Most theories of analogical transfer focus on similarities between the learning and transfer domains, where transfer is more likely between domains that share common surface features, similar elements, or common interpretations of structure. We suggest that characteristics of the learning instantiation alone can give rise to different levels of transfer. We propose that concreteness of the lear...

this study aimed at investigating the effect of blocked, incremental systematic and random contextual interferences on acquisition, retention and transfer of volleyball skills based on changes in a generalized motor program. in fact, this study was conducted to answer this question that whether regular increment of contextual interference level is more effective on learning volleyball service s...

Knowledge is often highly specific to the conditions of acquisition, so there is limited transfer of learning from training to testing. A series of studies is reported examining specificity and transfer of learning in three very different tasks, including digit data entry, speeded aiming, and time production. These studies address a variety of theoretical issues, including those involving menta...

Evaluation of transfer learning systems requires deployment of carefully structured problem instances, composed of source and target subproblems. In general, the difficulty of a transfer learning task is measured by the relationship between the source and target. Relational nets provide a formalism for rigorously defining the source-target relationship in transfer learning tasks.

BACKGROUND An outstanding question in sensory neuroscience is whether the perceived timing of events is mediated by a central supra-modal timing mechanism, or multiple modality-specific systems. We use a perceptual learning paradigm to address this question. METHODOLOGY/PRINCIPAL FINDINGS Three groups were trained daily for 10 sessions on an auditory, a visual or a combined audiovisual tempor...

Most theories of analogical transfer focus on similarities between the learning and transfer domains, where transfer is more likely between domains that share common surface features, similar elements, or common interpretations of structure. We suggest that characteristics of the learning instantiation alone can give rise to different levels of transfer. We propose that concreteness of the lear...

Visual perceptual learning has been traditionally characterized by its specificity. Namely, learning transfers little to many untrained stimulus attributes. This result of specificity is the basis for the inference that perceptual learning takes place in low-level visual areas in the brain. Recently, however, Xiao and colleagues (2008) demonstrated a double training technique that enabled compl...

Transfer learning addresses the problems that labeled training data are insufficient to produce a high-performance model. Typically, given a target learning task, most transfer learning approaches require to select one or more auxiliary tasks as sources by the designers. However, how to select the right source data to enable effective knowledge transfer automatically is still an unsolved proble...

Deep learning is the current bet for image classification. Its greed for huge amounts of annotated data limits its usage in medical imaging context. In this scenario transfer learning appears as a prominent solution. In this report we aim to clarify how transfer learning schemes may influence classification results. We are particularly focused in the automated melanoma screening problem, a case...

This paper presents a framework, called the knowledge co-creation framework (KCF), for heterogeneous multiagent robot systems that use a transfer learning method. A multiagent robot system (MARS) that utilizes reinforcement learning and a transfer learning method has recently been studied in realworld situations. In MARS, autonomous agents obtain behavior autonomously through multi-agent reinfo...