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Byte Sized Letter Eighteen
Byte Sized Letter Eighteen
On This Week's Newsletter - Tuesday, 02/25 to Monday, 03/03
On This Week's Newsletter - Tuesday, 02/25 to Monday, 03/03
Click on any of the titles to read the full piece!
Neuroscientist Gül Dölen's research suggests that psychedelics like MDMA, LSD, and psilocybin can reopen critical learning periods in the brain, aiding recovery from PTSD, stroke, and more. While promising for neurorehabilitation, risks remain, as reopened brain plasticity could reinforce harmful patterns. Further research is needed for safe, effective treatments.
Smoulder et al. attempted to uncover why people choke under pressure, revealing that high rewards can disrupt motor preparation rather than enhance it. Studying rhesus monkeys, the experimenters found that while moderate incentives improve performance, high stakes push neural activity beyond targeted levels, severely hindering execution.
A recent study using fMRI investigated the fusiform face area (FFA) in the fusiform gyrus, confirming its specialization for face perception. The FFA responded significantly more to faces than objects, houses, or hands in 12 of 15 subjects, even when external facial features were concealed. The findings align with previous studies, suggesting that face perception mechanisms are evolutionarily conserved. This research not only reinforces the presence of dedicated neural structures for processing faces in the human brain but also has significant implications for conditions like prosopagnosia, enhancing the practical relevance of the study.
The Shazam Band is an AI-powered pet collar that uses machine learning to interpret a pet’s emotions and generate speech responses. It includes GPS tracking and customizable voice personas. While intriguing, questions remain about whether it genuinely enhances communication or reflects human interpretations of pet behavior.
This Zhejiang University study presents DiffMDD, a new artificial intelligence system designed to diagnose Major Depressive Disorder (MDD) using brain wave recordings (EEG). The system overcomes two major challenges: noisy brain recordings and limited data. It works in three steps: first by learning to handle noisy signals, then generating additional high-quality brain wave samples, and finally making accurate diagnoses. Testing showed the system performed better than existing methods at identifying depression.
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