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Record W3112804570 · doi:10.1177/0956797620954856

Difficult Turned Easy: Suggestion Renders a Challenging Visual Task Simple

2020· article· en· W3112804570 on OpenAlexafffund
Mathieu Landry, Jason da Silva Castanheira, Jérôme Sackur, Amir Raz

Bibliographic record

VenuePsychological Science · 2020
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsJewish General HospitalMcGill University
FundersCanadian Institutes of Health Research
KeywordsHallucinatingPsychologyPerceptionCognitive psychologyTask (project management)Visual perceptionMental imageSensory systemCognitive scienceCognitionArtificial intelligenceNeuroscienceComputer science

Abstract

fetched live from OpenAlex

Suggestions can cause some individuals to miss or disregard existing visual stimuli, but can they infuse sensory input with nonexistent information? Although several prominent theories of hypnotic suggestion propose that mental imagery can change our perceptual experience, data to support this stance remain sparse. The present study addressed this lacuna, showing how suggesting the presence of physically absent, yet critical, visual information transforms an otherwise difficult task into an easy one. Here, we show how adult participants who are highly susceptible to hypnotic suggestion successfully hallucinated visual occluders on top of moving objects. Our findings support the idea that, at least in some people, suggestions can add perceptual information to sensory input. This observation adds meaningful weight to theoretical, clinical, and applied aspects of the brain and psychological sciences.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.078
GPT teacher head0.359
Teacher spread0.282 · 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

Citations9
Published2020
Admission routes2
Has abstractyes

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