When familiarity not novelty motivates information-seeking behaviour
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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 any 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 situational demands including recent attempted memory retrieval, as well as metacognitive retrieval experiences, can induce familiarity preferences. Our findings can be interpreted within theoretical frameworks that emphasize 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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it