Reliable Detection of Implicit Waveform-Specific Learning in Continuous Tracking Task Paradigm

Abstract

Implicit waveform-specific (IWS) learning during a typical continuous tracking task paradigm has been reported for decades, as evidenced by better tracking improvement on the repeated segment of a specific target waveform than random segments. However, the occurrence of the IWS learning in such a task paradigm has been challenged by several unsuccessful results in recent literature. This research concerns reliable detection of the induced IWS learning and to this end, proposes to use the similarity between the cursor and the target along the direction corresponding to the waveform pattern as the performance measure. A 3-day experiment designed with full examination on IWS learning including a practice phase, an immediate test phase and a consolidation test phase after 24 hours was conducted to validate the feasibility and sensitivity of the Pearson’s correlation coefficient on the vertical movement r v in this study. Experiment results indicate that r v is more sensitive in detecting the IWS learning in all phases compared to the conventional root mean square error (RMSE) performance measure. The findings confirm the importance of the performance measure in implicit learning research and the similarity measure in accordance with the waveform could be promising for waveform-specific learning detection in this paradigm.

Alpha down-regulation neurofeedback training effects on implicit motor learning and consolidation

“…tracking the target with a cursor) and waveformspecific learning (i.e. characteristics of repeated waveform pattern) [13,14]. GMS learning, referring to the acquisition of expertise with the general requirement of the task, appears when tracking both the random and repeated segments [3,13,14].…”

“…characteristics of repeated waveform pattern) [13,14]. GMS learning, referring to the acquisition of expertise with the general requirement of the task, appears when tracking both the random and repeated segments [3,13,14]. Since the random segments have no waveform-specific learning, the improved tracking performance in the random segments across practice blocks can be measured as GMS learning [3,13].…”

“…GMS learning, referring to the acquisition of expertise with the general requirement of the task, appears when tracking both the random and repeated segments [3,13,14]. Since the random segments have no waveform-specific learning, the improved tracking performance in the random segments across practice blocks can be measured as GMS learning [3,13]. When the middle random segment in the transfer test has a sharply reduced tracking performance compared to the middle repeated segment in the retention test, proves the successful learning of a specific repeated waveform pattern [3,14].…”

“…Participants are typically unaware of the existence of the repeated segment, leading to the conclusion that the waveform-specific learning is implicit (i.e. implicit motor learning) [13,14].…”

“…Considering that the alpha frequency has large variability among individuals [16], the alpha NFT group adopted individual alpha band instead of the fixed band (8)(9)(10)(11)(12). For the shamcontrol group, we utilized a random beta NFT protocol in which the participants received real NFT in 4 Hz bins of beta band randomly selected from a broad beta band (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28) for each session and participant [17].…”