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Record W2099734822 · doi:10.3389/fpsyg.2015.01487

Variability in social reasoning: the influence of attachment security on the attribution of goals

2015· article· en· W2099734822 on OpenAlexafffund
Kristen A. Dunfield, Susan Johnson

Bibliographic record

VenueFrontiers in Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsConcordia University
FundersFonds de Recherche du Québec-Société et CultureConcordia UniversityOhio State University
KeywordsPsychologyAttributionCognitionDistressSocial cognitionProblem of universalsSimilarity (geometry)Theory of mindSocial psychologyContradictionCognitive psychologyDevelopmental psychologyEpistemologyClinical psychology

Abstract

fetched live from OpenAlex

Over the last half decade there has been a growing move to apply the methods and theory of cognitive development to questions regarding infants' social understanding. Though this combination has afforded exciting opportunities to better understand our species' unique social cognitive abilities, the resulting findings do not always lead to the same conclusions. For example, a growing body of research has found support for both universal similarity and individual differences in infants' social reasoning about others' responses to incomplete goals. The present research examines this apparent contradiction by assessing the influence of attachment security on the ability of university undergraduates to represent instrumental needs versus social-emotional distress. When the two varieties of goals were clearly differentiated, we observed a universally similar pattern of results (Experiments 1A/B). However, when the goals were combined, and both instrumental need and social-emotional distress were presented together, individual differences emerged (Experiments 2 and 3). Taken together, these results demonstrate that by integrating the two perspectives of shared universals and individual differences, important points of contact can be revealed supporting a deeper, more nuanced understanding of the nature of human social reasoning.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 integrity0.0000.000
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 head0.030
GPT teacher head0.345
Teacher spread0.315 · 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 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

Citations6
Published2015
Admission routes2
Has abstractyes

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