Using preexperimental familiarity to compare the ICE and cue-overload accounts of context-dependent memory in item recognition.
Bibliographic record
Abstract
In recognition, context effects often manifest as higher hit and false-alarm rates to probes tested in an old context compared with probes tested in a new context; sometimes, this concordant effect is accompanied by a discrimination advantage. According to the cue-overload account of context effects (Rutherford, 2004), context acts like any other cue, and thus context effects should be larger with lighter context loads. Conversely, the Item, Associated Context, and Ensemble (ICE) account (Murnane et al., 1999) attributes context effects to two factors: subjects erroneously attributing context familiarity to the probe, and the formation of ensembles (mnemonic combinations of item and context). Context familiarity increases as exposure at study increases, and thus ICE predicts larger effects of context as context load increases. Relatedly, ICE predicts larger effects of context as context meaningfulness increases, as meaningful contexts are more likely to be bound to the target in an ensemble. In Experiments 1 and 2, rather than manipulate context load during the study phase, we relied on subjects' preexperimental context exposure to manipulate context load. Subjects studied words superimposed on photographs of their university campus or another university campus. At test, targets and distractors were evenly divided between study and novel contexts and between familiar and unfamiliar contexts. In Experiment 3, we manipulated context familiarity within the experimental session. Results supported ICE, suggesting that context does not act as a retrieval cue in recognition. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".