To support journal editors and peer-reviewers in evaluating the journal paper submission “The Xzistor Mathematical Model of Mind: A Novel Cognitive Architecture Bridging Neuroscience and Artificial Intelligence”, this page provides the key inputs and advice received from AI platforms.
The information is broadly structured to follow the flow of the paper.
To create this journal paper, the authors used all possible sources of information including own knowledge and records, advice from experts, books/papers/posts/videos mostly found online and a number of AI platforms including ChatGPT, Grok, Gemini, Copilot and Storm (Stanford University).
Since the developer of the Xzistor Mathematical Model of Mind, Rocco Van Schalkwyk from the Xzistor LAB, spent many years (2 decades) holding back on releasing detail information on the Xzistor brain model due to safety concerns, its online presence is still limited. It is only now that a credible safety case can be provided as to the safeguards that will constrain the model from full-AGI and ‘runaway intelligence’ – interpretable and rooted in physics – that the developer has approved making more information available to the public on the Xzistor Mathematical Model of Mind.
With the limited online exposure, brain scientists have been mostly sceptical about the model – as it introduces numerous unorthodox mechanisms not accounted for by current brain theories and cognitive architetures. Their responses to date reflected a more dogmatic approach, harking back to the prominent and celebrated models and approaches attachted to well-known high-profile academics, not even prepared to evaluate the substantive evidence provided by the Xzistor LAB e.g. virtual simulations and physical robots.
The response from AI platforms has been markedly different!
The responses from the AI platforms were overwhelmingly positive and complimentary. This confidence in the model grew even stronger once their blindspots were identified and they were provided with clearer and more comprehensive information – whereby they could deepen their grasp on the model and compare it with the other models out there.
The eventual inputs elicited from these AI platforms reflected a highly accurate and useful interpretation of what the Xzistor brain model is and how it can be differentiated from its peers. The ciriticisms put forward by the AI platforms showed insight and a kind of ‘understanding’ of the model that rivalled most academics outside of the Xzistor LAB.
Provided below are selected Prompts with Responses from these AI platforms that were worked into the paper, after checking its validity (mainly through citeable papers by renowned researchers from the academic literature).
Important: It is important to note that at all times during the production of the paper, the lead author (supported by his co-authors), remained the ‘controlling mind’ behind the paper. With over 30 years’ experience of developing the Xzistor model, and having personally programmed the simulations and built the physical robots, he is ready to defend the content (every word and sentence used) – as well as all cited material.
The Title
The title of the paper was proposed by AI and accepted by the authors.
Abstract
The abstract of the paper was proposed by AI and modified by the authors.
Keywords
The keywords of the paper were proposed by AI and accepted by the authors.
Main Body of Paper
The authors identified the key themes the paper needed to cover and prompted AI platforms to write a journal paper of 6, 10, 14 pages covering these themes. This was just to see which of the key elemements the AI was high-lighting in the paper and the proposed structure. The shorter papers proved more workable and useful.
Below are 3 examples of the AI platform responses received:
GROK
Gemini
ChatGPT
Storm
Thematic Searches by AI Platforms
Some topics/themes covered in the paper was subjected to more in depth queries (prompts) directed at AI platforms. Key responses that informed the paper are shown below.
Xzistor Model of Homeostatic Loops
Xzistor Model of Somatosensory Emotions
Xzistor Model Association Forming & Operant Learning
Xzistor Threading Mechanism
Xzistor Model of Default Mode Network (DMN)
Xzistor Model of Limbic System
Xzistor Model of Prediction Errors
Xzistor Model Explanation of ‘Gut Feeling’
Xzistor Model Explanation of Consciousness (Summary)
Compare Xzistor with most prominent Brain Theories and Cognitive Architectures
Compare Xzistor with most prominent Emotion Theories
Provide the main criticims and concerns of the Xzistor brain model.