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Record W4389146467 · doi:10.1098/rsos.230053

A meta-analytic cognitive framework of nudge and sludge

2023· article· en· W4389146467 on OpenAlexafffund
Yu Luo, Andrew Li, Dilip Soman, Jiaying Zhao

Bibliographic record

VenueRoyal Society Open Science · 2023
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of TorontoUniversity of British Columbia
FundersEnvironment and Climate Change CanadaSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of CanadaCanada Research Chairs
KeywordsPsychological interventionCognitionIncentivePsychologyPerceptionCognitive psychologyApplied psychologyEconomicsNeuroscienceMicroeconomics

Abstract

fetched live from OpenAlex

Public and private institutions have gained traction in developing interventions to alter people's behaviours in predictable ways without limiting the freedom of choice or significantly changing the incentive structure. A nudge is designed to facilitate actions by minimizing friction, while a sludge is an intervention that inhibits actions by increasing friction, but the underlying cognitive mechanisms behind these interventions remain largely unknown. Here, we develop a novel cognitive framework by organizing these interventions along six cognitive processes: attention, perception, memory, effort, intrinsic motivation and extrinsic motivation. In addition, we conduct a meta-analysis of field experiments (i.e. randomized controlled trials) that contained real behavioural measures ( n = 184 papers, k = 184 observations, N = 2 245 373 participants) from 2008 to 2021 to examine the effect size of these interventions targeting each cognitive process. Our findings demonstrate that interventions changing effort are more effective than interventions changing intrinsic motivation, and nudge and sludge interventions had similar effect sizes. However, these results need to be interpreted with caution due to a potential publication bias. This new meta-analytic framework provides cognitive principles for organizing nudge and sludge with corresponding behavioural impacts. The insights gained from this framework help inform the design and development of future interventions based on cognitive insights.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.215
GPT teacher head0.482
Teacher spread0.267 · 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 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

Citations21
Published2023
Admission routes2
Has abstractyes

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