This minimized any interindividual difference due to learning (see Supplementary Material).
Additionally, hypothesis-driven methods are built on optimised models derived from artificial intelligence domains, which can learn and evolve without explicit programming, and validate the created model using data from multiple and independent data sets (i.e. machine learning, supplementary-table-1 for related terminology).
The Monte Carlo simulation had an appropriate type 1 error rate when the model learning parameter was known (see Supplementary Material, S1.1), but this was inflated, in the more realistic case, when α was estimated from the data (e.g. Round-Box: 24% estimated Type 1 error rate for 5% significance, 95% confidence interval: 21.5% to 26.7%).
A subset of 37 adults (mean age = 52 years, SD = 16, range 22 85; 20 female, 17 male) also completed a reward task that did not depend on learning while undergoing fMRI (see Supplementary Information S1).
In Milwaukee County, residents were so angry to learn that supplementary pension accounts would turn some officials into millionaires that they held a recall election and voted seven county supervisors out of office.
The resulting learning curves are shown in Figure 3 (see Supplementary Fig. 1 A for a breakdown of the learning curves with regard to the 2 cue components).
The Bayesian network learning process was shown in Figure S1 (see Supplementary Material available online at http://dx.doi.org/10.1155/2014/746970).org/10.1155/2014/746970
Many pedagogical applications have adopted what are posted in the community for supplementary learning.
Its learning modules can be used by teachers as supplementary learning resources to traditional classroom instruction, or for independent learning.
More importantly, the support provided to learners is not only learning guidance or supplementary materials, but also some appropriate learning strategies or tools.
