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The Neuroscience Behind Writing: Handwriting vs. Typing—Who Wins the Battle?

Writing is a complex phenomenon that requires diverse skills: perceiving the pen and paper, moving the writing instrument, and directing the movement through thought. Using a pen involves paying attention to motor aspects such as drawing letters legibly, controlling the pressure of the tip on the paper, following lines and spaces on the page, and coordinating thought, action, and vision. This multisensory integration underlies memory abilities. Moreover, handwriting involves a wide variety of supporting materials, including pens, pencils, or chalk on a blackboard, all of which offer different experiences and create new neural activations and skills.

Despite sharing similar central goals and processes, handwriting and typing differ significantly in terms of the tools used, spatiotemporal dimensions, motor programming, and fine motor development. Compared with handwriting, which requires more time and attention to learn, typing can be considered simpler and faster, as it enables the production of a more easily readable and homogeneous product in less time. However, focused attention and a longer processing time improve memory retention, and once automatic control of the graphic gesture is achieved, minimal cognitive effort is required. Moreover, the specific movements memorized when learning to write contribute to the visual recognition of graphic shapes and letters and secondarily also improve reading ability. Indeed, since the ability to recognize letters is widely recognized in the literature as the first phase of reading, improving it through writing may effectively influence how children read.

The comparison between handwriting and typing reveals important differences in their neural and cognitive impacts. Handwriting activates a broader network of brain regions involved in motor, sensory, and cognitive processing, contributing to deeper learning, enhanced memory retention, and more effective engagement with written material. Typing, while more efficient and automated, engages fewer neural circuits, resulting in more passive cognitive engagement. These findings suggest that despite the advantages of typing in terms of speed and convenience, handwriting remains an important tool for learning and memory retention, particularly in educational contexts.

AI streamlines search for catalysts to clear hydrogen production hurdles

To increase energy efficiency and reduce the carbon footprint of hydrogen fuel production, Fanglin Che, associate professor in the Department of Chemical Engineering at Worcester Polytechnic Institute, is leveraging the power and potential of machine learning and computational modeling. The multi-university team she leads has completed a study that was just published in Nature Chemical Engineering. The study utilized artificial intelligence to identify catalysts with the potential to facilitate cleaner and more efficient hydrogen production.

AI tools fall short in predicting suicide, study finds

The accuracy of machine learning algorithms for predicting suicidal behavior is too low to be useful for screening or for prioritizing high-risk individuals for interventions, according to a new study published September 11 in the open-access journal PLOS Medicine by Matthew Spittal of the University of Melbourne, Australia, and colleagues.

Numerous risk assessment scales have been developed over the past 50 years to identify patients at high risk of suicide or self-harm. In general, these scales have had poor predictive accuracy, but the availability of modern machine learning methods combined with electronic health record data has re-focused attention on developing to predict suicide and self-harm.

In the new study, researchers undertook a systemic review and meta-analysis of 53 previous studies that used machine learning algorithms to predict suicide, self-harm and a combined suicide/self-harm outcome. In all, the studies involved more than 35 million and nearly 250,000 cases of suicide or hospital-treated self-harm.

Karl J. Friston

Professors Karl Friston & Mark Solms, pioneers in the fields of neuroscience, psychology, and theoretical biology, delve into the frontiers of consciousness: “Can We Engineer Artificial Consciousness?”. From mimicry to qualia, this historic conversation tackles whether artificial consciousness is achievable — and how. Essential viewing/listening for anyone interested in the mind, AI ethics, and the future of sentience. Subscribe to the channel for more profound discussions!

Professor Karl Friston is one of the most highly cited living neuroscientists in history. He is Professor of Neuroscience at University College London and holds Honorary Doctorates from the University of Zurich, University of York and Radboud University. He is the world expert on brain imaging, neuroscience, and theoretical neurobiology, and pioneers the Free-Energy Principle for action and perception, with well-over 300,000 citations. Friston was elected a Fellow of the Academy of Medical Sciences (1999). In 2000 he was President of the international Organization of Human Brain Mapping. He was elected a Fellow of the Royal Society in 2006. He became a Fellow of the Royal Society of Biology in 2012 and was elected as a member of EMBO (excellence in the life sciences) in 2014 and the Academia Europaea in (2015).

Professor Mark Solms is director of Neuropsychology in the Neuroscience Institute of the University of Cape Town and Groote Schuur Hospital (Departments of Psychology and Neurology), an Honorary Lecturer in Neurosurgery at the Royal London Hospital School of Medicine, an Honorary Fellow of the American College of Psychiatrists, and the President of the South African Psychoanalytical Association. He is also Research Chair of the International Psychoanalytical Association (since 2013). He founded the International Neuropsychoanalysis Society in 2000 and he was a Founding Editor (with Ed Nersessian) of the journal Neuropsychoanalysis. He is Director of the Arnold Pfeffer Center for Neuropsychoanalysis at the New York Psychoanalytic Institute. He is also Director of the Neuropsychoanalysis Foundation in New York, a Trustee of the Neuropsychoanalysis Fund in London, and Director of the Neuropsychoanalysis Trust in Cape Town.

TIMESTAMPS:
0:00 — Introduction.
0:45 — Defining Consciousness & Intelligence.
8:20 — Minimizing Free Energy + Maximizing Affective States.
9:07 — Knowing if Something is Conscious.
13:40 — Mimicry & Zombies.
17:13 — Homology in Consciousness Inference.
21:27 — Functional Criteria for Consciousness.
25:10 — Structure vs Function Debate.
29:35 — Mortal Computation & Substrate.
35:33 — Biological Naturalism vs Functionalism.
42:42 — Functional Architectures & Independence.
48:34 — Is Artificial Consciousness Possible?
55:12 — Reportability as Empirical Criterion.
57:28 — Feeling as Empirical Consciousness.
59:40 — Mechanistic Basis of Feeling.
1:06:24 — Constraints that Shape Us.
1:12:24 — Actively Building Artificial Consciousness (Mark’s current project)
1:24:51 — Hedonic Place Preference Test & Ethics.
1:30:51 — Conclusion.

EPISODE LINKS:
- Karl’s Round 1: https://youtu.be/Kb5X8xOWgpc.
- Karl’s Round 2: https://youtu.be/mqzyKs2Qvug.
- Karl’s Lecture 1: https://youtu.be/Gp9Sqvx4H7w.
- Karl’s Lecture 2: https://youtu.be/Sfjw41TBnRM
- Karl’s Lecture 3: https://youtu.be/dM3YINvDZsY
- Mark’s Round 1: https://youtu.be/qqM76ZHIR-o.
- Mark’s Round 2: https://youtu.be/rkbeaxjAZm4

CONNECT:

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