不同的感知偏差从赫比安可塑性中出现,在一个反复的神经网络模型中
Francesca Schönsberg1, Davide Giana2, Yukti Chopra2
1Laboratory of Physics of the Ecole Normale Supérieure, PSL and CNRS UMR8023, Sorbonne Université, Paris, France; Department of Physics, International School for Advanced Studies (SISSA), Trieste, Italy.
Neuron
|November 6, 2025
概括
收缩和排斥是两个感知偏差. 一个新的神经网络模型表明,这些偏差来自于在单个反复的神经网络中持续的赫比安可塑性,统一了这些不同的效应.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 认知科学 认知科学
背景情况:
- 感知偏差,如收缩和排斥,揭示了大脑如何处理感官信息.
- 心理物理学已经确定了这些系统偏见,但缺乏统一的神经模型.
研究的目的:
- 开发一个单一的循环神经网络模型,解释学习中收缩性和排斥性感知偏差的出现.
- 为了证明一个简单的,本地学习规则可以解释看似矛盾的感知现象.
主要方法:
- 开发了一种循环神经网络模型,该模型包含连续的Hebbian可塑性.
- 该模型与两个感官模式和各种记忆任务 (工作记忆,参考记忆,单回任务) 的四个不同的数据集进行了测试.
主要成果:
- 该模型成功地预测了实验数据中观察到的收缩和排斥偏差.
- 在模型预测和经验结果之间发现了很好的一致性,而无需对特定任务进行微调.
- 该模型在不同的任务和感官模式中表现出了稳健性.
结论:
- 在单个循环神经网络中,连续的Hebbian可塑性可以产生收缩性和排斥性感知偏差.
- 一个统一的,简单的,本地学习规则可以解释不同的感知偏见,挑战它们矛盾性质的概念.
- 这个模型为理解感知学习和偏见形成提供了一个节的神经基础.
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