人类认知处理

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哈佛新论文揭示 Transformer 模型与人脑“同步纠结”全过程,AI也会犹豫、反悔?
3 6 Ke· 2025-05-12 00:22
Core Insights - A recent study from researchers at Harvard University, Brown University, and the University of Tübingen explores the similarities between the processing dynamics of Transformer models and real-time human cognition [1][2]. Group 1: Research Objectives - The study aims to investigate the internal processing of AI models, particularly how it compares to human cognitive processes, rather than just focusing on the final outputs [2][4]. - The authors propose to analyze the processing dynamics at each layer of the Transformer model to see if they align with the real-time information processing in the human brain [4][24]. Group 2: Methodology and Findings - The researchers recorded the outputs and changes at each layer of the Transformer model, introducing a series of "processing load" metrics to understand how confidence in answers evolves through the layers [7][24]. - The study found that both AI and humans exhibit similar patterns of hesitation and correction when faced with challenging questions, indicating a shared cognitive process [11][18][23]. Group 3: Specific Examples - In the "capital killer question," both AI and humans initially leaned towards incorrect answers (e.g., Chicago) before correcting themselves to the right answer (Springfield) [13][15]. - In animal classification tasks, both AI and humans showed a tendency to initially misclassify (e.g., thinking whales are fish) before arriving at the correct classification [18][19]. - The study also highlighted that in logical reasoning tasks, both AI and humans can be misled by common misconceptions, leading to similar paths of confusion before reaching the correct conclusion [21][24]. Group 4: Implications - The findings suggest that understanding the internal "thought processes" of AI could provide insights into human cognitive challenges and potentially guide experimental design in cognitive science [24].