In this study, we have shown how LD generated by population admixture can be a powerful tool for learning about history, extending previous work that showed how it can be used for estimating dates of mixture (Moorjani et al. 2011; Patterson et al. 2012).
For a class of local learning rules, we determine the memory capacity of the model by extending previous analysis.
The results extend previous findings of configural learning in humans.
We extended previous findings by including pursuit motor learning, a classic procedural task that does not involve learning a sequence of discrete elements.
Our findings support and extend previous research implicating proprioception in motor learning to include repeated segment-specific continuous tracking [ 42- 44].
This result extends previous reports supporting the benefits of gesture for learning in children (e.g. Church et al., 2004; Cook et al., 2013; Koumoutsakis et al., 2016; Singer & Goldin-Meadow, 2005) to adults.
These data confirm and extend previous reports, and support a hypothesis wherein E treatment can improve learning on specific tasks by activating GPR30 and enhancing ACh release in association with food reward.
The current study extends previous research on gesture's role in instruction to young adults' learning of a commonly taught statistical concept, ANOVA.
Section 5, extends previous classification by adding the impact of observation accuracy and the benefit of learning techniques in such contexts.
The current work extends previous results on d-Confidence providing a comprehensive description and evaluation of this active learning strategy.
This paper extends previous models of sequence memory [7, 9, 11, 12] that were based on Willshaw's learning rule [10] to inhomogeneous pattern sizes, i.e., patterns of variable sparseness.
