Attachment: a predictive coding approach
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908364152242176 |
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| author | Lin, Anthony |
| author_facet | Lin, Anthony |
| contents | We introduce a novel predictive coding framework for studying attachment theory. Building off an established model of attachment, the dynamic-maturational model (DMM), as well as the neuroanatomical Embodied Predictive Interoception Coding (EPIC) model of interoception and emotion, we not only elucidate how neural processes can shape attachment strategies, but also explore how early attachment experiences can shape those processes in the first place. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05476 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Attachment: a predictive coding approach Lin, Anthony Neurons and Cognition We introduce a novel predictive coding framework for studying attachment theory. Building off an established model of attachment, the dynamic-maturational model (DMM), as well as the neuroanatomical Embodied Predictive Interoception Coding (EPIC) model of interoception and emotion, we not only elucidate how neural processes can shape attachment strategies, but also explore how early attachment experiences can shape those processes in the first place. |
| title | Attachment: a predictive coding approach |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2505.05476 |