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Record W1942688222 · doi:10.1177/1948550609359202

More Memory Bang for the Attentional Buck

2010· article· en· W1942688222 on OpenAlexaff
D. Vaughn Becker, Uriah S. Anderson, Steven L. Neuberg, Jon K. Maner, Jenessa R. Shapiro, Joshua M. Ackerman, Mark Schaller, Douglas T. Kenrick

Bibliographic record

VenueSocial Psychological and Personality Science · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of British Columbia
FundersArmy Research Institute for the Behavioral and Social SciencesNational Institute of Mental HealthNational Science Foundation
KeywordsPsychologyDilemmaHarmSocial psychologyEncoding (memory)Cognitive psychologyStaringCommunication

Abstract

fetched live from OpenAlex

When encountering individuals with a potential inclination to harm them, people face a dilemma: Staring at them provides useful information about their intentions but may also be perceived by them as intrusive and challenging-thereby increasing the likelihood of the very threat the people fear. One solution to this dilemma would be an enhanced ability to efficiently encode such individuals-to be able to remember them without spending any additional direct attention on them. In two experiments, the authors primed self-protective concerns in perceivers and assessed visual attention and recognition memory for a variety of faces. Consistent with hypotheses, self-protective participants (relative to control participants) exhibited enhanced encoding efficiency (i.e., greater memory not predicated on any enhancement of visual attention) for Black and Arab male faces-groups stereotyped as being potentially dangerous-but not for female or White male faces. Results suggest that encoding efficiency depends on the functional relevance of the social information people encounter.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.009
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.095
GPT teacher head0.457
Teacher spread0.362 · 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; both teacher heads agree on what is shown here.

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

Citations50
Published2010
Admission routes1
Has abstractyes

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