Between Fascination and Fear

This paper reflects on the current hyperpresence of artificial intelligence in everyday life and the way society oscillates between fascination and fear in response to its expansion. Through a historical and conceptual approach, the essay argues that AI does not operate as an autonomous agent, but rather as an amplifier of the human framework from which it is used. Within this context, the Hybrid Cognitive-Affective Model (HCAM) is introduced as a proposal for a conscious, responsible, and situated integration between the human and the artificial, in a scenario increasingly shaped by the virtualization of reality.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-29
DOI
https://doi.org/10.5281/zenodo.19314021
Primary Topic
Embodied and Extended Cognition
Type
article
Field-Weighted Citation Impact
0.00
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article

Between Fascination and Fear

Adrian Gabriel Muniello
Zenodo (CERN European Organization for Nuclear Research)
Embodied and Extended Cognition
article

Between Fascination and Fear

Adrian Gabriel Muniello
article en

Abstract

This paper reflects on the current hyperpresence of artificial intelligence in everyday life and the way society oscillates between fascination and fear in response to its expansion. Through a historical and conceptual approach, the essay argues that AI does not operate as an autonomous agent, but rather as an amplifier of the human framework from which it is used. Within this context, the Hybrid Cognitive-Affective Model (HCAM) is introduced as a proposal for a conscious, responsible, and situated integration between the human and the artificial, in a scenario increasingly shaped by the virtualization of reality.

Zenodo (CERN European Organization for Nuclear Research)
Reduced inequalities
Openalex Percentile: Top 5%
Embodied and Extended Cognition
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Between Fascination and Fear — Adrian Gabriel Muniello · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS