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Record W2049822216 · doi:10.1111/ejn.12339

The influence of dopamine‐related genes on perceptual stability

2013· article· en· W2049822216 on OpenAlexaff
Katharina Schmack, Maria Sekutowicz, Hannes Rössler, Eva J. Brandl, Daniel J. Müller, Philipp Sterzer

Bibliographic record

VenueEuropean Journal of Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsCentre for Addiction and Mental Health
FundersDeutsche Forschungsgemeinschaft
KeywordsPerceptionBistabilityPsychologyDopaminergicAlternation (linguistics)NeuroscienceTraitDopamineDevelopmental psychologyComputer science

Abstract

fetched live from OpenAlex

Bistable perception is the spontaneous and automatic alternation between two different perceptual states that occurs when sensory information is ambiguous. Perceptual alternation rates are robust within individuals but vary substantially between individuals. Slowed perceptual switching has been consistently reported in patients with bipolar disorder (BPD) and has been suggested as a trait marker for this disease. Although genetic factors have been implicated in both BPD and bistable perception, the underlying biological mechanisms that mediate the observed perceptual stability in BPD remain elusive. Here, we tested the effect of two variable number tandem repeat (VNTR) polymorphisms in DRD4 and DAT1 (SLC6A3), both candidate genes for BPD with functional impact on dopaminergic neurotransmission, on bistable perception in a cohort of 108 healthy human subjects. The BPD risk allele DRD4-2R was significantly associated with slow perceptual switching. There was no effect of DAT1 genotype on bistable perception. Our findings indicate that genetic differences in dopaminergic neurotransmission linked to BPD also account for interindividual variability in bistable perception, thus providing a genetic basis for perceptual stability as a trait marker of BPD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.293
Teacher spread0.228 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations23
Published2013
Admission routes1
Has abstractyes

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