Learning patterns in events can be a tricky business. Some patterns are easy to spot, like when you know that seeing dark clouds means rain is coming. But what about patterns that aren’t as obvious? That’s where nonadjacent dependencies (NADs) come in. NADs involve tracking the co-occurrences between elements that are far apart in time. This type of pattern is harder for us humans to learn, and we often need some help to notice these connections. In this study, researchers conducted seven experiments using different visual stimuli to investigate how dynamic motion and human agents impact our ability to learn NADs. The results showed that dynamic motion actually makes it easier for us to learn these nonadjacent dependencies! They also found that sequences involving human agents were more effective than those with nonhuman objects. These findings suggest that both dynamic motion and human agents contribute to creating stronger mental representations that aid in learning NADs. To dive deeper into the research, check out the full article!
Abstract
Many events that humans and other species experience contain regularities in which certain elements within an event predict certain others. While some of these regularities involve tracking the co-occurrences between temporally adjacent stimuli, others involve tracking the co-occurrences between temporally distant stimuli (i.e., nonadjacent dependencies, NADs). Prior research shows robust learning of adjacent dependencies in humans and other species, whereas learning NADs is more difficult, and often requires support from properties of the stimulus to help learners notice the NADs. Here, we report on seven experiments that examined NAD learning from various types of visual stimuli, exploring the effects of dynamic motion on adults’ NAD learning from visual sequences involving human and nonhuman agents. We tested adults’ NAD learning from visual sequences of human actions, object transformations, static images of human postures, and static images of an object in different postures. We found that dynamic motion aids the acquisition of NADs. We also found that learning NADs in sequences involving human agents is more robust compared to sequences involving nonhuman objects. We propose that dynamic motion and human agents both independently result in richer representations that provide a stronger signal for NADÂ learning.
Dr. David Lowemann, M.Sc, Ph.D., is a co-founder of the Institute for the Future of Human Potential, where he leads the charge in pioneering Self-Enhancement Science for the Success of Society. With a keen interest in exploring the untapped potential of the human mind, Dr. Lowemann has dedicated his career to pushing the boundaries of human capabilities and understanding.
Armed with a Master of Science degree and a Ph.D. in his field, Dr. Lowemann has consistently been at the forefront of research and innovation, delving into ways to optimize human performance, cognition, and overall well-being. His work at the Institute revolves around a profound commitment to harnessing cutting-edge science and technology to help individuals lead more fulfilling and intelligent lives.
Dr. Lowemann’s influence extends to the educational platform BetterSmarter.me, where he shares his insights, findings, and personal development strategies with a broader audience. His ongoing mission is shaping the way we perceive and leverage the vast capacities of the human mind, offering invaluable contributions to society’s overall success and collective well-being.