Carbon-Based Brain, Consciousness and Cognition: Understanding Cognitive States in the Context of the Kk Thesis
Bibliographic record
Abstract
This paper examines the nature of epistemic awareness, or cognitive states, in the context of the KK thesis and defines the relationship between cognitive states and neural states. The paper begins with examining what this indubitable epistemic awareness entails and ends with understanding when and why beliefs can be held as ‘true’ with, without, against or regardless of empirical evidence. Cognition is an existential mode of being accompanied by epistemic content. As such, ‘to know’ entails the following: to knowis to know that we know; know what we know; know how we know (what we know); and know that what we know is either true or false. In addition, to know is to be cognizant of why beliefs are considered basic or non-basic; when epistemic justification for belief is considered internal or external; when we have control over beliefs and when we do not; when evidence can provide the basis for beliefs; and when evidence cannot guarantee beliefs. It is noted that we do not choose to possess epistemic awareness, nor can we choose to negate epistemic awareness— we cannot choose not to know that we knowthough we may or may not choose to know what is known. From Gettier, we learn that TAK (JTB) can only prevent guessing but cannot prevent ‘lucky truth.’ Many authors have questioned what makes beliefs true or false, and how beliefs can be affirmed or falsified. Knowing what we know of the basis, process, content and truth of cognitive states,this paper endeavors to understand why our carbon-based brain allows us to hold beliefs with, without, against, or regardless of evidential consideration. Ideas from the following authors are examined: Plato, Gettier, Goldman, Searle, Rosenthal, Chalmers, Lehrer, Block,Pojman, and Sartre.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".