This study adopts an intentionally unconventional methodology!

The rationale for this approach rests on two principal considerations. First, the inquiry was conducted by an individual with lived experience of cognitive function variability, rather than by a conventionally credentialed domain expert. This positionality was deliberately retained, as it offers an experiential perspective that may surface questions, patterns, and interpretations that are less readily apparent within traditional expert-led frameworks.

Second, the primary investigative tool employed in this study was a Generative Artificial Intelligence model, specifically ChatGPT version 4.0. The model was used as an exploratory instrument to interrogate, synthesise, and contextualise existing knowledge across cognitive and neural domains. This approach reflects an emerging paradigm in which large language models are leveraged not as authoritative sources, but as hypothesis-generating and pattern-exploration tools.

The limitations of this methodology are explicitly acknowledged. Generative Artificial Intelligence models, particularly earlier iterations, are susceptible to hallucinations, whereby outputs may be generated without adequate evidentiary grounding. Consequently, the findings derived from this process are not presented as definitive conclusions, but as provisional insights intended to stimulate further empirical investigation.

Notwithstanding these constraints, this methodological approach is justified on the grounds of its potential to generate novel conceptual linkages and research hypotheses. Furthermore, it explores the feasibility of using large language models as adjunctive tools in scientific inquiry. These models encapsulate extensive, cross-domain knowledge repositories with advanced retrieval and synthesis capabilities. Systematic evaluation in future studies will be required to establish their reliability, validity, and appropriate role within rigorous research methodologies.