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Record W3046375917 · doi:10.1101/2020.08.03.228692

Extensive Phenomenological Overlap between Induced and Naturally-Occurring Synaesthetic Experiences

2020· preprint· en· W3046375917 on OpenAlexafffund
David J. Schwartzman, Aleš Oblak, Nicolas Rothen, Daniel Bor, Anil K. Seth

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsCanadian Institute for Advanced Research
FundersErasmus+European CommissionCanadian Institute for Advanced Research
KeywordsAutomaticityPsychologyAssociative propertyPhenomenology (philosophy)Cognitive psychologyPerceptionSynesthesiaExperiential learningAssociative learningCognitionNeuroscienceEpistemologyMathematics

Abstract

fetched live from OpenAlex

Abstract Grapheme-colour synaesthesia (GCS) is defined by additional perceptual experiences, which are automatically and consistently triggered by specific inducing stimuli. The associative nature of GCS has motivated attempts to induce synaesthesia by means of associative learning. Two recent studies have shown that extensive associative training can generate not only behavioural (consistency and automaticity) and neurophysiological markers of GCS, but also synaesthesia-like phenomenology [1,2]. However, these studies provided only superficial descriptions regarding the training-related changes in subjective experience: they did not directly assess how closely induced synaesthetic experiences mirror those found in natural GCS. Here we report an extended qualitative analysis of the transcripts of the semi-structured interviews obtained following the completion of the associative training protocol used by [2]. In addition, we performed a comparable analysis of responses to an interview with a new population of natural occurring grapheme-colour synaesthetes (NOS), allowing us to directly compare the phenomenological dimensions of induced and naturally occurring synaesthetic experience. Our results provide an extensive addition to the description of the phenomenology of NOS experience, revealing a high degree of heterogeneity both within and across all experiential categories. Capitalising on this unique level of detail, we identified a number of shared experiential categories between NOS and induced synaesthesia-like (ISL) groups, including: stability of experience, location of colour experience, shape of co-occurring colour experience, relative strength of colour experience and automaticity of colour experience . Only the automaticity of colour experience differed significantly between the two groups: NOS experience was reported as being mostly automatic, whereas induced ISL were mostly described as being ‘wilful’. We observed three additional experiential categories relating to the automaticity of synaesthetic experience within the NOS group: contextually varied experience, semi-automatic experience and reflective association , which suggests that, as with other experiential categories, the automaticity of synaesthetic experience is also highly heterogeneous. Our results provide new evidence that 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 similarities to natural grapheme-colour synaesthesia.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.008
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.062
GPT teacher head0.304
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes2
Has abstractyes

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