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Record W2169018224 · doi:10.1093/scan/nsu013

Escape from harm: linking affective vision and motor responses during active avoidance

2014· article· en· W2169018224 on OpenAlexfundno aff
Vladimir Miskovic, Andreas Keil

Bibliographic record

VenueSocial Cognitive and Affective Neuroscience · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsnot available
FundersNational Institute of Mental HealthCanadian Institutes of Health Research
KeywordsPsychologyAversive StimulusNeurosciencePerceptionSensory systemAction (physics)PopulationNeural correlates of consciousnessVisual perceptionSensory cueVisual processingAvoidance responseCognitive psychologyCognition

Abstract

fetched live from OpenAlex

When organisms confront unpleasant objects in their natural environments, they engage in behaviors that allow them to avoid aversive outcomes. Here, we linked visual processing of threat to its behavioral consequences by including a motor response that terminated exposure to an aversive event. Dense-array steady-state visual evoked potentials were recorded in response to conditioned threat and safety signals viewed in active or passive behavioral contexts. The amplitude of neuronal responses in visual cortex increased additively, as a function of emotional value and action relevance. The gain in local cortical population activity for threat relative to safety cues persisted when aversive reinforcement was behaviorally terminated, suggesting a lingering emotionally based response amplification within the visual system. Distinct patterns of long-range neural synchrony emerged between the visual cortex and extravisual regions. Increased coupling between visual and higher-order structures was observed specifically during active perception of threat, consistent with a reorganization of neuronal populations involved in linking sensory processing to action preparation.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.687
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.021
GPT teacher head0.285
Teacher spread0.264 · 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

Citations26
Published2014
Admission routes1
Has abstractyes

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