Visual working memory and long-term memory interactions
Project description
Semantic learning creates detailed long-term representations by presenting various bits of information that could be connected and integrated into a semantic network related to the studied material. While this type of learning is prevalent in our everyday form of learning, it has not been systematically studied. In the current project, we are looking at the effectiveness of semantic learning for complex content, and comparing it to learning through memorizing and rehearsing the studied information. We have developed a semantic form of learning based on the exposure to a wide range of aspects regarding the studied material, and we have shown that this type of learning enables the creation of detailed long-term representations as well as maximizing the performance of visual working memory. In the upcoming year, we plan to compare semantic learning to repetition-based learning, by using behavioral metrics as well as EEG, and examine the effectiveness of these learning forms on simple and complex contents. The goal of this research is to show that semantic learning can be very effective and efficient in learning complex content in a short period of time, and to offer a new approach to how we perceive learning.
About me
I hold an M.A in Cognitive Psychology, and a B.A in Psychology and Philosophy, both from Tel-Aviv University. My interest in cognitive processing limitations, and in particular the limitations of visual working memory capacity, led me to ask whether familiar information enables more efficient allocation of visual working memory capacity. In my master's research, I found that learning semantic information, which is irrelevant to visual working memory tasks, directly improved performance in visual working memory tasks. To carry out a direct extension of this research, I turned to investigating familiarity and whether different types of learning lead to differences in visual working memory utilization.