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, blocked-dissimilar, 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 six-block). 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.
Highlights
Motor learning is defined as the relatively permanent changes in the capacity for movement through practice or experience (Schmidt et al, 2018, p. 283)
There is a contradiction between similar and dissimilar conditions concerning contextual interference effect (CIE), so that some studies showed the superior performance in dissimilar conditions of random practice (Wood and Ging, 1991; Boutin and Blandin, 2010a), whereas another study revealed the superior performance in similar conditions of random practice (Boutin and Blandin, 2010b)
To date, no study has investigated the effect of practice based on the challenge point on EEG frequencies and we examined a novel practice schedule based on the individual cognitive style on EEG bands, especially alpha and beta bands that are related to cognitive and motor processing (Kropotov, 2010; Espenhahn et al, 2019; Schmidt et al, 2019)
Summary
Motor learning is defined as the relatively permanent changes in the capacity for movement through practice or experience (Schmidt et al, 2018, p. 283). One of the most frequent research topics in terms of the practice conditions is about how the multiple tasks or variations of a task are arranged in a practice session This issue is examined under the contextual interference effect (CIE). Some studies did not show the superior performance of random practice during retention in older adults (de Souza et al, 2015) In this regard, other pieces of evidence showed that variables such as age, skill level, and learning style of learners can affect the CIE (Magill and Hall, 1990; Brady, 1998, 2008; Merbah and Meulemans, 2011), and optimal learning outcome is the result of interaction between the skill level of learner and task difficulty, known as challenge point framework (Guadagnoli and Lee, 2004). The beneficial effect of CIE can be seen in similar and dissimilar conditions both by creating the optimal challenge point using an algorithm-based practice schedule
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