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The Decay of Motor Memories Is Independent of Context Change Detection.

Brennan AE, Smith MA - PLoS Comput. Biol. (2015)

Bottom Line: When the error signals that guide human motor learning are withheld following training, recently-learned motor memories systematically regress toward untrained performance.However, a recently-proposed alternative posits that even recently-acquired motor memories are intrinsically stable, decaying only if a change in context is detected.Our results suggest that the decay of motor memories is an intrinsic feature of error-based learning that does not depend on context change detection.

View Article: PubMed Central - PubMed

Affiliation: School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America.

ABSTRACT
When the error signals that guide human motor learning are withheld following training, recently-learned motor memories systematically regress toward untrained performance. It has previously been hypothesized that this regression results from an intrinsic volatility in these memories, resulting in an inevitable decay in the absence of ongoing error signals. However, a recently-proposed alternative posits that even recently-acquired motor memories are intrinsically stable, decaying only if a change in context is detected. This new theory, the context-dependent decay hypothesis, makes two key predictions: (1) after error signals are withheld, decay onset should be systematically delayed until the context change is detected; and (2) manipulations that impair detection by masking context changes should result in prolonged delays in decay onset and reduced decay amplitude at any given time. Here we examine the decay of motor adaptation following the learning of novel environmental dynamics in order to carefully evaluate this hypothesis. To account for potential issues in previous work that supported the context-dependent decay hypothesis, we measured decay using a balanced and baseline-referenced experimental design that allowed for direct comparisons between analogous masked and unmasked context changes. Using both an unbiased variant of the previous decay onset analysis and a novel highly-powered group-level version of this analysis, we found no evidence for systematically delayed decay onset nor for the masked context change affecting decay amplitude or its onset time. We further show how previous estimates of decay onset latency can be substantially biased in the presence of noise, and even more so with correlated noise, explaining the discrepancy between the previous results and our findings. Our results suggest that the decay of motor memories is an intrinsic feature of error-based learning that does not depend on context change detection.

No MeSH data available.


Related in: MedlinePlus

The effect of drift in the retention period on the individual-level delay estimates.We hypothesized that the poor fitting and large delays we found in some subjects in Fig 6D were due to random drifting noise in the retention period. (A) Two example subjects from the zEC shooting movement experiment show pronounced drift (large persistent deviations) in the retention period. (B-C) The autocorrelation function (B) at lag τ represents the raw correlation between trials separated by τ, i.e. between trial t and t−τ. The partial autocorrelation function (C) measures the correlations for trials separated by τ adjusting for the effects of the intermediate trials (see Methods). Independent noise will have autocorrelation and partial autocorrelation functions equal to zero. The red and blue traces from experiments 1 (vEC) and 2 (zEC) have autocorrelations consistently greater than zero, thus showing there is correlated noise (drift) present during the retention period data. The black lines are the result of a simulation designed to match the correlation structure of the data. The simulations included 60 subjects, each decaying with zero delay, and with individual differences in decay depth, decay rate, and noise level (see Methods). We then fit these simulations with delayed exponentials to determine the effect of the drift on the resulting distribution of delay estimates. (D) Two example simulated subjects show realistic drifting behavior, comparable to that in panel A. (E) Both the simulations with and without drift have similar histograms to the experimental data for decay depth, decay rate, and standard deviation of noise during the retention period (retention noise). As expected, the partial autocorrelation function at lags 1–3 (PAC1–PAC3) are different for the drift and no-drift simulations with the drift simulation matching the data. The simulation with drift results in delay estimates that have a distribution similar to the experimental data: largely centered at zero with a wide spread and several subjects with very large delay estimates. In contrast, the simulation without drift has a much narrower distribution of delays and fewer large delay estimates. This shows that the amount of drift present in the data is capable of causing best-fit delays to be very large even when the true delay is zero.
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pcbi.1004278.g007: The effect of drift in the retention period on the individual-level delay estimates.We hypothesized that the poor fitting and large delays we found in some subjects in Fig 6D were due to random drifting noise in the retention period. (A) Two example subjects from the zEC shooting movement experiment show pronounced drift (large persistent deviations) in the retention period. (B-C) The autocorrelation function (B) at lag τ represents the raw correlation between trials separated by τ, i.e. between trial t and t−τ. The partial autocorrelation function (C) measures the correlations for trials separated by τ adjusting for the effects of the intermediate trials (see Methods). Independent noise will have autocorrelation and partial autocorrelation functions equal to zero. The red and blue traces from experiments 1 (vEC) and 2 (zEC) have autocorrelations consistently greater than zero, thus showing there is correlated noise (drift) present during the retention period data. The black lines are the result of a simulation designed to match the correlation structure of the data. The simulations included 60 subjects, each decaying with zero delay, and with individual differences in decay depth, decay rate, and noise level (see Methods). We then fit these simulations with delayed exponentials to determine the effect of the drift on the resulting distribution of delay estimates. (D) Two example simulated subjects show realistic drifting behavior, comparable to that in panel A. (E) Both the simulations with and without drift have similar histograms to the experimental data for decay depth, decay rate, and standard deviation of noise during the retention period (retention noise). As expected, the partial autocorrelation function at lags 1–3 (PAC1–PAC3) are different for the drift and no-drift simulations with the drift simulation matching the data. The simulation with drift results in delay estimates that have a distribution similar to the experimental data: largely centered at zero with a wide spread and several subjects with very large delay estimates. In contrast, the simulation without drift has a much narrower distribution of delays and fewer large delay estimates. This shows that the amount of drift present in the data is capable of causing best-fit delays to be very large even when the true delay is zero.

Mentions: We hypothesized that both the poor fitting and the large estimated delays were caused by random drifting noise in adaptation levels during the retention period, as such drift is readily apparent in many subjects’ data, including all example subjects shown in Figs 6B and 7A. The vEC data in Fig 6 had the average response to the vEC sequence subtracted from the individual data, so the apparent drift is not likely to be driven by vEC sequence specific components, although individual differences in learning rates and stiffness could cause some residual vEC-specific patterns to remain. However, drift was also clearly present in zEC subjects (Fig 7A) who did not have errors during the retention period, so the observed drift could not be merely explained by the presence of an externally-imposed error sequence.


The Decay of Motor Memories Is Independent of Context Change Detection.

Brennan AE, Smith MA - PLoS Comput. Biol. (2015)

The effect of drift in the retention period on the individual-level delay estimates.We hypothesized that the poor fitting and large delays we found in some subjects in Fig 6D were due to random drifting noise in the retention period. (A) Two example subjects from the zEC shooting movement experiment show pronounced drift (large persistent deviations) in the retention period. (B-C) The autocorrelation function (B) at lag τ represents the raw correlation between trials separated by τ, i.e. between trial t and t−τ. The partial autocorrelation function (C) measures the correlations for trials separated by τ adjusting for the effects of the intermediate trials (see Methods). Independent noise will have autocorrelation and partial autocorrelation functions equal to zero. The red and blue traces from experiments 1 (vEC) and 2 (zEC) have autocorrelations consistently greater than zero, thus showing there is correlated noise (drift) present during the retention period data. The black lines are the result of a simulation designed to match the correlation structure of the data. The simulations included 60 subjects, each decaying with zero delay, and with individual differences in decay depth, decay rate, and noise level (see Methods). We then fit these simulations with delayed exponentials to determine the effect of the drift on the resulting distribution of delay estimates. (D) Two example simulated subjects show realistic drifting behavior, comparable to that in panel A. (E) Both the simulations with and without drift have similar histograms to the experimental data for decay depth, decay rate, and standard deviation of noise during the retention period (retention noise). As expected, the partial autocorrelation function at lags 1–3 (PAC1–PAC3) are different for the drift and no-drift simulations with the drift simulation matching the data. The simulation with drift results in delay estimates that have a distribution similar to the experimental data: largely centered at zero with a wide spread and several subjects with very large delay estimates. In contrast, the simulation without drift has a much narrower distribution of delays and fewer large delay estimates. This shows that the amount of drift present in the data is capable of causing best-fit delays to be very large even when the true delay is zero.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4482542&req=5

pcbi.1004278.g007: The effect of drift in the retention period on the individual-level delay estimates.We hypothesized that the poor fitting and large delays we found in some subjects in Fig 6D were due to random drifting noise in the retention period. (A) Two example subjects from the zEC shooting movement experiment show pronounced drift (large persistent deviations) in the retention period. (B-C) The autocorrelation function (B) at lag τ represents the raw correlation between trials separated by τ, i.e. between trial t and t−τ. The partial autocorrelation function (C) measures the correlations for trials separated by τ adjusting for the effects of the intermediate trials (see Methods). Independent noise will have autocorrelation and partial autocorrelation functions equal to zero. The red and blue traces from experiments 1 (vEC) and 2 (zEC) have autocorrelations consistently greater than zero, thus showing there is correlated noise (drift) present during the retention period data. The black lines are the result of a simulation designed to match the correlation structure of the data. The simulations included 60 subjects, each decaying with zero delay, and with individual differences in decay depth, decay rate, and noise level (see Methods). We then fit these simulations with delayed exponentials to determine the effect of the drift on the resulting distribution of delay estimates. (D) Two example simulated subjects show realistic drifting behavior, comparable to that in panel A. (E) Both the simulations with and without drift have similar histograms to the experimental data for decay depth, decay rate, and standard deviation of noise during the retention period (retention noise). As expected, the partial autocorrelation function at lags 1–3 (PAC1–PAC3) are different for the drift and no-drift simulations with the drift simulation matching the data. The simulation with drift results in delay estimates that have a distribution similar to the experimental data: largely centered at zero with a wide spread and several subjects with very large delay estimates. In contrast, the simulation without drift has a much narrower distribution of delays and fewer large delay estimates. This shows that the amount of drift present in the data is capable of causing best-fit delays to be very large even when the true delay is zero.
Mentions: We hypothesized that both the poor fitting and the large estimated delays were caused by random drifting noise in adaptation levels during the retention period, as such drift is readily apparent in many subjects’ data, including all example subjects shown in Figs 6B and 7A. The vEC data in Fig 6 had the average response to the vEC sequence subtracted from the individual data, so the apparent drift is not likely to be driven by vEC sequence specific components, although individual differences in learning rates and stiffness could cause some residual vEC-specific patterns to remain. However, drift was also clearly present in zEC subjects (Fig 7A) who did not have errors during the retention period, so the observed drift could not be merely explained by the presence of an externally-imposed error sequence.

Bottom Line: When the error signals that guide human motor learning are withheld following training, recently-learned motor memories systematically regress toward untrained performance.However, a recently-proposed alternative posits that even recently-acquired motor memories are intrinsically stable, decaying only if a change in context is detected.Our results suggest that the decay of motor memories is an intrinsic feature of error-based learning that does not depend on context change detection.

View Article: PubMed Central - PubMed

Affiliation: School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America.

ABSTRACT
When the error signals that guide human motor learning are withheld following training, recently-learned motor memories systematically regress toward untrained performance. It has previously been hypothesized that this regression results from an intrinsic volatility in these memories, resulting in an inevitable decay in the absence of ongoing error signals. However, a recently-proposed alternative posits that even recently-acquired motor memories are intrinsically stable, decaying only if a change in context is detected. This new theory, the context-dependent decay hypothesis, makes two key predictions: (1) after error signals are withheld, decay onset should be systematically delayed until the context change is detected; and (2) manipulations that impair detection by masking context changes should result in prolonged delays in decay onset and reduced decay amplitude at any given time. Here we examine the decay of motor adaptation following the learning of novel environmental dynamics in order to carefully evaluate this hypothesis. To account for potential issues in previous work that supported the context-dependent decay hypothesis, we measured decay using a balanced and baseline-referenced experimental design that allowed for direct comparisons between analogous masked and unmasked context changes. Using both an unbiased variant of the previous decay onset analysis and a novel highly-powered group-level version of this analysis, we found no evidence for systematically delayed decay onset nor for the masked context change affecting decay amplitude or its onset time. We further show how previous estimates of decay onset latency can be substantially biased in the presence of noise, and even more so with correlated noise, explaining the discrepancy between the previous results and our findings. Our results suggest that the decay of motor memories is an intrinsic feature of error-based learning that does not depend on context change detection.

No MeSH data available.


Related in: MedlinePlus