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Record W3105916523 · doi:10.1002/bdm.2214

Resistance to cognitive biases: Longitudinal trajectories and associations with cognitive abilities and academic achievement across development

2020· article· en· W3105916523 on OpenAlexafffund
Maggie E. Toplak, David B. Flora

Bibliographic record

VenueJournal of Behavioral Decision Making · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyCognitionSophisticationDevelopmental psychologyCognitive psychologyCognitive biasCognitive development

Abstract

fetched live from OpenAlex

Abstract Cognitive failures on several reasoning and judgment tasks can be explained by miserly information processing tendencies. These tasks have been examined in child and youth samples, and we extend this work by examining the developmental trajectory of performance on these cognitive bias tasks and their association with other markers of cognitive sophistication. A longitudinal design was used to examine the development of resistance to cognitive biases in a sample of 204 typically developing children and youth. These youth were 8–14 years of age at first assessment and were assessed at three measurement occasions separated by 3 years. Resistance to cognitive biases as represented by performance on five reasoning and judgment tasks, including ratio bias, belief‐bias syllogisms, attribute framing problems, base‐rate sensitivity, and temporal discounting. The developmental trajectory of resistance to cognitive biases was examined. We also estimated associations between trajectories of resistance to cognitive biases and measures of cognitive abilities, actively open‐minded thinking, and superstitious thinking to examine how individual differences in other measures of cognitive sophistication were associated with the development of resistance to cognitive biases. Cognitive ability measures included intelligence (verbal and nonverbal) and executive function tasks (interference control and set‐shifting). Growth modeling results showed that resistance to cognitive biases increased linearly from 8 to 15 years of age, followed by a flat mean trajectory up to age 20. Cognitive ability, actively open‐minded thinking, and superstitious thinking predicted individual differences in resistance to cognitive biases, but not changes in resistance to cognitive biases. Performance on resistance to cognitive biases tasks was positively correlated with self‐ and parent‐reported academic achievement.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.252
GPT teacher head0.462
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations33
Published2020
Admission routes2
Has abstractyes

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