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Record W2786951144 · doi:10.1371/journal.pbio.2005429

Fruit flies are multistable geniuses

2018· article· en· W2786951144 on OpenAlexaff
Christopher C. Pack, Jamie C. Theobald

Bibliographic record

VenuePLoS Biology · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiologySensory systemStimulus (psychology)PerceptionSensationNeuroscienceSensory cueCommunicationCognitive psychologyPsychology

Abstract

fetched live from OpenAlex

Our sensory systems have evolved to provide us with information about the external world. Such information is useful only insofar as it leads to actions that enhance fitness, and thus, the link between sensation and action has been thoroughly studied in many species. In insects, for example, specific visual stimuli lead to highly stereotyped responses. In contrast, humans can exhibit a wide range of responses to the same stimulus, as occurs most notably in the phenomenon of multistable perception. On this basis, one might think that humans have a fundamentally different way of generating actions from sensory inputs, but Toepfer et al. show that flies show evidence of multistable perception as well. Specifically, when confronted with a sensory stimulus that can yield different motor responses, flies switch from one response to another with temporal dynamics that are similar to those of humans and other animals. This suggests that the mechanisms that give rise to the rich repertoire of sensory experience in humans have correlates in much simpler nervous systems.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.094
GPT teacher head0.332
Teacher spread0.238 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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