感知专业知识和注意力:使用深度神经网络的探索
Soukhin Das1,2, G R Mangun1,2,3, Mingzhou Ding3
1Center for Mind and Brain, University of California, Davis.
bioRxiv : the preprint server for biology
|October 28, 2024
概括
感知专业知识增强了对象识别,基于特征的注意力 (FBA) 仅在专家领域内提高了性能. 专业知识之外的注意力显示了减少的好处,突出了神经调的作用.
科学领域:
- 认知神经科学 认知神经科学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 感知专业知识和注意力对于对象识别和任务执行至关重要.
- 专家和新手之间注意力机制被认为是不同的,但这仍然是不充分研究的.
研究的目的:
- 研究感知专业知识和注意力之间的相互作用.
- 通过使用计算模型,探索基于特征的注意力 (FBA) 如何影响专家领域内外的识别.
主要方法:
- 利用卷积神经网络 (CNN) 作为灵长类视觉通路的模型.
- 培训了两名CNN模特,专门从事面部或场景识别.
- 评估了FBA对复杂刺激的表现的影响,包括叠加图像.
主要成果:
- 专家模型在他们的训练领域表现出了卓越的表现.
- FBA显著提高了性能 (在场景中高达35%,在面孔中高达15%),但仅限于专业知识领域.
- 专业领域之外的关注对业绩产生了减少或负面影响.
结论:
- 专业知识导致对特定类别的特征进行增强的神经调,通过减少代表性竞争来改善识别.
- 在神经科学研究中,CNN作为有价值的计算工具,特别是在研究注意力和专业知识方面.
- 神经调对于区分专家与新手的注意力效应至关重要.
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