Sentence examples for probability of learning from inspiring English sources
However, these measures conflate changes in the probability of learning with changes in the amplitude of learned responses.
There are clear benefits of humor in the workplace: less burnout, higher probability of learning, more collaboration, faster recovery from stressful situations and an increase in overall work effectiveness.
Current educational research shows the importance of using various instructional methods and incorporating a number of active learning strategies in order to increase the probability of learning among their students (Armbruster et al. 2009; Freeman et al. 2007; Wood 2009).
It is notable that despite the fact that Mecp2 is also expressed in PCs (Mullaney et al., 2004), Mecp2R308/Y mice showed no defect in probability of learning.
Educational resources affect educational process efficiency, as their development and creative use enhances the probability of students learning better.
More specifically, for decision making, decreased deactivation in areas of the DM may imply that the process of learning probabilities of future events is impaired.
The prior probabilities were set manually to ensure that all hypotheses had non-zero probability mass at the start of learning.
Results of the research will then be relevant to instructors since there will be high probability of achieving the same level of learning outcome as reported in the study.
Moreover, our findings draw attention to the fact that children acquire the hidden sequences very fast, as they are sensitive to statistical probabilities already in the first epoch of learning.
This suggests that, in typical learning data, the IO 0.95) learning trial may be earlier than the learning trial determined using learning criteria that require a high probability of correct response, such as 90% correct, before accepting the occurrence of learning.
Neural Networks, 22(3), 220 228] of such selectionist style of learning reveals a resemblance between its ensemble-level dynamics governing the change of action probability and Bayesian learning where evidence (in this case, reward) is distributively applied to all action alternatives.
