Trial-by-trial strategy assessments and a microgenetic design were used to examine 4- and 5-year-olds' learning of rules for solving balance scale problems. The design allowed us to examine simultaneously the contribution to rule learning of distal variables (qualities and knowledge with which children enter the learning situation) and proximal variables (processes that they execute during lear...

Learning of a complex olfactory discrimination (OD) task results in acquisition of rule learning after prolonged training. Previously, we demonstrated enhanced synaptic connectivity between the piriform cortex (PC) and its ascending and descending inputs from the olfactory bulb (OB) and orbitofrontal cortex (OFC) following OD rule learning. Here, using recordings of evoked field postsynaptic po...

In Quantum Mechanics the transition from a deterministic description to a probabilistic one is done using a simple rule termed the Born rule. This rule states that the probability of an outcome (a) given a state (Ψ) is the square of their inner products ((a>Ψ)2). In this paper, we unravel a new probabilistic justification for popular algebraic algorithms, based on the Born rule. These algorithm...

A control of real processes requires different approach to neural network learning. The presented modification of backpropagation learning algorithm changes a meaning of learning constants. A base of modification is stability condition of learning dynamics.

Typically rule extraction is done for the purposes of human interpretation. However, there are other possible applications of rule extraction. One practical application is transfer learning, in which knowledge learned in one task is used to aid in learning a related task. The extracted rules, which explain the learned solution to the first task, can be considered advice on how to approach the s...

In this paper, we present evaluations of learning algorithms for a novel rule evaluation support method in data mining post-processing, which is one of the key processes in a data mining process. It is difficult for human experts to evaluate many thousands of rules from a large dataset with noises completely. To reduce the costs of rule evaluation task, we have developed the rule evaluation sup...

Rule learning is a mechanism that allows infants to recognize and generalize rule-like patterns, such as ABB or ABA. Although infants are better at learning rules from speech vs. non-speech, rule learning can be applied also to frequently experienced visual stimuli, suggesting that perceptual expertise with material to be learned is critical in enhancing rule learning abilities. Yet infants' ru...

A novel integrative learning architecture, RLSM with a STDP network is described. This architecture models symbol emergence in an autonomous agent engaged in reinforcement learning tasks. The architecture consists of two constitutional learning architectures: a reinforcement learning schema model (RLSM) and a spike timing-dependent plasticity (STDP) network. RLSM is an incremental modular reinf...