difference between the accelerating and decelerating segments, nor a significant difference across the two trials. Subjects performed with an average movement time of 463 and 457 ms for V-short and NV-short, respectively; for NV-long the average movement time was 536 ms, which was significantly longer than the other two conditions, which did not differ from each other (p < 0.01). These times were significantly longer than the minimum movement times (p < 0.01). This result supported Hypothesis 1. However, the symmetry in movement times between the accelerating and decelerating portions was not consistent with Hypothesis 2. Importantly, these results also revealed that zero dwell times started earlier in the condition with the explicit temporal information. This finding is consistent with Hypothesis 3 predicting that explicit temporal information will not prevent a transition. In fact, the result shows that the transition occurred even earlier in the short beep conditions.

Despite the symmetry in movement times at DT = 0, inspection of Figures 8, 9 shows that the first and second transition, i.e., start and end of the minimum movement time, MT-min, were slightly shifted relative to the instructed movement times or metronome indices, suggestive of hysteresis. To quantify this observation we defined the intervals between DT = 0 and MT-min in the accelerating and decelerating portions, ΔT_Accel and ΔT_Decel, for each subject (see methods and Figure 5). A first set of t-tests confirmed again that these intervals were significantly different from zero for all subjects and conditions (p < 0.01). Subsequently, ΔT_Accel and ΔT_Decel were subjected to a 3 (condition) × 2 (Accel-Decel) × 2 (trial) ANOVA. Results showed a significant difference between the two transitions, F_{(1, 76)} = 24.77, p < 0.0001, but no differences between the three perceptual conditions, nor between the two trials. Dwell time vanished on average 11 movements before the minimum movement time was reached; in the decelerating portion movement time MT increased on average 7 movements before dwell time DT re-appeared. This asymmetry is consistent with a hysteresis effect as predicted by Hypothesis 2. The persistence of fast rhythmic movements after metronome intervals had increased lasted ~2.8 s, which is relatively long and makes a mechanical cause improbable.

To further identify the hypothesized transition we examined changes in the continuous movement kinematics in terms of the discreteness index DI; this index quantified the shape in the bell-shaped velocity profile of each movement. Figures 10A–C shows the DI for all subjects in the three perceptual conditions. As can be seen, DI changed rapidly near movement number 60 and again at 100 in a similar fashion for all three conditions. Figure 10D shows an exemplary single subject's trial in the V-short condition to further illustrate the abrupt change in DI. For statistical analysis, the trial was parsed into three segments: before DT = 0_Accel, the center portion, and after DT = 0_Decel. This individual's DI values were 0.13 in first and last segments, with a drop to 0.05 in the middle segment. A 3 (segment) × 3 (condition) × 2 (trial) ANOVA on all subjects' data identified significant differences across segments, F_{(2, 76)} = 431.42, p < 0.0001. There was no main effect or interactions for the perceptual conditions and trials. The average DI across the three conditions was 0.13 before DT = 0_Accel, 0.04 in the center portion, and 0.14 after DT = 0_Decel, similar to the exemplary subject in Figure 10D. This measure captured changes in each trajectory and revealed a relatively abrupt transition from discrete to rhythmic movement control strategies, consistent with Hypothesis 1.

Given that the task required synchronization with a metronome, the synchrony of movement onset with the auditory stimulus was analyzed to assess entrainment and predictive control. Figures 11A–C shows the sequence of onset asynchronies, the difference between movement and metronome onset. The data from all individual subjects in all three conditions showed that there was considerable variability across subjects. Despite this inter-individual variability, the majority of subjects exhibited a relatively constant onset asynchrony OA from the trial start until movement numbers 50–60, which lasted well beyond the initial long-interval section. Similarly, the second part of the trial showed relatively constant values of OA for the last 40–50 movements. The OA values after DT = 0_Accel and before DT = 0_Decel are shown as dashed lines. To highlight these observations, Figure 11D shows OA values in a single subject's trial in the V-short condition. The straight black lines were obtained from linear regression over the first 40 and last 40 OA estimates in each trial. As the slopes did not differ from zero in all conditions, we used the mean values to estimate representative onset asynchrony for the accelerating and decelerating segment and condition.

A 3 (condition) × 2 (segment) × 2 (trial) ANOVA identified a main effect for condition, F_{(2, 106)} = 6.755, p < 0.002, and a main effect for segment, F_{(1, 91)} = 5.97, p < 0.05. There were no other main effects or interactions. In the two conditions with the short metronome sound, V-short and NV-short, subjects showed an onset asynchrony of 536 and 399 ms; in NV-long it was only 129 ms, which was significantly different from the other two, as shown in post-hoc tests (p < 0.01). The delayed onset of the discrete movement in the two conditions with the short metronome sounds indicates reactive responses to the auditory stimulus, while the shorter onset asynchrony suggests some degree of predictive control. The long reaction times also suggest that the movements were complex and demanded substantial neural resources. The mean onset asynchronies in the first and second segment of the trials were 469 and 240 ms, respectively. Even though this difference between the two segments does not directly test hysteresis, it may still be consistent with the hysteresis effect shown above (Hypothesis 2). Summarizing, explicit auditory timing information apparently elicited predictive control.

The findings unambiguously demonstrated a switch from the instructed discrete movements to continuous rhythmic performance as TMT was reduced and another switch back to discrete movements as TMT increased again. This could not be attributed to limitations of the peripheral neuro-musculo-skeletal system. Despite the fact that subjects matched their TMTs to the metronome interval, they did not preserve the instructed dwell times, but progressively reduced them. The movement times at which dwell times disappeared were significantly longer than the minimum movement time achieved.

This alone might be attributed to a limited peripheral response speed or bandwidth, because, for a given metronome interval, a movement with dwell time has higher frequency components than a movement without dwell. Stated in the time domain, a movement with infinitesimal dwell time has larger accelerations than a smoothly rhythmic movement (Hogan and Sternad, 2007). However, changing the sensory condition significantly affected the movement duration at which dwell times disappeared—536 ms for NV-long vs. 463 and 457 ms for V-short and NV-short, respectively. In the NV-long condition, the passage to zero dwell time cannot be attributed to limited peripheral response speed, because subjects were demonstrably capable of faster movements with non-zero dwell times.

In addition, in all subjects and conditions, the discreteness index showed an abrupt change around metronome number 60 and 100. It dropped rapidly to zero approximately coincident with dwell time reaching zero as TMT decreased; it recovered rapidly from zero approximately coincident with dwell time increasing from zero as TMT increased again. Both of these observations are as predicted by Hypothesis 1. Discrete movements with precisely timed starts and stops require more neural resources than smoothly rhythmic movements (Schaal et al., 2004). As available movement time decreased, the increasing demand on neural resources evoked a switch to the neurally simpler rhythmic movements. Once available movement time increased again, the more challenging discrete movements were reinstated.

A common phenomenon in systems with more than one stable state is that transitions between these stable states may display hysteresis: transitions in one direction occur at different parameter values than in the reverse direction. To test Hypothesis 2, the experiment included accelerating and decelerating portions that induced transitions from discrete to rhythmic movements and back from rhythmic to discrete movements, respectively. However, the TMT when dwell time reached zero as metronome intervals decreased was not significantly different from the TMT when dwell time increased from zero as metronome intervals increased again. This result is not consistent with Hypothesis 2.

Nevertheless, some asymmetry in the transitions was evident. The fastest (rhythmic) movements persisted for several seconds (2.8 s on average) after the metronome intervals began to increase again. This was followed by a faster rate of increase of movement time and dwell time with metronome number than their rate of decrease during the accelerating portion of the trial. The exact origin of this phenomenon requires further investigation.

One condition was included that presented explicit auditory information about the dwell duration in order to test whether this would guide subjects to produce the required dwell time. Hypothesis 3 stated that explicit auditory information about dwell duration should not be able to prevent the transition between the two primitives: transitions should occur when the neural system was challenged and induced to resort to simpler solutions. Indeed, as predicted, auditory specification of dwell times did not facilitate the discrete movements and the weak hysteresis effect in the decelerating portion was unaffected by the different auditory conditions. However, there was one effect of prolonged auditory stimuli that was counter to expectations: when the metronome sounds were longer, subjects switched to rhythmic movements earlier and switched back to discrete movements later than in the short metronome conditions. This effect may be due to the fact that “filling” half of the time interval with sound made the auditory signal appear more periodic: sound and no-sound alternated periodically. The more rhythmic nature of the signal may have entrained the movement and induced rhythmic behavior.

The different auditory stimuli produced one additional effect. The two conditions with the short sound revealed a considerable delay between metronome to movement onset, 536 and 399 ms, that clearly indicated a reactive response. This is not surprising as the intervals of 2 s in the beginning and end of the trial were longer than humans can temporally integrate and perceive as rhythmic (Fraisse, 1984). In contrast, in the long-beep condition the delays from metronome onset to movement onset were considerably shorter (129 ms). In this condition the silent interval was at most 1 s, alternating with 1 s of sound. This sound pattern can easily be perceived as periodic. Human subjects readily predict periodic signals and have been shown to reduce the phase lag of their responses or even exhibit phase lead (Aschersleben, 2002). This distinctive feature of responses to periodic stimuli may also account for the transitions to and from rhythmic performance at longer metronome intervals. In effect, the auditory stimulus was more readily perceived as rhythmic and may have entrained a primitive rhythmic motor behavior (Mates et al., 1994). If so, this raises the intriguing possibility that perception as well as action may be based on dynamic primitives.

The substantially longer delays in the V-short and NV-short conditions suggest that subjects only reacted to the metronome trigger—without any prediction. Reactive behavior during the long inter-beep intervals of 2–1.5 s is not unexpected, although the reaction times were approximately 2–3 times longer than simple auditory reaction times. Moreover, the same reaction times persisted into much shorter intervals, even though the interval changes were systematic and predictable. These long reaction times may indicate the demands of movement planning (Henry and Rogers, 1960; Sternberg et al., 1978). It would appear that simple reaching movements with a timed dwell demanded considerable neural resources.

We speculated in Hypothesis 4 that visual information may mask primitive-based transitions and performance with visual information excluded may better reveal transitions due to neural or computational constraints. However, none of the transition effects showed any dependency on the presence of visual information. One possible explanation for this lack of effect could be that in the no-vision conditions, where the spatial targets were not visible, subjects may have decreased their amplitudes to maintain discrete movements longer, which could have cancelled the hypothesized effect. The only effect that depended on vision was the minimum movement time reached. In the vision condition the minimum movement time was ~25 ms longer compared to the no-vision conditions. This difference was probably due to the fact that subject tried harder to maintain the target amplitude while when their eyes were closed they traded amplitude for timing accuracy.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.