MétaCan
Menu
Back to cohort
Record W4283395869 · doi:10.31234/osf.io/4pyqn

Assessing the replicability of Cognitive Psychology via remote experiential learning

2022· preprint· en· W4283395869 on OpenAlexafffund
Ben Dyson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsToronto Metropolitan UniversityUniversity of Alberta
FundersDirectorate for Biological SciencesUniversity of SussexUniversity of Alberta
KeywordsExperiential learningReplication (statistics)Context (archaeology)Object (grammar)Asynchronous communicationCognitionPsychologyPerceptionComputer scienceCognitive psychologyCognitive scienceMultimediaApplied psychologyMathematics educationArtificial intelligence

Abstract

fetched live from OpenAlex

A recent global health crisis demanded the wholesale configuration of both teaching and research from in-person to on-line formats. This presented an opportunity to conduct an environmental sweep on the replicability of Cognitive Psychology in the context of an undergraduate course, in which portable experimental packages were written for mobile phone (Flex Labs). Students received direct experience with studies that had central implications for the discussion of Cognitive Neuroscience (Faces), Perception (Search), Attention (Doodle), Everyday Memory (House), Long-Term Memory (Object), Semantic Memory (Trivia), Decision-Making (RPS), and, Mental Imagery (Rotate). Running across Winter 2021, Fall 2021 and Winter 2022 (average n per study = 585), with the expectation of one study, data consistently produced evidence either for (Faces, Search, Object, RPS, Rotate) or against (Doodle, Trivia) the original findings. The Flex Lab scheme not only allows students to play an active role in the discussion of the replication crises within empirical science, but also provides a framework for the future implementation of experiential learning during remote and asynchronous teaching. With continued evaluation made possible via Open Science Framework (https://osf.io/kv5qp/), a central question for all future research is whether on-line data collection violates an essential auxiliary assumption for the replication of in-person data collection.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.732
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0300.000

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 head0.068
GPT teacher head0.458
Teacher spread0.389 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same topicIdentity, Memory, and TherapyFrench-language works237,207