流体智能的最小计算基质流体智能
Amy P K Nelson1, Joe Mole2, Guilherme Pombo1
1High Dimensional Neurology Group, UCL Queen Square Institute of Neurology, University College London, Russell Square House, Bloomsbury, London, UK.
概括
一个新的人工神经网络LaMa在流体智能测试中获得了人类水平的分数,而无需特殊训练. 这表明矩阵式推理测试可能可以通过更简单的计算方法解决.
科学领域:
- 认知科学是一种认知科学.
- 人工智能的人工智能是人工智能.
- 神经科学是一个神经科学.
背景情况:
- 量化认知能力依赖于行为任务,但任务的具体性和通用性往往是不确定的.
- 雷文的高级渐进矩阵 (RAPM) 是一种流体智能的常见临床测试.
研究的目的:
- 为了评估在自然场景中训练的自我监督的人工神经网络 (LaMa) 是否可以解决RAPM测试.
- 调查拉马的表现和错误模式是否模仿人类的表现,特别是与大脑功能有关的表现.
主要方法:
- 一个自我监督的神经网络LaMa被训练在图像完成任务上.
- 在一个紧的RAPM上LaMa的表现与人类参与者 (健康和脑损伤) 相比较.
- 在降低空间模式整合的条件下,分析了LaMa的错误模式.
主要成果:
- 在RAPM测试中,LaMa在没有特定任务培训或诱导偏见的情况下获得了人类水平的分数.
- 拉马展示了类似人类的项目难度变化和错误模式.
- 在空间整合挑战下LaMa的错误与右额叶损伤时看到的错误相似.
结论:
- 矩阵式智能测试可能容易受到计算上简单的解决方案的影响.
- 这些解决方案可能不一定涉及通常与推理相关的神经基质.
- 拉马的表现挑战了关于流体智力测试背后的认知过程的假设.
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