More than words: can free reports adequately measure the richness of perception?
Bibliographic record
Abstract
The question of the richness (or sparseness) of conscious experience has evoked ongoing debate and discussion. Claims for both richness and sparseness are supported by empirical data, yet they are often indirect, and alternative explanations have been put forward. Recently, it has been suggested that current experimental methods limit participants' responses, thereby preventing researchers from assessing the actual richness of perception. Instead, free verbal reports were presented as a possible way to overcome this limitation. As part of this approach, a novel paradigm of freely reported words was developed using a new metric, intersubjective agreement (IA), with experimental results interpreted as capturing aspects of conscious perception. Here, we challenge the validity of freely reported words as a tool for studying the richness of conscious experience. We base our claims on two studies (each composed of three experiments), where we manipulated the richness of percepts and tested whether IA changed accordingly. Five additional control experiments were conducted to validate the experimental logic and examine alternative explanations. Our results suggest otherwise, presenting four challenges to the free verbal report paradigm: first, impoverished stimuli did not evoke lower IA scores. Second, the IA score was correlated with word frequency in English. Third, the original positive relationship between IA scores and rated confidence was not found in any of the six experiments. Fourth, a high rate of nonexisting words was found, some of which described items that matched the gist of the scene but did not appear in the image. We conclude that a metric based on freely reported words might be better explained by vocabulary conventions and gist-based reports than by capturing the richness of perception.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".