Multidisciplinary Intersections on Artificial-Human Vividness: Phenomenology, Representation, and the Brain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article will explore the expressivity and tractability of vividness, as viewed from the interdisciplinary perspective of the cognitive sciences, including the sub-disciplines of artificial intelligence, cognitive psychology, neuroscience, and phenomenology. Following the precursor work by Benussi in experimental phenomenology, seminal papers by David Marks in psychology and, later, Hector Levesque in computer science, a substantial part of the discussion has been around a symbolic approach to the concept of vividness. At the same time, a similar concept linked to semantic memory, imagery, and mental models has had a long history in cognitive psychology, with new emerging links to cognitive neuroscience. More recently, there is a push towards neural-symbolic representations which allows room for the integration of brain models of vividness to a symbolic concept of vividness. Such works lead to question the phenomenology of vividness in the context of consciousness, and the related ethical concerns. The purpose of this paper is to review the state of the art, advances, and further potential developments of artificial-human vividness while laying the ground for a shared conceptual platform for dialogue, communication, and debate across all the relevant sub-disciplines. Within such context, an important goal of the paper is to define the crucial role of vividness in grounding simulation and modeling within the psychology (and neuroscience) of human reasoning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it