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Record W4280565926 · doi:10.3389/fnbeh.2022.797119

Surprising Threats Accelerate Conscious Perception

2022· article· en· W4280565926 on OpenAlexaff
Jessica McFadyen, Naotsugu Tsuchiya, Jason B. Mattingley, Marta I. Garrido

Bibliographic record

VenueFrontiers in Behavioral Neuroscience · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsCanadian Institute for Advanced Research
FundersCentre of Excellence for Integrative Brain Function, Australian Research CouncilAustralian Research CouncilMedical Research CouncilNational Health and Medical Research CouncilWellcome Trust
KeywordsPsychologyPerceptionElectroencephalographyCognitive psychologyTrait anxietyAnxietyTraitSensory systemNeuroscienceComputer science

Abstract

fetched live from OpenAlex

The folk psychological notion that "we see what we expect to see" is supported by evidence that we become consciously aware of visual stimuli that match our prior expectations more quickly than stimuli that violate our expectations. Similarly, "we see what we want to see," such that more biologically-relevant stimuli are also prioritised for conscious perception. How, then, is perception shaped by biologically-relevant stimuli that we did not expect? Here, we conducted two experiments using breaking continuous flash suppression (bCFS) to investigate how prior expectations modulated response times to neutral and fearful faces. In both experiments, we found that prior expectations for neutral faces hastened responses, whereas the opposite was true for fearful faces. This interaction between emotional expression and prior expectations was driven predominantly by participants with higher trait anxiety. Electroencephalography (EEG) data collected in Experiment 2 revealed an interaction evident in the earliest stages of sensory encoding, suggesting prediction errors expedite sensory encoding of fearful faces. These findings support a survival hypothesis, where biologically-relevant fearful stimuli are prioritised for conscious access even more so when unexpected, especially for people with high trait anxiety.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.200
GPT teacher head0.393
Teacher spread0.193 · 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.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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