The influence of the generation of detail on accurate and inaccurate remembering
Bibliographic record
Abstract
Traditionally, recognition judgments have been thought of as arising from two fundamentally different processes; namely, familiarity and recollection (Jacoby,199l;Mandler, 1980).According to the dual-process theory (Jacoby, l99I), the differences in these two influences on recognition judgments lies in their degree of automaticity and the extent to which they rely on a heuristic attribution about the source of current processing.Specifically, familiarity is thought to be based on the attribution that the automatic perceptual processing of a stimulus originates from prior exposure, whereas recollection is often seen as resulting from the direct and consciously controlled retrieval of details associated with prior exposure to a stimulus (Jacoby & Dallas, 1981).As a result, familiarity is conceptualizeð as being a remembering process that is error-prone and inferential, whereas recollection is conceptualized as being a relatively infallible basis of making remembering judgments.The main objective of this thesis is to critically examine the commonly held perspective that recollection is a less error-prone basis for making remernbering judgments than a reliance on familiarity.Across three experiments, I examined the factors that influence how people use the recollection of detail in forming recognition memory judgments.Together, the results from the three studies provide evidence that the generation of detail, or recollection, is not an infallible basis to form remembering judgments; instead, in some situations, recollection was found to be based on an error-prone inferential process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".