Extensive Phenomenological Overlap between Training-Induced and Naturally-Occurring Synaesthetic Experiences
Bibliographic record
Abstract
Synaesthesia is a condition defined by additional perceptual experiences, which are automatically and consistently triggered by specific inducing stimuli. The associative nature of synaesthesia has motivated attempts to induce synaesthesia by means of associative learning. Two recent studies of this kind highlighted the potential for perceptual plasticity even in adulthood, by demonstrating that extensive associative training can generate not only behavioural and neurophysiological markers of synaesthesia, but also synaesthesia-like phenomenology. However, while the results of these studies provided tantalising evidence that a learning component may be involved in the development of synesthetic phenomenology, they only provided superficial descriptions regarding the training-related changes in induced synaesthesia-like (ISL) experience. Therefore, it was not possible to assess how closely the phenomenology of ISL and naturally occurring grapheme-colour synaesthesia (NOS) overlap. Here, we addressed this question by providing a new extended qualitative analysis of the phenomenological changes associated with learning new perceptual phenomenology (ISL group) and comparing the descriptive similarities in colour experience to equivalent qualitative data acquired from a new group of NOS participants. Using this approach, we were able to directly compare associated colour experiences between the ISL and NOS group to assess how closely these two types of novel perceptual experience align. Our results reveal that induced and synaesthetic experience are remarkably similar, displaying a high degree of phenomenological overlap across multiple experiential categories, including: stability of experience, location of colour experience, shape of co-occurring colour experience, relative strength of colour experience and automaticity of colour experience. Our results exemplify the benefits of qualitative methods by providing new evidence that intensive training of letter-colour associations can alter conscious perceptual experiences in non-synaesthetes, and that such alterations produce synaesthesia-like phenomenology, which substantially resembles experiences described in natural grapheme-colour synaesthesia. Our results have implications for the plasticity of visual perception and the role of learning and development in establishing perceptual traits.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".