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Record W4292636101 · doi:10.31234/osf.io/bwh2u

When familiarity not novelty motivates information-seeking behaviour

2022· preprint· en· W4292636101 on OpenAlexafffund
Gregory Brooks, H. G. Whitehead

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicPsychological and Educational Research Studies
Canadian institutionsUniversity of TorontoWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNoveltyRecallPreferencePsychologyMetacognitionInformation seekingNovelty seekingCognitive psychologyTask (project management)Social psychologyCognitionComputer scienceInformation retrieval

Abstract

fetched live from OpenAlex

Research has established that novelty motivates information-seeking behaviour in many situations. While novelty preferences have been well-studied, an understanding of conditions under which familiarity trumps novelty remains limited. Recent work has revealed that when a metacognitive experience indicates that unsuccessfully recalled information may still be available, a subsequent tendency to seek out unrecalled familiar information can emerge. We conducted three experiments to identify critical factors that determine when familiarity preferences can be observed. Experiment 1 demonstrated the critical role of a recent unsuccessful recall attempt in inducing such a preference. Experiment 2 revealed that the impact of recall attempts is not limited to situations that follow unsuccessful recall, as a familiarity preference was observed even when information was successfully generated. Experiment 3 showed that the level of confidence in the accuracy of recalled information is a key factor, with moderate levels of confidence leading to the strongest subsequent familiarity preference. Together, our results suggest that novelty preferences in information-seeking are not ubiquitous, as specific task demands including attempted memory retrieval, as well as metacognitive retrieval experiences, can induce familiarity preferences. Our findings can be interpreted within theoretical frameworks that emphasise the role of knowledge gaps as driving factors of information-seeking.

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)0.001
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.591
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0480.001

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 head0.125
GPT teacher head0.411
Teacher spread0.287 · 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 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

Citations0
Published2022
Admission routes2
Has abstractyes

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