Bibliographic record
Abstract
It is always difficult to even advance possible dimensions of consciousness, but Birch et al., 2020 have suggested four possible dimensions and this review discusses the first, perceptual richness, with relation to octopuses. They advance acuity, bandwidth, and categorization power as possible components. It is first necessary to realize that sensory richness does not automatically lead to perceptual richness and this capacity may not be accessed by consciousness. Octopuses do not discriminate light wavelength frequency (color) but rather its plane of polarization, a dimension that we do not understand. Their eyes are laterally placed on the head, leading to monocular vision and head movements that give a sequential rather than simultaneous view of items, possibly consciously planned. Details of control of the rich sensorimotor system of the arms, with 3/5 of the neurons of the nervous system, may normally not be accessed to the brain and thus to consciousness. The chromatophore-based skin appearance system is likely open loop, and not available to the octopus’ vision. Conversely, in a laboratory situation that is not ecologically valid for the octopus, learning about shapes and extents of visual figures was extensive and flexible, likely consciously planned. Similarly, octopuses’ local place in and navigation around space can be guided by light polarization plane and visual landmark location and is learned and monitored. The complex array of chemical cues delivered by water and on surfaces does not fit neatly into the components above and has barely been tested but might easily be described as perceptually rich. The octopus’ curiosity and drive to investigate and gain more information may mean that, apart from richness of any stimulus situation, they are consciously driven to seek out more information. This review suggests that cephalopods may not have a similar type of intelligence as the ‘higher’ vertebrates, they may not have similar dimensions or contents of consciousness, but that such a capacity is present nevertheless.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".