MétaCan
Menu
Back to cohort
Record W1503110770 · doi:10.3389/fpsyg.2015.00975

Emotional words can be embodied or disembodied: the role of superficial vs. deep types of processing

2015· review· en· W1503110770 on OpenAlexafffund
Ensie Abbassi, Isabelle Blanchette, Ana Inés Ansaldo, Habib Ghassemzadeh, Yves Joanette

Bibliographic record

VenueFrontiers in Psychology · 2015
Typereview
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
FundersCanadian Institutes of Health Research
KeywordsPsychologyEmbodied cognitionCognitive psychologyPerceptionWord processingInformation processingSalientWord (group theory)Levels-of-processing effectContrast (vision)Right hemisphereCognitionLinguisticsNatural language processingComputer scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Emotional words are processed rapidly and automatically in the left hemisphere (LH) and slowly, with the involvement of attention, in the right hemisphere (RH). This review aims to find the reason for this difference and suggests that emotional words can be processed superficially or deeply due to the involvement of the linguistic and imagery systems, respectively. During superficial processing, emotional words likely make connections only with semantically associated words in the LH. This part of the process is automatic and may be sufficient for the purpose of language processing. Deep processing, in contrast, seems to involve conceptual information and imagery of a word's perceptual and emotional properties using autobiographical memory contents. Imagery and the involvement of autobiographical memory likely differentiate between emotional and neutral word processing and explain the salient role of the RH in emotional word processing. It is concluded that the level of emotional word processing in the RH should be deeper than in the LH and, thus, it is conceivable that the slow mode of processing adds certain qualities to the output.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.359
Teacher spread0.318 · 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 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

Citations28
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in PsychologySame topicLanguage, Metaphor, and CognitionFrench-language works237,207