Sentence examples for learning interference from inspiring English sources

Yuqing Huang et al. in [75] proposed a CR learning interference and decision-making engine based on Bayesian networks.

However, if a second motor task is practiced after initial motor learning interference can occur and consequently motor performance on subsequent occasions may be no better than baseline on day one [2], [6].

Depotentiation is an interesting phenomenon that occurs when LFS is applied within a very short time-window (maximum 30 min) of inducing LTP (Staubli and Lynch 1990; Kulla et al. 1999) and has been proposed to comprise a functional correlate of active forgetting or perhaps of learning interference.

Lastly, the effect of the second session was markedly diminished as compared to the effect of the first session, indicating strong anterograde learning interference.

We applied nonlinear mathematical modeling to parametrize four main consequences of DecNef: main effect of change in confidence, strength of down-regulation of confidence relative to up-regulation, maintenance of learning effects, and anterograde learning interference.

For these types of learning, interference from competing tasks is persistent and resistance to interference is not observed with passage of time as it is observed as it is in the current study.

Two important results reinforce each other: Experiment I and III demonstrated that between-task interference requires identical or overlapping circuits to be engaged in competing motor learning processes; Interference does not occur when learning is not involved.

The files describe how the force first learned of interference in the investigation.

Within this framework, the robots learn the interference and therefore, the coalition's utility, from their past experience using an on-line Support Vector Regression method (SVR).

We first examined whether linking such skills that interfere to different follow-through movements might activate separate motor memories for each, thereby allowing both skills to be learned without interference.

In contrast, the work in [9] classifies fusion algorithms into estimation methods (weighted average, Kalman filter), classification methods (cluster analysis, unsupervised or self-organized learning algorithms), interference methods (Bayesian interference, Dempster-Shafter evidential reasoning), and artificial intelligence methods (neural networks, fuzzy logic).