摘要：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.
