Dual Processes in Recognition Memory: The Opposing Influences of Processing-Ease on Recognition Memory Decisions
Bibliographic record
Abstract
A key finding in recognition memory experiments is that difficult to process stimuli are often remembered better than easy to process stimuli. In the present study, processing difficulty was manipulated by presenting participants with interleaved word pairs which were either congruent (perceptually fluent) or incongruent (perceptually disfluent). In a reanalysis of datasets from several prior studies, we found that recognition sensitivity (d’) was greater for incongruent items. However, this benefit in d’ for incongruent items was not reflected in the hit rates for responses in the two slowest response time quartiles; here we found equivalent hit rates for congruent and incongruent items. We propose that a dual process account can explain this pattern of equivalent hit rates. While there is one process at study which leads to better memory for incongruent items, there is another process at test that affects bias rather than sensitivity. Specifically, items at test which were perceptually fluent lead to an illusion of memory, where participants mistook the ease of processing these items with prior experience. In a following empirical study, by manipulating processing fluency at study separately from processing fluency at test, we investigated the contribution of each of these processes to recognition memory decisions. The results offered strong evidence for this dual process account.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 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".