For more than two decades, researchers have contrasted the relative merits of associative and statistical theories as accounts of human contingency learning. This debate, still far from resolution, has led to further refinement of models within each family of theories. More recently, a third theoretical view has joined the debate: the inferential reasoning account. The explanations of these thr...

Several theories of learning have been proposed to account for the acquisition of abstract, generative knowledge including schema theory, analogical learning and implicit learning. However, past research has not compared these three theories directly. In the present studies we instantiated each theory as a learning scenario (i.e., direct instruction, analogy training and implicit training) and ...

Advances in electronic technologies offer e-learning applications the possibility to become skillful and acquire knowledge on a task. The purpose of this paper is twofold. First to review papers related to usability methods and learning theories currently used to design e-learning applications. Secondly, to draw on the author’s experience from carrying out a test with a web-based testing applic...

Several theories of learning have been proposed to account for the acquisition of abstract, generative knowledge including schema theory, analogical learning and implicit learning. However, past research has not compared these three theories directly. In the present studies we instantiated each theory as a learning scenario (i.e., direct instruction, analogy training and implicit training) and ...

The dominant theoretical approach to causal learning postulates the acquisition of associative weights between cues and outcomes. This reduction of causal induction to associative learning implies that learners are insensitive to important characteristics of causality, such as the inherent directionality between causes and effects. An ongoing debate centers on the question of whether causal lea...

Inductive learning of recursive logical theories from a set of examples is a complex task characterized by three important issues that do not arise in single predicate learning, namely the adoption of a generality order stronger than θsubsumption, the non-monotonicity of the consistency property, and the automated discovery of dependencies between target predicates. Some variations of the class...