Probabilistic Motor Sequence Yields Greater Offline and Less Online Learning than Fixed Sequence
نویسندگان
چکیده
منابع مشابه
Probabilistic Motor Sequence Yields Greater Offline and Less Online Learning than Fixed Sequence
It is well acknowledged that motor sequences can be learned quickly through online learning. Subsequently, the initial acquisition of a motor sequence is boosted or consolidated by offline learning. However, little is known whether offline learning can drive the fast learning of motor sequences (i.e., initial sequence learning in the first training session). To examine offline learning in the f...
متن کاملSleep does not benefit probabilistic motor sequence learning.
It has become widely accepted that sleep-dependent consolidation occurs for motor sequence learning based on studies using finger-tapping tasks. Studies using another motor sequence learning task [the serial response time task (SRTT)] have portrayed a more nuanced picture of off-line consolidation, involving both sleep-dependent and daytime consolidation, as well as modifying influences of expl...
متن کاملEditorial: Online and Offline Modulators of Motor Learning
What are the multitude of factors and processes that shape the acquisition and stabilization of a new motor skill? This is an important question that needs to be meticulously considered in order to design efficient paradigms for sports training programs as well as new rehabilitative protocols for restoring motor function following trauma or disease. Although the motor learning literature is abu...
متن کاملdevelopmental differences in motor sequence learning: task learning approach based on motor development
learning motor tasks is one of the fundamental attributes of mankind'sexperiences and is a collection of sensitive, cognitive and motor processes. manyof complex motor behaviors are performed based on a type of order or sequence.in the present study, the developmental differences in motor sequence learningwere examined in three groups of children (age range 7,8,10 yr) and one controlgroup ...
متن کاملDiscriminative Learning of Probabilistic Sequence Models for Sequence Labeling Problems
The problem of labeling (or segmenting) sequences is very important in many applications such as part-of-speech tagging in natural language processing, multimodal object detection in computer vision, and DNA/protein structure prediction in bioinformatics. Conditional Random Fields (CRFs) of [1] are known to be the best sequence models ever for the problem. CRF is a conditional model, P (s|y), i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Human Neuroscience
سال: 2016
ISSN: 1662-5161
DOI: 10.3389/fnhum.2016.00087