无论是神经网络还是思维语言,都不能单独构成一个完整的游戏
Iris Oved1, Nikhil Krishnaswamy2, James Pustejovsky3
1Independent Scholar, 911 Central Ave; San Francisco, CA, USA irisoved@gmail.com, irisoved@paradoxlab.org.
The Behavioral and brain sciences
|September 28, 2023
概括
认知科学辩论经验主义与本土主义. 虽然深度神经网络恢复了经验主义,但思想语言表征仍然至关重要,但需要在更广泛的认知科学中理解它们的背景.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 思想的哲学 思想的哲学
背景情况:
- 认知科学中的历史辩论集中在激进的经验主义与激进的原生主义之间.
- 深度神经网络 (NN) 模型的近期进展刺激了对经验主义观点的兴趣的复苏.
- 作者承认NN模型的成功,但主张采用更细微的视角.
研究的目的:
- 为了解决由于深度神经网络的成功而在认知科学中激进的经验主义的复兴.
- 根据当前的人工智能进步,重新评估语言类心理表征的作用和理解.
- 认为思想语言 (LoTs) 不能单独被完全理解.
主要方法:
- 认知科学中历史和当代理论的概念分析.
- 根据NN模型,批评纯粹经验主义解释的局限性.
- 在更广泛的认知框架内,论证思维语言 (LoTs) 的必要性的综合.
主要成果:
- 深度神经网络虽然很强大,但并不能取消结构化的,类似语言的心理表现的重要性.
- 思想语言 (LoTs) 被认为是解释复杂认知的必要条件.
- 作者强调,LOT必须与其他认知过程和理论构造相关理解.
结论:
- 经验主义和本地主义之间的辩论正在进行中,最近的AI发展增加了新的维度.
- 对认知的全面理解需要整合各种理论方法的见解,包括连接主义 (NN) 和代表性 (LoTs) 观点.
- 未来的研究应该关注思维语言如何与其他认知系统相互作用,而不是将其视为孤立的实体.
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