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Abstract-concept learning is seeing the comparison of the stimuli based on a rule (e.g., identity, difference, oddity, greater than, addition, subtraction) and when it is a novel stimulus. [9] With abstract-concept learning have three criteria’s to rule out any alternative explanations to define the novelty of the stimuli.
Mechanics (from Ancient Greek μηχανική (mēkhanikḗ) ' of machines ') [1] [2] is the area of physics concerned with the relationships between force, matter, and motion among physical objects. [3] Forces applied to objects may result in displacements, which are changes of an object's position relative to its environment.
The engineering design process, also known as the engineering method, is a common series of steps that engineers use in creating functional products and processes. The process is highly iterative – parts of the process often need to be repeated many times before another can be entered – though the part(s) that get iterated and the number of such cycles in any given project may vary.
Treatment learning is a form of weighted contrast-set learning that takes a single desirable group and contrasts it against the remaining undesirable groups (the level of desirability is represented by weighted classes). [5] The resulting "treatment" suggests a set of rules that, when applied, will lead to the desired outcome.
In animals, the 2AFC task has been used to test reinforcement probability learning, for example such as choices in pigeons after reinforcement of trials. [3] A 2AFC task has also been designed to test decision making and the interaction of reward and probability learning in monkeys. [4] Example of a random dot kinetogram as used in a 2AFC task.
Shearing forces in contrast with normal forces, act parallel rather than perpendicular to the material plane and the shearing force per unit area is called shear stress. Therefore, solid mechanics examines the shear stress, deformation and the failure of solid materials and structures. The most common topics covered in solid mechanics include:
By definition, different streamlines at the same instant in a flow do not intersect, because a fluid particle cannot have two different velocities at the same point. Pathlines are allowed to intersect themselves or other pathlines (except the starting and end points of the different pathlines, which need to be distinct).
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.