MétaCan
Menu
Back to cohort

Emotional Objectivity: Neural Representations of Emotions and Their Interaction with Cognition

2019· review· en· W2980422367 on OpenAlexafffund
Rebecca M. Todd, Vladimir Miskovic, Junichi Chikazoe, Adam K. Anderson

Bibliographic record

VenueAnnual Review of Psychology · 2019
Typereview
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Mental HealthJapan Society for the Promotion of ScienceCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsPsychologyCognitionPerceptionCognitive psychologyObjectivity (philosophy)Cognitive scienceEmotion perceptionObject (grammar)Two-factor theory of emotionRepresentation (politics)Affective scienceEmotion workEpistemologyNeuroscience

Abstract

fetched live from OpenAlex

Recent advances in our understanding of information states in the human brain have opened a new window into the brain's representation of emotion. While emotion was once thought to constitute a separate domain from cognition, current evidence suggests that all events are filtered through the lens of whether they are good or bad for us. Focusing on new methods of decoding information states from brain activation, we review growing evidence that emotion is represented at multiple levels of our sensory systems and infuses perception, attention, learning, and memory. We provide evidence that the primary function of emotional representations is to produce unified emotion, perception, and thought (e.g., "That is a good thing") rather than discrete and isolated psychological events (e.g., "That is a thing. I feel good"). The emergent view suggests ways in which emotion operates as a fundamental feature of cognition, by design ensuring that emotional outcomes are the central object of perception, thought, and action.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.233
GPT teacher head0.497
Teacher spread0.263 · 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 designOther design
Domainnot available
GenreReview

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

Citations103
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueAnnual Review of PsychologySame topicNeural and Behavioral Psychology StudiesFrench-language works237,207