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Record W4284677968 · doi:10.31234/osf.io/8zk32

Not so fast: Individual differences in impulsiveness are only a modest predictor of cognitive reflection

2022· preprint· en· W4284677968 on OpenAlexaff
Shane Littrell, Jonathan A. Fugelsang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPsychologyCognitionCognitive styleAmbiguityRelation (database)Bivariate analysisStyle (visual arts)Cognitive psychologyReflection (computer programming)Developmental psychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

The extent to which a person engages in reflective thinking while problem-solving is often measured using the Cognitive Reflection Test (CRT; Frederick, 2005). Some past research has attributed poorer performance on the CRT to impulsiveness, which is consistent with the close conceptual relation between Type I processing and dispositional impulsiveness (and the putative relation between a tendency to engage in Type I processing and poor performance on the CRT). However, existing research has been mixed on whether such a relation exists. To address this ambiguity, we report two large sample size studies examining the relation between impulsiveness and CRT performance. Unlike previous studies, we use a number of different measures of impulsiveness, as well as measures of cognitive ability and analytic thinking style. Overall, impulsiveness is clearly related to CRT performance at the bivariate level. However, once cognitive ability and analytic thinking style are controlled, these relations become small and, in some cases, non-significant. Thus, dispositional impulsiveness, in and of itself, is not a strong predictor of CRT performance.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.113
GPT teacher head0.384
Teacher spread0.271 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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