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Description
A previous study demonstrated that learning to lift an object is context-based and that in the presence of both the memory and visual cues, the acquired sensorimotor memory to manipulate an object in one context interferes with the performance of the same task in presence of visual information about a different context (Fu et al, 2012).
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.
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.
ContributorsHasan, Salman Bashir (Author) / Santello, Marco (Thesis director) / Kleim, Jeffrey (Committee member) / Helms Tillery, Stephen (Committee member) / Barrett, The Honors College (Contributor) / W. P. Carey School of Business (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
Increase in the usage of Internet of Things(IoT) devices across physical systems has provided a platform for continuous data collection, real-time monitoring, and extracting useful insights. Limited computing power and constrained resources on the IoT devices has driven the physical systems to rely on external resources such as cloud computing for handling compute-intensive and data-intensive processing. Recently, physical environments have began to explore the usage of edge devices for handling complex processing. However, these environments may face many challenges suchas uncertainty of device availability, uncertainty of data relevance, and large set of geographically dispersed devices. This research proposes the design of a reliable distributed management system that focuses on the following objectives: 1. improving the success rate of task completion in uncertain environments. 2. enhancing the reliability of the applications and 3. support latency sensitive applications. Main modules of the proposed system include:
1. A novel proactive user recruitment approach to improve the success rate of the task completion.
2.Contextual data acquisition and integration of false data detection for enhancing the reliability of the applications.
3. Novel distributed management of compute resources for achieving real-time monitoring and to support highly responsive
applications.
User recruitment approaches select the devices for offloading computation. Proposed proactive user recruitment module selects an optimized set of devices that match the resource requirements of the application. Contextual data acquisition
module banks on the contextual requirements for identifying the data sources that are more useful to the application. Proposed reliable distributed management system can be used as a framework for offloading the latency sensitive applications across the volunteer computing edge devices.
ContributorsCHAKATI, VINAYA (Author) / Gupta, Sandeep K.S (Thesis advisor) / Dasgupta, Partha (Committee member) / Banerjee, Ayan (Committee member) / Pal, Anamitra (Committee member) / Kumar, Karthik (Committee member) / Arizona State University (Publisher)
Created2021