Ethics statement

The University of Leeds School of Psychology ethics committee granted ethical approval for this study. Written informed consent was obtained for all participants via the web-based sign-up form.

Participants

A total of 20 participants (age 19–35, median 23 years; 12 male) each completed one session per day of an electromyographic control task for five consecutive days. Participants completed a pre-session questionnaire describing their prior experience with playing computer games, playing musical instruments, participating in sports, and driving. This information was not used to select participants for inclusion or exclusion from the study. All participants had no known neurological disorder and provided written consent through the online study sign-up process.

Participants were assigned to one of two conditions, labelled “congruent” (10 participants, 6 male) or “incongruent” (10 participants, 6 male). The two groups completed identical sessions but each used a differently configured myoelectric interface. During post-hoc analysis, one male participant was excluded from each group due to having extremely large mean cursor amplitude (more than double the maximum required amplitude) or mean cursor peak time after the end of each trial. The presented analyses use the remaining 18 participants unless otherwise stated.

Participants completed the Edinburgh handedness inventory, and we allowed participation regardless of handedness. Two participants (one male, one female) from the incongruent group and one female participant from the congruent group were identified as strongly left-handed.

Experiment setup

Participants sat on a chair with both feet placed on blocks and arms resting on armrests (Fig 1). Abduction of the fingers of the right hand and dorsiflexion of the left foot were limited using Velcro straps. The straps were placed around the fingers of the right hand and over the bridge of the left foot. Participants self-adjusted each strap to the maximum comfortable tension at the start of each session. Participants viewed visual feedback for the experiment tasks on a computer monitor (Dell AW2521HFLA, 24.5-inch, 1080 × 1920 pixels, 244Hz) at a distance of approximately 1.1 metres. The framerate of the task feedback was consistently above 110Hz.

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Fig 1.

Experiment setup, EMG interface, and task design (A) Participants sat in a chair 1.1m from the computer screen on which task feedback was displayed. A bipolar EMG channel was recorded in real time from the right abductor digiti minimi (yellow) and the left tibialis anterior (green), and used to control the horizontal velocity of a cursor. (B) The smoothed and scaled EMG signals generated by the hand, h(t), and shin, s(t), were combined in a weighted sum to produce the unscaled cursor velocity. The output cursor trajectory relative to centre was given by the integral of this velocity signal multiplied by a constant velocity scale factor, α = 2500 pixels∙s⁻¹. (C) Each trial of the main task proceeded through several stages, as described in the main text. In training trials, feedback of targets hit (green circles) and produced cursor trajectory (green curve) were given for 3s. On probe trials, no feedback was given, and participants rested for 3s. (D) Six different path shapes were used in the main task, named according to their direction (Right or Left) and magnitude (1, 2, or 3). (E) The schedule of trial blocks used in each session of the study. On session 1 an initial instructions block was also included at the beginning.

https://doi.org/10.1371/journal.pcbi.1012492.g001

Two bipolar EMG channels were recorded in real-time at 2048Hz using a biosignal amplifier (OTBioelettronica Quattrocento) and custom interface code written in Python. One channel was recorded from the left shin (tibialis anterior) and one from the right hand (abductor digiti minimi) of each participant. The shin electrodes were centred at points 7cm apart along a vertical line approximately 6cm below the tibial tuberosity and 2cm to the lateral side of the anterior margin of the tibia. The hand electrodes were centred at points 3cm apart approximately equidistant from the pisiform bone and the base of the fifth metacarpal bone. A reference electrode was placed on the ulnar styloid process of the right wrist. Locations of the electrodes were marked on the skin in ink and re-marked each session to allow consistent placement of the electrodes. All electrodes were self-adhesive solid gel type (Skintact F-261, 26mm diameter), and were further secured using micropore tape (hand electrodes) or kinesiology tape (shin electrodes).

Experiment tasks

Each session comprised a series of calibration tasks and experiment tasks. Detailed instructions were presented to the participants through simultaneous on-screen text and audio narration. Instruction transcripts are available in the experiment data repository. All sessions were identical in structure except for the addition of a single practice block and more detailed instructions in the first session.

EMG interface

To create a low-latency control signal using the bipolar EMG signals, on each frame, a weighted average of the latest samples of rectified EMG data was computed for each channel. Two variants of the interface were used for different tasks. For the free movement and path following tasks, a weighted average of the rectified bipolar EMG was taken using a 256-sample triangular smoothing kernel which assigned greatest weight to the most recent EMG sample. For the power cycle task, a longer uniformly weighted kernel of 4096 samples was used. In both cases, the noise threshold for each channel (as identified during calibration) was subtracted from its smoothed EMG signal, and resulting values less than zero were set to zero. The thresholded and smoothed EMG channels were then scaled such that 35% of the participant’s maximum power level produced an output signal of 1. We refer to the resulting time-varying signals as the channel profiles, h(t) and s(t), from the hand and shin muscle respectively (Fig 1B).

Two variants of the thresholding method were used for different tasks. For the main free movement and path-following tasks, a log-normal distribution was fitted to the baseline EMG data recorded during calibration, and the noise threshold was set at the 99.99th percentile of this distribution plus 1% of the EMG power at maximal contraction. This thresholding method provided robustness to noise without excessively reducing the dynamic range of the control signals. For the power cycle task, only the 1% of maximal contraction threshold was used. This was chosen to prevent the introduction of a noticeable “dead zone” in the controller, given that position control rather than velocity control was used in the power-cycle task.

For the path-following task, each control signal was linearly mapped to a one-dimensional velocity value, such that a control signal of 1 resulted in a cursor velocity of 2500 pixels per second. Unscaled control signals with magnitude greater than 1 were not capped. The entire screen width could therefore be traversed in 0.432 seconds without exceeding 35% of maximal power. The two channels each controlled velocity in opposite directions: one for positive (rightward) velocity, one for negative (leftward) velocity. A weighted sum of the two channels’ control signals then determined the unscaled velocity of the cursor. The integral of this unscaled velocity signal (multiplied by 2500 pixels per second) gives the cursor trajectory used to complete the path-following task.

For participants from the congruent group, the left shin was mapped to leftward cursor velocity (i.e. negative velocity values), while the right hand was mapped to positive velocity (w₁ = 1 and w₂ = −1 in Fig 1B). For participants in the incongruent group, the signs of the velocity for each channel were flipped (w₁ = −1 and w₂ = 1 in Fig 1B). The same laterality of electrode placement (left shin and right hand) was used for both groups.

Data analysis

Channel peak feature model.

For the peak amplitudes and times of each channel, we used a simpler model, transforming the data in the same way as for the RMS error model. This model was applied separately to data for each channel, again rejecting trials in which the order of channel peak activation was incorrect.

(2)

Where μ_p,s,d are the specific contributions to mean RMS for each participant, scale condition, and day (session); σ_p,s,d is the specific standard deviation for each participant, scale condition, and day; and y_i is the (appropriately transformed) channel peak amplitude (or time) of one observed trial.

Channel peak time correlation model.

To estimate the correlation between peak times in the first and second-activating channels, we used a multivariate normal model. Prior to model fitting, we subtracted the first channel activity start time from the peak times of both channels in each trial, and re-centred the resulting dataset to have sample mean of zero.

(3)

Where μ₁ and μ₂ are the prior means for the two-dimensional multivariate normal; σ₁ and σ₂ are the prior standard deviations for the covariance matrix; R is an LKJ prior for the unit standard deviation covariance matrix; S is the prior over covariance matrices, with scaled standard deviation; and p₁ and p₂are the channel peak times for the first and second-activating channels, with initiation time subtracted and re-centred to have sample mean of 0.

Performance on trained conditions improved gradually over multiple sessions

Participants controlled the horizontal velocity of a computer cursor using bipolar EMG signals recorded from a muscle of the right hand (abductor digiti minimi) and a muscle of the left shin (tibialis anterior). Each participant was randomly assigned to one of two groups: congruent or incongruent. For the congruent group, each channel affected cursor velocity in the direction matching the laterality of the source muscle on the body (i.e., left shin to leftward velocity, and right hand to rightward velocity). For the incongruent groups, the muscles’ laterality was unchanged, but the direction of their velocity contributions was reversed.

In the main task, participants were instructed to move the cursor to hit a series of circular targets that descended at a constant speed down the screen. Online visual feedback of cursor position was not provided, but post-trial feedback of target hits and output cursor trajectory was provided during training blocks.

To determine whether the shape of participants’ cursor trajectories improved independent of their timing relative to the target trajectory, we aligned the amplitude peaks of the output and target trajectories and computed performance measures based on these peak-aligned trajectories. Over five sessions, participants’ mean peak-aligned target hit percentages for the trained trajectories (R₁ and R₃) showed a steady but statistically unreliable increase (Fig 2B). This is likely due to qualitative improvements in cursor trajectory shape which do not consistently result in more targets being hit, as demonstrated by the shape of session 5 median cursor trajectories compared to those of session 1 (Fig 2A, dark curves).

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Fig 2. Example cursor trajectories and peak-aligned target hits.

(A) Session 1 and 5 median cursor trajectories per participant (faint lines) and across participants (dark lines) in the conditions trained with post-trial feedback (R₁ and R₃). Inset black circles show the target points (to scale) for each trial condition. (B) Peak-aligned target hit percentages for the for the trained trial conditions. Lines show cross-participant medians within each condition group, and points show per-session means for individuals. Session 1 to session 5 increase in median hit percentage was 6.8% (SD = 7.6%) for congruent R₁; 4.4% (SD = 5.0%) for incongruent R₁; 2.5% (SD = 3.9%) for congruent R₃; 2.6% (SD = 3.3%) for incongruent R₃.

https://doi.org/10.1371/journal.pcbi.1012492.g002

To provide a more sensitive measure of trajectory shape quality, we computed root-mean-squared error between the peak-aligned output and target trajectories. Marginalising across participants, we observed statistically robust reductions in the posterior mean and standard deviation of the peak-aligned RMS error between sessions 1 and 5 (Fig 3, top left plots). These reductions are less robust for the incongruent group, likely because this group had lower variability in session 1 than did the congruent group: Bayes factors in favour of the incongruent group having lower σ(ϵ) on session 1 than the congruent group are 11.23 for R₁, and 5.58 for R₃. Bayes factors in favour of the incongruent group having lower ϵ on session 1 than the congruent group are 1.09 for R₁, and 1.47 for R₃. Together, these results are consistent with improvements in mean trajectory shape and reductions in the variability of the generated trajectory shape.

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Fig 3. Task performance on the trained and untrained conditions.

Per-participant posterior means (larger plots, faint points) and standard deviations (smaller plots, faint points) of peak-aligned RMS error (top row) and trajectory peak time (bottom row) in the generated cursor trajectories. Dotted horizontal line in the lower plots represents the ideal trajectory peak time. Marginal means across participants (dark points and lines) show a steady reduction in RMS and peak time across sessions for the trained conditions (R₁ and R₃). Only peak timing shows a consistent improvement across participants for the untrained conditions (L₁ and L₃). Inset numbers are Bayes factors in favour of a reduction in marginal mean statistic from session 1 to session 5 for each of the two participant groups individually. With the exception of the trajectory shape for untrained conditions, Bayes factors for both the congruent (blue) and incongruent groups (red) favour improvements in performance and reductions in variability with respect to both shape and peak timing.

https://doi.org/10.1371/journal.pcbi.1012492.g003

A separate feature of performance in the path-following task is the temporal alignment between the generated cursor trajectory and the target trajectory. To quantify how well participants aligned their outputs with the target, we computed the times at which each trajectory reached its largest amplitude (i.e. its peak) in comparison to the ideal peak time (Fig 3, bottom row). The per-participant and cross-participant marginal mean peak times show a statistically robust improvement, as measured by Bayes factors in favour of a reduction from session 1 to session 5. Variability in peak timing may also have reduced between sessions 1 and 5, but this is less statistically reliable for the incongruent group than the congruent group.

Improvements in peak timing but not trajectory shape transferred to the untrained leftward conditions

To further clarify which learning processes resulted in the observed performance improvements on the trained paths, we assessed how these improvements transferred to the leftward conditions L₁ and L₃. These conditions were untrained (i.e. practiced without post-trial visual feedback) and required reversed order of input channel activation compared to the rightward conditions. As such, if the learning for the rightward conditions was specific to the trained order of muscle activation, we would expect little transfer to the leftward conditions.

Despite robust reductions in mean peak-aligned RMS for R₁ and R₂ between sessions 1 and 5, we found relatively weaker evidence in favour of a reduction for L₁ and L₃ in the congruent group and weak evidence in favour of no change or an increase in the incongruent group (Fig 3, top right). The standard deviation of RMS for the leftward path showed similarly weak evidence of a reduction for both congruence conditions, with Bayes factors around a quarter the magnitude of those observed for R₁ and R₃.

In contrast, although the leftward path conditions require a different order of input channel activation than the trained rightward conditions, there is robust evidence in favour of an improvement in mean peak timing and a reduction in peak time standard deviation in the leftward conditions for the congruent participants (Fig 3, bottom right). The Bayes factors for the incongruent group also favour an improvement in peak time and a reduction in standard deviation of peak time, with evidence approximately as strong as in the corresponding trained rightward conditions.

Performance improvements transferred to the untrained medium amplitude rightward condition

The above analyses demonstrate that, despite the unfamiliarity of the task and interface, participants were able to execute the task and, on average, improved their performance on trained conditions over the five sessions. We next sought to determine whether this learning could have arisen due to the formation of habitual responses to the trained trajectories, rather than emergence of a new controller as is purported to occur in de novo sensorimotor skill learning. To achieve this, we assessed whether the performance improvements observed for the trained conditions arose concurrently in the untrained task conditions. We reasoned that, while de novo skill learning could support condition-general improvements in performance, habit formation should not [16].

The medium-magnitude rightward path (R₂) can be executed by generating EMG outputs with an intermediate magnitude between those used for the small and large rightward paths. Unlike the other three untrained conditions, R₂ also requires the same order of channel activation as the trained paths, determined by the participants’ congruence conditions. All performance results for R₂ were qualitatively similar to those for R₁ and R₃: there was a statistically unreliable increase in peak-aligned target hit percentage across the sessions (Fig 4A); the median shape of the output cursor trajectories on “no-feedback” trials improved from session 1 to session 5 (Fig 4B); and we found statistically robust Bayes factors in favour of reductions in the mean and standard deviation of peak-aligned RMS (Fig 4C) and trajectory peak time (Fig 4D) between sessions 1 and 5.

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Fig 4. Performance improvements transferred to the untrained R₂ condition.

(A) Peak-aligned target hit percentage for R₂. Session 1 to 5 increase is 3.8% (SD = 4.5%) for congruent; 3.3% (SD = 3.5%) for incongruent (B) Changes in median R₂ trial trajectory shape for individual participants (faint lines) and across participants (dark lines). (C) Changes in the logarithm of the posterior mean (top) and standard deviation (bottom) of peak-aligned RMS error in cursor trajectory for individual participants (faint points) and marginalising across participants (dark lines). Inset Bayes factors are in favour of a reduction in the marginal values from session 1 to session 5. (D) Similar to C, but for trajectory peak time. All data is from “no-feedback” trials.

https://doi.org/10.1371/journal.pcbi.1012492.g004

Although performance on R₂ improved according to the selected measures, these trends do not show that the outputs being generated for the R₂ trials were specific to that condition. Performance improvements for this condition could also arise if participants used the same outputs for R₂ as for R₃ or R₁, as outputs suitable for these conditions will approximate the trajectory shape required by the R₂ condition. It is unclear from inspection of the median cursor trajectories for the R₂ condition (Fig 4B) whether the generated outputs are distinct from those of the other two conditions. To clarify this, we consider the per-participant distributions of input channel peak amplitudes (not to be confused with cursor trajectory peak amplitudes). These distributions show idiosyncratic differences dependent upon path condition. For some participants there is a clear difference in the amplitude distributions for each of the three conditions in session 5 (S2A Fig), while for other participants the medium rightward trial distribution in session 5 almost perfectly coincides with those of the large or small rightward trial conditions (S2B Fig). This suggests that, while some participants generated condition-specific outputs, others simply re-used one or both outputs from the trained conditions to execute the R₂ condition. Grouping participants by whether their peak channel amplitudes had distinguishable or indistinguishable distributions for the three rightward trial conditions, we observed in both cases statistically robust reductions in RMS error (S2C Fig), trajectory peak time (S2D Fig), and the standard deviation thereof. This demonstrates that transfer of performance improvements to the untrained rightward condition was achieved either by production of untrained intermediate outputs or by simple re-use of outputs suited to other conditions.

Per-channel features were consistently similar for rightward and leftward paths of equal magnitude

The patterns of transfer described in the preceding section suggest that the learning processes responsible for determining trajectory shape and peak timing are partly independent. To clarify how these processes give rise to performance improvements, we now assess learning-related changes in the properties of the per-channel control signals.

Direct comparison of the per-participant mean channel profiles for leftward and rightward trial conditions suggests that the choice of profile shapes played an important role in both effects. In particular, the mean profiles for a given channel tend to be very similar regardless of whether that channel is activated in the context of a leftward or rightward path trial (Fig 5A, light versus dark traces; Data for all participants is shown in S7 Fig). This re-use of output profiles across directions has different consequences for the timing and the shape of the resulting cursor trajectory.

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Fig 5. Reuse of idiosyncratic channel profiles explains the observed patterns of generalisation.

(A) Mean peak-aligned per-participant channel profiles in R₃ trials (dark lines) and L₃ test trials in session 5, for four example participants. Dotted lines indicate the mean peak amplitudes for the R₃ trials. Red traces correspond to incongruent group participants, and blue traces correspond to congruent group participants. Participants produced very similar per-channel outputs for the leftward and rightward path conditions. Results for other sessions and path magnitudes are qualitatively similar. (B) Top row shows trends in cursor trajectory posterior mean RMS error for the small- and large-amplitude leftward paths as in Fig 3, but grouping participants by whether they improved from session 1 to session 5 (yellow circles) or did not improve (turquoise squares). Participants were classified as improving if their Bayes factor in favour of reduction in posterior mean RMS from session 1 to session 5 was greater than 3. Participants who showed an improvement for the untrained conditions tended to have smaller and more similar mean peak amplitude in the hand and shin channel than did the non-improvers. (C) An example illustrating transfer of peak timing and non-transfer of trajectory shape to leftward conditions for a participant with different per-channel amplitudes. The example participant’s mean channel profiles for R₃ trials (top left) and for L₃ trials (top right) are very similar within channel but different across channels. For the rightward path condition, the hand channel is activated first, and the shin channel second, while the order is reversed for the leftward path condition. This leads to two different velocity profiles (top, grey lines), even when the peaks of the channels in each condition occur at the same two times. The cursor velocities are integrated and scaled to give the output cursor trajectory. This results in a different cursor trajectory in each condition, even though they used near identical per-channel outputs and relative timing. The timing of the trajectory peak is almost unchanged in each condition, due to the symmetry of the channel profiles.

https://doi.org/10.1371/journal.pcbi.1012492.g005

As the hand and shin channel profiles tend to differ in shape (including with respect to their amplitude), the cursor velocity resulting from taking a weighted sum of the two will differ depending on the order in which the profiles are generated. Consequently, the generated cursor trajectory will differ in shape when the same channel profiles are used in reversed order (Fig 5C). Notably, the timing of the cursor trajectory peak is not as strongly affected by reversing the order of the channels. If each channel profile is approximately symmetrical about its peak and has approximately equal duration, activating the two channels at the same times but in reversed order will result in a cursor trajectory that reaches its peak at approximately the same time.

Consistent with the preceding explanation, we observed that participants whose trajectory shape improved for the small and large-amplitude leftward conditions between sessions 1 and 5 tended to have smaller and more similar channel peak amplitudes (Fig 5B). Conversely, participants whose trajectory shapes did not improve tended to have larger and less similar channel peak amplitudes. This dissimilarity of channel amplitudes is a sufficient but not necessary condition for a lack of improvement in the leftward cursor trajectory shapes. These observations explain why we observed transfer of improvements in peak timing to the leftward path conditions but did not observe transfer of improvements in cursor trajectory shape to the leftward condition.

Participants did not learn to generate entirely new outputs

The performance improvements observed in the preceding sections are consistent with improved trajectory shape and reduced variability in the generated shape. We now consider whether this was achieved by developing the ability to generate entirely new outputs that could not be generated at the beginning of training.

To determine if the patterns of hand and shin muscle activity in session 5 could already be generated in session 1, we compared the amplitude and peak timing for R1 and R2 in session 5 to those of all paths in session 1. For each trial in session 5, we computed the minimum difference in amplitude between this trial and all others in session 1. For each participant, we then took the 99^th percentile of these trial minima. We reasoned that, if this selected minimum was small, 99% of trials of this scale in session 5 involved channel outputs which could already be generated (to within this small error) in the first session. The same calculation was used for peak timing.

For both amplitude and peak time in both the hand and shin channels, we observe that outputs very similar to those used in session 5 could already be generated during the first session (Fig 6). This suggests that the observed improvements in task performance were not due to the participants learning to generate new per-channel outputs.

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Fig 6. Participants could produce per-channel outputs in session 1 which closely resembled those used in session 5.

(A) Points show the channel peak amplitude of a selected R₃ or R₁ trial from session 5 plotted against that of a trial of any condition from session 1. The trials were selected by computing the minimum difference between the channel amplitude of a given trial in session 5 and the channel amplitudes of all trials in session 1, then selecting from these minimums the pair whose magnitude difference was the 99^th percentile value. Plotted points therefore represent trials in session 5 for which the minimum difference between the session 5 channel peak amplitude and any session 1 channel peak amplitude is greater than 99% of other similarly calculated differences. 99% of other points (not shown) would therefore have even greater similarity between session 5 and session 1 than the shown points. The consistently small difference between the session 5 and session 1 values demonstrates that participants could produce outputs matching the amplitude of those used in session 5 during the first session. (B) As in A, but for per channel peak time. Again, the peak times in session 5 could be generated during the first session.

https://doi.org/10.1371/journal.pcbi.1012492.g006

We note that this similarity is not limited to the trained paths. S6 and S7 Figs show the same analyses repeated for the L₁ and L₃ as well as the R₂ and L₂ conditions, again with high similarity between each session 5 trial and a trial in session 1. Moreover, the similarity is not limited to our choice of features. Using functional principal components analysis (fPCA) we computed a three-dimensional feature space representation of each generated hand and shin profile, and repeated the similarity analysis using these features (S7 Fig). With these features, as with our manually selected ones, the majority of outputs generated in session 5 could already be produced in session 1.

Participants had different condition-specific biases in channel peak amplitude and timing

An alternative explanation for the limited transfer of trajectory shape improvements to the leftward conditions is that these trajectories may be intrinsically more difficult to generate than the corresponding magnitude rightward trajectories. If so, performance on these conditions during the first session should be worse than that of the rightward conditions. We observed no such bias in the difference in log-RMS for rightward and leftward path trials of equal magnitude (S3A Fig), with individual participants instead showing idiosyncratic biases distributed approximately evenly around zero. This suggests that the limited transfer of trajectory shape quality to the leftward conditions is not a consequence of intrinsic differences in difficulty between the two directions.

A condition-dependent bias was observed for trajectory peak time in session 1. Participants from the congruent group tended to have a later peak time for leftward than for rightward trials, while the pattern was reversed for participants from the incongruent group (S3B Fig). This bias is likely a result of differences in the strength of inputs generated by each input channel. If participants tended to activate the shin channel more vigorously than the hand channel, this would result in an earlier trajectory peak when the shin channel was activated first and later when it was activated second. Consequently, the different congruence conditions will show opposite biases in peak time for the leftward and rightward conditions, due to their opposite mappings from muscle laterality to cursor velocity. Several participants showed such a difference in channel peak amplitudes which persisted in session 5 (S5 Fig).

Relative timing of the channel profiles changed little with practice

We next assessed whether the timing of the input channel profiles could have contributed to the observed performance improvements. To generate an appropriately timed cursor trajectory, the input channel profiles must themselves be appropriately timed. This could be achieved by triggering the channel inputs relative to some fixed movement initiation time, or by timing the second-activating channel relative to the first. To check for evidence of the latter case, we estimated the correlation between channel peak times after subtraction of movement initiation time. For the trained conditions, the resulting correlations were reliably positive for all participants in both the first and last sessions, with no consistent change in the posterior mean correlation coefficient between these sessions across participants (Fig 7A). Similar positive correlations are seen for the corresponding leftward paths, suggesting that the strategy of relative timing was consistently applied regardless of task condition, and was not strongly affected by training.

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Fig 7. Channel peaks are timed relative to each other, and inter-peak interval is consistent across sessions.

(A) Scatter plots show the per-participant marginal posterior correlation coefficients between first and second channel peak times in session 1 (horizontal axis) versus those in session 5 (vertical axis). Stacked lines show 99% posterior credible intervals around the session 1 correlation coefficient for each participant. Blue markers are for participants in the congruent group, while red markers are for participants in the incongruent group. (B) Points show posterior mean differences in per-channel peak times (i.e. inter-peak time intervals) for each path scale condition across sessions, in the test blocks. Dotted lines at ±0.21s are ideal inter-peak times. Inset Bayes factors represent evidence in favour of no change in the inter-peak intervals for sessions 1 and 5.

https://doi.org/10.1371/journal.pcbi.1012492.g007

Although the second-activating channel is timed relative to the first-activating channel, the interval between activation of the two channels may vary across sessions without affecting the observed correlations. Improvements in trajectory peak time could therefore have been influenced by changes in the interval between channel activations. To check for such a change, we computed the posterior mean difference in channel peak times for each participant in each session and trial condition. The corresponding Bayes factors broadly support no change in cross-participant marginal mean inter-peak interval between sessions 1 and 5 (Fig 7B).

New controllers from old commands

One possible means of learning a new controller is to develop the ability to generate entirely new task-specific motor commands. The process of generating motor commands could be implemented in various ways, including through spatiotemporal muscle synergies [17–19] or dynamical modes in motor-cortical neural populations [20–23]. While details of neural implementation will determine how the controller is learned, a common behavioural signature of learning to generate new motor commands can be found in each case: the participants develop the ability to generate new spatiotemporal patterns of muscle activity. As we recorded EMG signals from single muscles, and used these to directly control the task state, we were able to assess whether new patterns of muscle activity appeared with practice. Across participants, we found no evidence that the muscle activity generated in the final session was different from that which could already be generated during the first session. As such, we conclude that even if the participants did learn to generate entirely new motor commands, such commands were not necessary to achieve the observed performance improvements.

An alternative means of learning a new controller is to explore the space of pre-existing motor commands and select a set of task-appropriate commands to use in different conditions. Consistent with this explanation, we observed gradual reductions in the standard deviation of both trajectory shape and trajectory peak timing. However, this reduction in variability is also consistent with improvements in the reliability of the selection process. If participants quickly determined which commands to generate for a given task condition, they may still have had to learn to reliably select those commands within the time constraints of the task. The tendency of participants to re-use their idiosyncratic per-channel profiles in the trained and untrained conditions suggests an important role for selection in learning of a new controller. However, it remains unclear from our results whether learning an association between tasks and commands contributed more to developing the new controller than did learning to reliably produce the associated commands.

A related though easily overlooked question for de novo learning is how it balances reuse of existing learning with acquisition of new learning. If new controllers are learned in isolation from existing ones, prior learning cannot be applied to speed up learning of new tasks; when learning to tie our shoelaces and to write by hand, control of the fingers would have to be learned twice. Alternatively, if there is too much overlap between the new and existing controllers, learning one task could lead to changes in performance on the other [24,25]; mastering cursive could help or hinder our ability to tie a bow. In the present study, the tendency of participants to reuse previously learned commands could arise in part from an adaptive bias: preferentially reusing existing learning during learning of new tasks could prevent existing skills from being harmed by modification of their underlying processes. Future studies could more directly assess whether such reuse arises by choice, perhaps owing to the reduced cognitive demand of selecting a well-practiced command, or by necessity, perhaps arising from the basic properties of the neural representation of sensorimotor skill [11,26].

Independence of selection and timing

Another distinctive feature of our results was independent changes in channel profile shape and relative timing. While profile shapes changed with practice, their relative timing remained largely consistent, including in untrained conditions. Previous studies of sequence learning have suggested that, when the elements of the sequence overlap in time, the later elements are likely to be timed relative to the state of the preceding element, rather than relative to a common movement initiation time [27]. In the context of the path-following task, we observed positive correlations in channel peak times in all task conditions, consistent with relative timing.

As the two channels usually have different profiles (Figs 5A and S5), partly due to the different physiological characteristics of the two muscles, using the precise state of one muscle to trigger activation of the other would generally not produce the same timing when the order of the muscles was reversed. Although we concluded that the second-activating channel is timed relative to the first-activating channel during all five sessions, this does not imply that the second-activating channel is timed relative to the intensity of activity in the first-activating channel. Rather, the second-activating channel may have been timed relative to some qualitative feature of the first-activating channel profile, such as its peak or the time at which power in that channel started to reduce after the peak. It should be noted that this feature-relative timing could be achieved during planning or based on feedback received during execution. Further studies will be required to test these possibilities.

The observed independence of channel peak timing and amplitude is consistent with results from neuroimaging studies of discrete sequence learning. For discrete finger presses, the production of ordered output sequences is attributed to a hierarchical representation in which the complete sequence is built up from successively smaller sub-sequences [28,29]. Separate representations of the order and timing of finger press sequences are found in bilateral pre-motor areas during movement preparation, with integrated representations of both order and timing arising in primary motor areas contralateral to the active hand during sequence execution [30–32]. We would anticipate a similar independence in the neural representation of order and timing for the two muscles used in the present study. This suggests a plausible neural basis for independent improvements in the timing and the selection of amplitudes for coordinated motor outputs, though we observed changes in only the latter.

As the second-activating channel is timed relative to the first-activating channel, the peak time of the generated cursor trajectory is influenced by both the time interval between the two channels and the onset time of the first channel. We found little evidence for a change in the average time interval between channel peaks in any condition (Fig 7B). This suggests that the main channel timing-related behavioural feature affecting trajectory peak timing was the timing of the onset of the whole movement. Although the rightward and leftward direction paths require activation of a different muscle at the start of the movement, we observed that improvements in the timing of the cursor trajectory achieved by practising the R₁ and R₃ conditions were preserved in the untrained leftward conditions. This suggests that the mechanisms responsible for onset timing are at least partly independent of the muscle being activated. This may contribute to explaining why improvements in trajectory peak timing transferred to the untrained leftward conditions despite their requiring reversed order of muscle activation relative to the rightward conditions.

Factors limiting performance improvement

Although the observed improvements in trajectory shape and timing for the trained conditions are statistically robust, the magnitude of these improvements is often rather small. Despite extensive training over five sessions, participants were unable to reliably hit more than around 15% of the target points for the trained paths. We suggest that this limited effect size is attributable to two main factors.

Firstly, the intrinsic variability of the EMG interface may have limited participants’ ability to control cursor movements. This possibility is supported by the observed differences in performance on the small and large-magnitude trained paths. As the magnitude of the rectified EMG signal’s envelope is directly related to the variability of the raw EMG signal [33], stronger muscle contractions produce more variable signals than weaker ones. This causes the larger magnitude control signals in our interface to be more variable than smaller magnitude ones, resulting in worse performance on the large-magnitude trained paths compared to the small-magnitude ones. It is plausible that a different type of interface, such as one using limb position or force e.g. [34,35] may support faster or better learning through lower intrinsic variability. While the use of an EMG interface allows convenient access to any muscle of the body–an advantage for studying de novo learning of previously unpractised movements–this advantage should be weighed against the need for precision in the resulting interface.

A second potential reason for the limited learning in our study is the absence of online visual feedback of cursor position during the training trials. As described in Methods, online feedback was restricted to ensure that our study met two important requirements: firstly, that the strategies used by participants to complete training and test trials were consistent; and secondly, that learning during the test trials was limited. Providing online visual feedback during both training and test trials could have resulted in learning during the latter, confounding our analysis of generalisation. Providing online visual feedback during only the training trials could have caused participants to behave differently on these trials compared to the test trials. Importantly, while our decision to limit online visual feedback allowed us to study generalisation of learning to untrained conditions, it could also have biased participants towards reuse of existing motor commands. In the absence of online visual feedback, learning may result in changes to feedforward controllers more than feedback controllers [36]. As such, it is plausible that the observed tendency to reuse existing motor commands would not arise in tasks with online visual feedback and where feedback controllers were used. Future studies may investigate this possibility by comparing learning and generalisation both with and without online visual feedback.

Is this learning “De Novo”?

Together, the above-described results suggest that learning of a new continuous control task can be achieved by improving the selection and timing of outputs that are already in the repertoire of the learner. It remains unclear, however, if this is true of de novo learning tasks in general, or if it is a consequence of specific features of the task used in this study. Compared to previous de novo learning studies, our task has several distinguishing features.

By using a combination of muscles which are not typically coordinated in natural movements, we were able to reduce the influence of prior experience on learning. This contrasts with several previous de novo learning paradigms [5,13,14] in which well-practiced movements were deliberately used to reduce the need for exploratory learning of mapping from body state to task state. The learning observed in our study may therefore have a larger component of exploration, but should also be less affected by interference from pre-existing associations. To empirically assess the influence of this type of interference on learning, we assigned participants to either a congruent or incongruent mapping condition. We observed qualitatively similar patterns of learning in both cases, though participants from the incongruent group tended to have more variable performance throughout. This result demonstrates that prior associations between body state and task goals can affect learning, even for previously unpractised tasks.

Another distinctive feature of our task is that the EMG interface controlled the velocity of the output cursor rather than its position. Using the velocity control interface, the path-following task could be completed by generating a pair of appropriately timed pulses of EMG activity. The mechanisms involved in learning to generate a well-timed sequence of discrete motor outputs are likely to differ from those involved in learning more continuous control tasks [37]. As such, although the learning observed in this study meets the definition of de novo learning, the mechanisms supporting learning in the path-following task may not be identical to those observed in other de novo learning studies. It is already well understood that sensorimotor learning is supported by multiple interacting learning processes [38], and we suggest that de novo learning should be similarly understood as arising from a range of learning mechanisms, differently recruited by different tasks.

Our results demonstrate that learning a new controller for an unfamiliar coordinated control task need not involve learning to generate entirely new motor commands. Instead, independent changes in the timing and selection of already available commands may be sufficient to support the production of novel movements.