11/18/2023 0 Comments Visualized learning![]() ![]() Stop-Signal Task (Click on “Run the Demo” in the left-hand margin.Acquisition and consolidation processes following motor imagery practice ( Scientific Reports). ![]() ( Journal of Experimental Psychology: Human Perception and Performance) Visual images preserve metric spatial information: Evidence from studies of image scanning.Using motor imagery practice for improving motor performance – A review ( Brain & Cognition).Visual mental imagery and visual perception: Structural equivalence revealed by scanning processes ( Memory & Cognition).Chapter 15 – Aphantasia: The science of visual imagery extremes ( Handbook of Clinical Neurology).What is the relationship between Aphantasia, Synaesthesia and Autism? ( Consciousness and Cognition).Motor Imagery Combined With Physical Training Improves Response Inhibition in the Stop Signal Task ( Frontiers in Psychology).Mental practice modulates functional connectivity between the cerebellum and the primary motor cortex ( iScience).Sleep and the Time Course of Motor Skill Learning ( Learning & Memory).Best practice for motor imagery: a systematic literature review on motor imagery training elements in five different disciplines ( BMC Medicine).This episode should allow anyone to learn or teach more effectively through the use of mental visualization and training. ![]() Throughout, I reference the scientific studies supporting these concepts. I also provide examples of specific protocols, including repetitions, rest periods, and session frequency, and how to adapt these methods for injuries or breaks from traditional training. I then present five key principles of mental visualization to enhance learning speed, accuracy, and consistency. I discuss neuroplasticity-based skill development and the roles of focus, sleep, movement restriction, and agitation. In this episode, I explore the science of mental visualization and its application for learning motor and cognitive skills. Using t-SimCNE, we obtain informative visualizations of the CIFAR-10 and CIFAR-100 datasets, showing rich cluster structure and highlighting artifacts and outliers.Listen: YouTube | Apple Podcasts | Spotify We show that the resulting 2D embeddings achieve classification accuracy comparable to the state-of-the-art high-dimensional SimCLR representations, thus faithfully capturing semantic relationships. T-SimCNE combines ideas from contrastive learning and neighbor embeddings, and trains a parametric mapping from the high-dimensional pixel space into two dimensions. Here, we present a new method, called t-SimCNE, for unsupervised visualization of image data. This problem can be circumvented by self-supervised approaches based on contrastive learning, such as SimCLR, relying on data augmentation to generate implicit neighbors, but these methods do not produce two-dimensional embeddings suitable for visualization. For images represented in pixel space this is not the case, as distances in pixel space are often not capturing our sense of similarity and therefore neighbors are not semantically close. Visual Learning is one of the three different learning styles popularized by Neil D. Yet, these approaches only produce meaningful results if the nearest neighbors themselves are meaningful. Abstract: Visualization methods based on the nearest neighbor graph, such as t-SNE or UMAP, are widely used for visualizing high-dimensional data. ![]()
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