Synesthesia and self-organizing learning: How are synesthetic concurrents generated and why do they not disappear?
Synesthesia and self-organizing learning: How are synesthetic concurrents generated and why do they not disappear?
复制标题
联觉和自组织学习：联觉并发是如何产生的以及为什么它们不会消失？
DOI：
10.11225/cs.2021.059
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
牧岡 省吾
中科院分区：
文献类型：
--
作者：
牧岡 省吾
联觉是一种特定刺激持续且自动诱发额外意识体验的现象。引起联觉的刺激称为诱导物，由诱导物引起的感觉称为并发感觉。本文围绕以下两个问题进行组织：（1）并发是如何产生的？（2）为什么与外部刺激不一致的并发不会通过学习消失？问题（1）已通过模式之间的先天联系或习得关联来解释。然而，仅仅假设联运连接并不能解释联觉中观察到的规律性和不规则性的混合。在本文中，我们讨论了作者提出的空间序列联觉的自组织模型，关于联觉和非联觉之间共性的心理学实验，以及这些实验表明联觉和非联觉之间发生模态自组织学习的可能性。许多感知理论假设学习是以最小化内部模型预测与感觉输入之间的误差的方式进行的。对于深度学习网络来说也是如此。这种学习应该努力消除与外部刺激不一致的并发，但并发并不会在联觉中消失。这就引出了问题（2），我们根据 Seth（2014）对分层生成模型的讨论、Gershman（2019）对对抗性生成网络的讨论以及 Cleeremans 等人（2020）对意识的自组织元表征解释来讨论这个问题。
Synesthesia is a phenomenon in which specific stimuli consistently and automatically induce additional conscious experiences. The stimuli that cause synesthesia are called inducers, and the sensations evoked by inducers are called concurrents. This paper is organized around the following two questions.(1) How are concurrents generated?(2) Why do concurrents that are inconsistent with external stimuli not disappear through learning? Question (1) has been explained by innate connections or learned associations between modalities. However, the mere assumption of intermodal connections cannot explain the mixture of regularity and irregularity observed in synesthesia. In this paper, we discuss the self-organizing model of spatial sequence synesthesia proposed by the author, psychological experiments on the commonalities between synesthetes and nonsynesthetes, and the possibility that these experiments indicate that self-organizing learning between modalities takes place in both synesthetes and nonsynesthetes. Many theories of perception assume that learning takes place in such a way as to minimize the error between the predictions made by the internal model and the sensory input. This is also true for deep learning networks. Such learning should work to eliminate concurrents that are inconsistent with external stimuli, but concurrents do not disappear in synesthetes. This leads to question (2), and we discuss this issue in light of Seth's (2014) discussion of hierarchical generative models, Gershman's (2019) discussion of adversarial generative networks, and Cleeremans et al.'s (2020) self-organizing metarepresentational account of consciousness.