Rapid Changes in Movement Representations during Human Reaching Could Be Preserved in Memory for at Least 850 ms

Abstract

Humans adapt to mechanical perturbations such as force fields during reaching within tens of trials. However, recent findings suggested that this adaptation may start within one single trial, i.e., online corrective movements can become tuned to the unanticipated perturbations within a trial. This was highlighted in previous works with a reaching experiment in which participants had to stop at a via-point (VP) located between the start and the goal. A force field was applied during the first and second parts of the movement and then occasionally unexpectedly switched off at the VP during catch trials. The results showed an after-effect during the second part of the movement when participants exited the VP. This behavioural result was interpreted as a standard after-effect, but it remained unclear how it was related to conventional trial-by-trial learning. The current study aimed to investigate how long do such changes in movement representations last in memory. For this, we have studied the same reaching task with VP in two situations: one with very short residing time in the VP and the second with an imposed minimum 500ms dwell time in the VP. In both situations, during the unexpected absence of the force field after VP, after-effects were observed. This suggests that online corrections to the internal representation of reach dynamics can be preserved in memory for around 850ms of resting time on average. Therefore, rapid changes occurring within movements can thus be preserved in memory long enough to influence trial-by-trial motor adaptation. SIGNIFICANCE STATEMENT Recent studies suggested that adaptive feedback control happens within a reach movement and the feedback responses are tuned specifically to the single-trial perturbation. Here we show that these feedback mediated changes in movement representations can last for around 850ms and are available to reproduce the characteristics of the newly acquired correction process. Current data replicate previous studies showing that feedback corrections are associated with changes 3 in online representations, and demonstrate that these changes are preserved in memory long enough to be an important component of standard trial-by-trial adaptation.

Savings in human force field learning supported by feedback adaptation

“…Then we compared the EMG in baseline trials with the first trial in adaptation and readaptation blocks for each participant and found no significant difference (repeated measures ANOVA for PEC: F(2,30)=0.26;p=0.76, ;BF =24.31 and for DELT: F(2,30) =0.61, p=0.54, ; BF = 16.91, strong evidence for the null hypothesis that muscles activities are equal). This supports the argument that the better reach control in the first trial in readaptation blocks was not due to any co-contraction modulating the limb intrinsic properties, instead, it involved a feedback adaptation component that was described previously (Crevecoeur et al, 2020a; Mathew et al, 2020).…”

“…All extracted parameters from the very first relearning trials presented the features of more adapted movements. If we can demonstrate that there was no anticipation of the force field at the beginning of the readaptation phase, that is the washout was complete, then the improvement in the first relearning trials must correspond to an adaptation of online feedback control (Crevecoeur et al, 2020b, 2020a; Mathew et al, 2020). The foregoing analysis shows that it was indeed the case.…”

“…Since the commanded force was a function of hand forward velocity, we could interpret the increased correlation across trials as the tuning of hand force with the hand forward velocity (Fig 2d), which is a marker of adaptation (Crevecoeur et al, 2020a, 2020b; Mathew et al, 2020). Thus, both the reduction in pathlength and the increase in correlation reached their asymptotes faster, which highlighted the presence of savings.…”

“…The path length and maximum lateral deviation are related, and they are minimum for straight movements. The lateral target overshoot reflects the absolute magnitude of the interaction force at the handle near to the target, ie, the peak terminal force and they are associated with adaptation of feedback control (Crevecoeur et al, 2020b, 2020a; Mathew et al, 2020), and the correlation between commanded and measured forces was also previously demonstrated as an indicator of adaptation. The exponential model has the following form: and we regressed the 3 free parameters a, b and c, which correspond to the amplitude of adaptation change, adaptation rate and asymptotic performance, respectively.…”

“…First, it is clear that the time-varying force fields recruit and adapt the feedback control system (Wagner and Smith, 2008; Yousif and Diedrichsen, 2012; Cluff and Scott, 2013; Joiner et al, 2017; Maeda et al, 2018). Additionally, recent studies highlighted the importance of online adaptive control in reaching movements and argued that rapid feedback adaptation contributed to the trial-by-trial adaptation (Crevecoeur et al, 2020b, 2020a; Mathew et al, 2020). Moreover, changes in long-latency feedback responses to perturbations paralleled the fast learning timescale (Smith et al, 2006; Coltman et al, 2019; Coltman and Gribble, 2020), which must be involved if an increase in learning rate occurs over the course of few tens of trials.…”