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- All Subjects: Learning
- Creators: Harrington Bioengineering Program
- Creators: Santello, Marco
- Creators: Computer Science and Engineering Program
- Resource Type: Text
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Motor learning is the process of improving task execution according to some measure of performance. This can be divided into skill learning, a model-free process, and adaptation, a model-based process. Prior studies have indicated that adaptation results from two complementary learning systems with parallel organization. This report attempted to answer the question of whether a similar interaction leads to savings, a model-free process that is described as faster relearning when experiencing something familiar. This was tested in a two-week reaching task conducted on a robotic arm capable of perturbing movements. The task was designed so that the two sessions differed in their history of errors. By measuring the change in the learning rate, the savings was determined at various points. The results showed that the history of errors successfully modulated savings. Thus, this supports the notion that the two complementary systems interact to develop savings. Additionally, this report was part of a larger study that will explore the organizational structure of the complementary systems as well as the neural basis of this motor learning.
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"No civil discourse, no cooperation; misinformation, mistruth." These were the words of former Facebook Vice President Chamath Palihapitiya who publicly expressed his regret in a 2017 interview over his role in co-creating Facebook. Palihapitiya shared that social media is ripping apart the social fabric of society and he also sounded the alarm regarding social media’s unavoidable global impact. He is only one of social media’s countless critics. The more disturbing issue resides in the empirical evidence supporting such notions. At least 95% of adolescents own a smartphone and spend an average time of two to four hours a day on social media. Moreover, 91% of 16-24-year-olds use social media, yet youth rate Instagram, Facebook, and Twitter as the worst social media platforms. However, the social, clinical, and neurodevelopment ramifications of using social media regularly are only beginning to emerge in research. Early research findings show that social media platforms trigger anxiety, depression, low self-esteem, and other negative mental health effects. These negative mental health symptoms are commonly reported by individuals from of 18-25-years old, a unique period of human development known as emerging adulthood. Although emerging adulthood is characterized by identity exploration, unbounded optimism, and freedom from most responsibilities, it also serves as a high-risk period for the onset of most psychological disorders. Despite social media’s adverse impacts, it retains its utility as it facilitates identity exploration and virtual socialization for emerging adults. Investigating the “user-centered” design and neuroscience underlying social media platforms can help reveal, and potentially mitigate, the onset of negative mental health consequences among emerging adults. Effectively deconstructing the Facebook, Twitter, and Instagram (i.e., hereafter referred to as “The Big Three”) will require an extensive analysis into common features across platforms. A few examples of these design features include: like and reaction counters, perpetual news feeds, and omnipresent banners and notifications surrounding the user’s viewport. Such social media features are inherently designed to stimulate specific neurotransmitters and hormones such as dopamine, serotonin, and cortisol. Identifying such predacious social media features that unknowingly manipulate and highjack emerging adults’ brain chemistry will serve as a first step in mitigating the negative mental health effects of today’s social media platforms. A second concrete step will involve altering or eliminating said features by creating a social media platform that supports and even enhances mental well-being.
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The purpose of this study is to know whether the primary motor cortex (M1) plays a role in the sensorimotor memory. It was hypothesized that temporary disruption of the M1 following the learning to minimize a tilt using a ‘L’ shaped object would negatively affect the retention of sensorimotor memory and thus reduce interference between the memory acquired in one context and the visual cues to perform the same task in a different context.
Significant findings were shown in blocks 1, 2, and 4. In block 3, subjects displayed insignificant amount of learning. However, it cannot be concluded that there is full interference in block 3. Therefore, looked into 3 effects in statistical analysis: the main effects of the blocks, the main effects of the trials, and the effects of the blocks and trials combined. From the block effects, there is a p-value of 0.001, and from the trial effects, the p-value is less than 0.001. Both of these effects indicate that there is learning occurring. However, when looking at the blocks * trials effects, we see a p-value of 0.002 < 0.05 indicating significant interaction between sensorimotor memories. Based on the results that were found, there is a presence of interference in all the blocks but not enough to justify the use of TMS in order to reduce interference because there is a partial reduction of interference from the control experiment. It is evident that the time delay might be the issue between context switches. By reducing the time delay between block 2 and 3 from 10 minutes to 5 minutes, I will hope to see significant learning to occur from the first trial to the second trial.
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Along with aging, sleep deprivation is correlated with learning deficits. Research has shown that a lack of sleep negatively impacts motor skill learning and consolidation. Since there is a link between sleep and learning, as well as learning and the reticulospinal system, these observations raise the question: does sleep deprivation underlie reticulospinal delays? We hypothesized that sleep deprivation was correlated to a slower startle response, indicating a delayed reticulospinal system. Our objectives were to observe the impact of sleep deprivation on 1) the startle response (characterized by muscle onset latency and percentage of startle responses elicited) and 2) functional performance (to determine whether subjects were sufficiently sleep deprived).
21 young adults participated in two experimental sessions: one control session (8-10 hour time in bed opportunity for at least 3 nights prior) and one sleep deprivation session (0 hour time in bed opportunity for one night prior). The same protocol was conducted during each session. First, subjects were randomly exposed to 15 loud, startling acoustic stimuli of 120 dB. Electromyography (EMG) data measured muscle activity from the left and right sternocleidomastoid (LSCM and RSCM), biceps brachii, and triceps brachii. To assess functional performance, cognitive, balance, and motor tests were also administered. The EMG data were analyzed in MATLAB. A generalized linear mixed model was performed on LSCM and RSCM onset latencies. Paired t-tests were performed on the percentage of startle responses elicited and functional performance metrics. A p-value of less than 0.05 indicated significance.
Thirteen out of 21 participants displayed at least one startle response during their control and sleep deprived sessions and were further analyzed. No differences were found in onset latency (RSCM: control = 75.87 ± 21.94ms, sleep deprived = 82.06 ± 27.47ms; LSCM: control = 79.53 ± 17.85ms, sleep deprived = 78.48 ± 20.75ms) and percentage of startle responses elicited (control = 84.10 ± 15.53%; sleep deprived = 83.59 ± 18.58%) between the two sessions. However, significant differences were observed in reaction time, TUG with Dual time, and average balance time with the right leg up. Our data did not support our hypothesis; no significant differences were seen between subjects’ startle responses during the control and sleep deprived sessions. However, sleep deprivation was indicated with declines were observed in functional performance. Therefore, we concluded that sleep deprivation may not affect the startle response and underlie delays in the reticulospinal system.
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movement to become faster, more accurate and efficient, declines with age. Initial skill acquisition is dominated by cortical structures; however as learning proceeds, literature from
rodents and songbirds suggests that there is a transition away from cortical execution. Recent
evidence indicates that the reticulospinal system plays an important role in integration and
retention of learned motor skills. The brainstem has known age-rated deficits including cell
shrinkage & death. Given the role of the reticulospinal system in skill acquisition and older
adult’s poor capacity to learn, it begs the question: are delays in the reticulospinal system
associated with older adult’s poor capacity to learn?
Our objective was to evaluate if delays in the reticulospinal system (measured via the
startle reflex) and corticospinal system (measured via Transcranial Magnetic Stimulation (TMS) are correlated to impairment of motor learning in older adults. We found that individuals with fast startle responses resembling those of younger adults show the most improvement and retention while individuals with delayed startle responses show the least. We also found that there was no relationship between MEP latencies and improvement and retention. Moreover, linear regression analysis indicated that startle onset latency exists within a continuum of learning outcomes suggesting that startle onset latency may be a sensitive measure to predict learning deficits in older adults. As there exists no method to determine an individual’s relative learning capacity, these results open the possibility of startle, which is an easy and inexpensive behavioral measure and can be used to determine learning deficits in older adults to facilitate better dosing during rehabilitation therapy.
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Our objective was to evaluate if delays in the reticulospinal system (measured via the startle reflex) are correlated to impairment of motor learning in older adults. We found that individuals with fast startle responses resembling those of younger adults show the most learning and retention of that learning while individuals with delayed startle responses show the least. Moreover, linear regression analysis indicated that startle onset latency exists within a continuum of learning outcomes suggesting that startle onset latency may be a sensitive measure to predict learning deficits in older adults. As there exists no method to determine an individual’s relative learning capacity, these results open the possibility of startle, which is an easy and inexpensive behavioral measure, being used to predict learning deficits in older adults to facilitate better dosing during rehabilitation therapy.