预测编码和主动推理的经验状态
Rowan Hodson1, Marishka Mehta1, Ryan Smith1
1Laureate Institute for Brain Research, USA.
Neuroscience and biobehavioral reviews
|November 29, 2023
概括
预测编码和主动推理等预测处理模型显示出希望,但需要更多的实证测试. 目前的证据提供了适度的支持,需要进一步的研究才能完全验证.
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.
- 理论心理学 理论心理学
背景情况:
- 预测处理模型,包括预测编码 (感知) 和主动推理 (决策),提供广泛的解释框架.
- 这些理论最近开始经验评估,促使对它们目前的证据支持进行审查.
- 这些模型的相互联系凸显了它们在跨认知功能之间统一理解的潜力.
研究的目的:
- 批判性地审查最近评估预测编码和主动推理的经验研究.
- 评估这些预测处理算法的经验支持程度.
- 确定研究缺陷,并建议未来的方向验证这些理论.
主要方法:
- 系统审查关于预测编码和主动推理的最新实证研究.
- 对研究的分析,重点是经验评估和模型与行为数据相适应.
- 结果与替代计算模型进行比较.
主要成果:
- 经验证据对预测编码提供了适度的支持,尽管替代的前模型也可以解释一些发现.
- 主动推理模型通常很适合行为数据,但验证通常涉及解释个体差异,而不是直接测试理论.
- 与非贝叶斯式或无模型的强化学习模型进行正式比较对于主动推理缺乏.
结论:
- 虽然预测编码和主动推理是有希望的,但它们的经验充分性需要进一步调查.
- 需要进行具体的研究,以严格测试这些预测处理算法的有效性和解释能力.
- 未来的研究应该优先考虑正式的模型比较,以确定这些理论的独特贡献.
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