Drawabox | A free, exercise based approach to learning the fundamentals of drawing
A free, exercise based approach to learning the fundamentals of drawing. We tackle drawing in a logical, analytical fashion inspired by concept artists and industrial designers, focusing on technical skills that can be developed with hard work and determination.
Cultivating Visual Literacy: A Play-Based Approach to Drawing Education for Young Children
This paper introduces a novel, play-based method for teaching drawing to young children, emphasising observation, creativity, and the development of visual literacy. The method, refined through years of practical testing with the author's children, moves beyond rote copying and focuses on engaging parents in the learning process. It leverages fun, interactive exercises designed to enhance spatial reasoning, attention to detail, and an appreciation for artistic expression while fostering the understanding of drawing as a natural form of expression alongside language and mathematics, helping children develop a confident, lifelong relationship with visual communication that transcends common perceptions of drawing as an intimidating or exclusively talent-based skill. This paper also presents a literature review on early childhood art education and highlights the importance of drawing as a fundamental cognitive skill.
Prof. Judy Fan: Cognitive Tools for Making the Invisible Visible
Prof. Judy Fan: Cognitive Tools for Making the Invisible Visible
How Sketching Helps Me Develop Ideas
Sketching isn’t about perfection—it’s about thinking through ideas and making them easier to share.
People Who Can't Visualize Anything Are Challenging a 300-Year-Old Theory of Thought
Mental imagery might not be as central to complex human thought as we believed, philosophers say.
Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem
Recent work has documented striking heterogeneity in the performance of state-of-the-art vision language models (VLMs), including both multimodal language models and text-to-image models. These models are able to describe and generate a diverse array of complex, naturalistic images, yet they exhibit surprising failures on basic multi-object reasoning tasks -- such as counting, localization, and simple forms of visual analogy -- that humans perform with near perfect accuracy. To better understand this puzzling pattern of successes and failures, we turn to theoretical accounts of the binding problem in cognitive science and neuroscience, a fundamental problem that arises when a shared set of representational resources must be used to represent distinct entities (e.g., to represent multiple objects in an image), necessitating the use of serial processing to avoid interference. We find that many of the puzzling failures of state-of-the-art VLMs can be explained as arising due to the binding problem, and that these failure modes are strikingly similar to the limitations exhibited by rapid, feedforward processing in the human brain.
Why A.I. Isn’t Going to Make Art
To create a novel or a painting, an artist makes choices that are fundamentally alien to artificial intelligence.
Making Programming Visual, Spatial, and Learnable
What's wrong with linear, static programming mediums and how might we improve them?
paint: a timeline — Kristen Roos
This timeline is the result of researching the origins of digital paint and draw software, and the tools that were developed to allow for hand manipulation (versus plotter drawn) drawing and painting - the mouse, light pen & drawing tablet. If we look at the software that has become commonplace today (such as adobe photoshop), which allows for painting, animation and photo manipulation in one, we can trace the roots of this software to the University and Corporate Labs that housed large computers with advanced capabilities for their time - MIT Lincoln Labs & Radiation Labs, DARPA & the Augmented Research Centre (ARC), Bell Labs, NYIT’s Computer Graphics Lab, Xerox Palo Alto Research Centre (Xerox PARC), NASA’s Jet Propulsion Labs (JPL). The artistic collaborations that grew out of these labs fueled the advent of Computer Graphics, Computer Art and Video Art from the 1960's to the 1990's.
I’ve been working through the Turvey book on Rob Gray’s podcast and one key feature is the extended articulation of the argument that every attempt to handle perception other than direct perception is just an example of the same old Cartesian mistake with all the same problems. How accepted is this?
Reading Group: Turvey (2019), Lectures on Perception
psychsciencenotes.blogspot.com
