Neural Mechanisms of the Contextual Interference Effect and Parameter Similarity on Motor Learning in Older Adults: An EEG Study

Abstract

The purpose of this study was to investigate the neural mechanisms of the contextual interference effect (CIE) and parameter similarity on motor learning in older adults. Sixty older adults (mean age, 67.68 ± 3.95 years) were randomly assigned to one of six experimental groups: blocked-similar, algorithm-similar, random-similar, blockeddissimilar, algorithm-dissimilar, and random-dissimilar. Algorithm practice was a hybrid practice schedule (a combination of blocked, serial, and random practice) that switching between practice schedules were based on error trial number, ≤33%. The sequential motor task was used to record the absolute timing for the absolute timing goals (ATGs). In similar conditions, the participants' performance was near ATGs (1,350, 1,500, 1,650 ms) and in dissimilar conditions, they performed far ATGs (1,050, 1,500, 1,950 ms) with the same spatial sequence for all groups. EEG signals were continuously collected during the acquisition phase and delayed retention. Data were analyzed in different bands (alpha and beta) and scalp locations (frontal: Fp1, Fp2, F3, F4; central: C3, C4; and parietal: P3, P4) with repeated measures on the last factor. The analyses were included motor preparation and intertrial interval (motor evaluation) periods in the first six blocks and the last six blocks, respectively. The results of behavioral data indicated that algorithm practice resulted in medium error related to classic blocked and random practice during the acquisition, however, algorithm practice outperformed the classic blocked and random practice in the delayed retention test. The results of EEG data demonstrated that algorithm practice, due to optimal activity in the frontal lobe (medium alpha and beta activation at prefrontal), resulted in increased activity of sensorimotor areas (high alpha activation at C3 and P4) in older adults. Also, EEG data showed that similar conditions could affect the intertrial interval period (medium alpha and beta activation in frontal in the last six-block), while the dissimilar conditions could affect the motor preparation period (medium alpha and beta activation in frontal in the first sixblock). In conclusion, algorithm practice can enhance motor learning and optimize the efficiency of brain activity, resulting in the achievement of a desirable goal in older adults.

Contextual Interference Effects on Approach Motivation When Learning Timing Tasks: A Frontal Electroencephalography (EEG) Alpha Asymmetry Study in Older Adults

“…Also, participants in the random practice schedule performed better on retention than participants in the blocked practice schedule. These findings are in line with previous studies (Beik et al, 2020; Pauwels et al, 2015; Sidaway et al, 2016). Beik et al (2021) used a similar paradigm to show that an algorithm practice schedule led to higher learning among older adults than did random and blocked practice schedules.…”

“…Beik et al (2021) used a similar paradigm to show that an algorithm practice schedule led to higher learning among older adults than did random and blocked practice schedules. Beik et al (2020) showed a better frontal activity among older adults in the algorithm practice schedule.…”

“…Although we recorded EEG data during all acquisition trials with eight electrodes, we analyzed data from the first three and last three blocks (1, 2, 3; 16, 17, 18) of acquisition using only two electrodes (F3 and F4). This method differed from EEG data gathering in Beik et al (2020) from eight electrodes (to investigate cognitive and motor areas of the brain in relation to motor learning) and from Beik and Fazeli (2021), in which intrinsic motivation data was limited by the use of a questionnaire for measuring dynamic mental state before task execution (Chen et al, 2019). Our current study also differed by its use of EEG to measure motivational regulation in older adults.…”

Polish Psychological Bulletin

“…To perform the statistical analysis, a paired sample t-test was used. (Beik, Taheri, Saberi Kakhki, & Ghoshuni, 2020). According to the normality of data (Shapiro-Wilk…”

The Age-Specific Impact of Alpha-Wave Binaural Acoustic Stimulation on Motor-Learning Aptitude

“…Several methods can be used to measure motor learning and control, one of which is electroencephalography (EEG), also known as the neural technique (Beik et al, 2020). In fact, EEG is a good tool to analyze neural correlates for both simple and complex movements in humans (Bradford et al, 2016;Pfurtscheller et al, 2003).…”