在人类人数解码器中出现行为现象和适应效应,使用循环神经网络
Bhavesh K Verma1, Rakesh Sengupta2
1Indian Institute of Science Education and Research, Pune, 411008, India.
Scientific reports
|November 10, 2023
概括
这项研究引入了一种新的数字感知计算模型,模拟大脑网络以准确地表示从1到50的数字,并解释诸如适应效应等行为现象.
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.
- 数学心理学 数学心理学
背景情况:
- 人类具有对视觉数量感知,集合的基本性等天生的能力.
- 侧侧内皮层 (LIP) 和内皮层 (IPS) 是数字处理的关键大脑区域.
- 现有的数字感知计算模型在范围和行为特征模拟方面存在局限性.
研究的目的:
- 开发一个新的数字感知计算模型.
- 为了解释更广泛的数量和行为特征,如适应效应.
- 阐明了数字感知背后的神经机制.
主要方法:
- 利用具有自我激发和相互抑制特性的神经网络.
- 在稳定状态下假设的平均网络激活编码数量,与设置大小的单调增长关系.
- 优化抑制强度,以覆盖不同的数值范围 (1-4, 5-17, 21-50).
主要成果:
- 该模型成功地编码了三个不同的间隔中的数量,与行为断点保持一致.
- 实现了将平均激活值解码到从1到50的连续尺度.
- 提出了一个动态抑制强度选择机制,用于扩展数量范围的操作.
结论:
- 这种新的计算模型为数字感知的神经基质提供了洞察力.
- 该模型解释了各种行为现象,包括适应效应和持续的视觉属性影响.
- 这项工作推动了我们对大脑如何处理数字信息的理解.
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