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Record W1515895933 · doi:10.1007/s10683-015-9454-z

Keeping others in our mind or in our heart? Distribution games under cognitive load

2015· article· en· W1515895933 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueExperimental Economics · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsSmiths Detection (Canada)
FundersNorges Forskningsråd
KeywordsDictator gameDictatorCognitive loadCognitionSocial psychologyAffect (linguistics)Frame (networking)PsychologyEconomicsTest (biology)MicroeconomicsComputer sciencePolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

Abstract It has recently been argued that giving is spontaneous while greed is calculated (Rand et al., in Nature 489:427–430, 2012). If greed is calculated we would expect that cognitive load, which is assumed to reduce the influence of cognitive processes, should affect greed. In this paper we study both charitable giving and the behavior of dictators under high and low cognitive load to test if greed is affected by the load. This is tested in three different dictator game experiments. In the dictator games we use both a give frame, where the dictators are given an amount that they may share with a partner, and a take frame, where dictators may take from an amount initially allocated to the partner. The results from all three experiments show that the behavioral effect in terms of allocated money of the induced load is small if at all existent. At the same time, follow-up questions indicate that the subjects’ decisions are more impulsive and less driven by their thoughts under cognitive load.

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.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.087
GPT teacher head0.386
Teacher spread0.299 · 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