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Record W4399003348 · doi:10.7910/dvn/rnl8ka

Implicit sense of agency in independent and joint actions

2020· dataset· en· W4399003348 on OpenAlexaff
Michael Jenkins

Bibliographic record

VenueHarvard Dataverse · 2020
Typedataset
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSense (electronics)Agency (philosophy)Joint (building)Computer scienceEpistemologyChemistryEngineeringPhilosophyArchitectural engineering

Abstract

fetched live from OpenAlex

This dataset contains the raw data for the three experiments reported in the manuscript titled "Implicit sense of agency in independent and joint actions". Each file contains the data obtained by a single participant, with rows corresponding to individual trials and columns corresponding to different pieces of information recorded for each trial. In each experiment, the data contains numerical estimates made by participants undergoing versions of an intentional binding task, either alone or jointly with another participant. These estimates reflect the perceived time between the participant's action (e.g., pressing a button) and a consequent auditory tone that was played after a variable delay. Experiment 1A includes data from 100 participants performing a task in which they were paired together and pressed a key on their own or at the same time as the other participant. Experiment 1B includes data from 16 participants performing a similar task in which they simply reported the time between two auditory tones. Experiment 2 includes data from 72 participants performing a task in which they were paired together and engaged in a joint task: one participant moved a mouse to move an on-screen cursor towards a stimulus, and the other participant clicked the same mouse when the cursor reached the stimulus.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.028
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.013

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 head0.056
GPT teacher head0.267
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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

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