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This opinion piece supports the idea of cognitive penetration by arguing that expectations and ideas can directly impact how color is perceived. It makes reference to Fiona Macpherson's interpretation of a 1965 study by Delk and Fillenbaum, in which participants thought that recognizable shapes—like hearts—were redder than others, indicating that cognitive expectations influenced their sense of color. The study challenges conventional notions of perception as independent by providing evidence that beliefs actively impact perceptual experiences. In response to rebuttals, the paper highlights that cognitive penetration provides a more straightforward explanation than judgment errors and concludes that this phenomenon merits greater investigation in all perceptual domains.
This East China Normal University study examined over 1,400 people to understand why we help others. It found that people consider multiple factors—like fairness, overall benefit, and personal cost—when deciding to help someone or punish wrongdoers. The study revealed that most people prefer helping over punishing and identified three types of helpers: justice warriors, pragmatic helpers, and rational moralists. This allows us to model altruism and human behavior much better.
This UCSC study shows that a small network of four simple neurons can mimic the behavior of a more complex neuron model, which is a significant advancement in AI. This new approach not only saves memory but also boosts processing speed. By focusing on the internal workings of neurons rather than just their connections, researchers are paving the way for more efficient and energy-saving AI systems that better resemble human brain functions.
This American University of Beirut study developed a new AI approach called "deep distilling" that makes artificial intelligence more understandable to humans. Unlike traditional AI systems that work like black boxes, this method can explain its decision-making process and turn its calculations into readable computer code. The system has already shown promise in tasks like image processing, though it currently works best with simple, discrete data.
This study from Beijing Normal University investigated how our brains process and understand patterns in language through analogical reasoning– the ability to recognize similarities between different things. The researchers were particularly interested in how people recognize and apply syntactic (grammatical) patterns, which is fundamental to how we learn and use language. The study revealed how different brain regions work together when we learn and understand language patterns.
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