Bibliographic record
Abstract
Logic and common sense say that judging two stimuli as "same" is the converse of judging them as "different". Empirically, however, 'Same'-'Different' judgment data are anomalous in two major ways. The fast-'Same' effect violates the expectation that 'Same' reaction time (RT) should be predictable by extrapolating from 'Different' RT. The criterion effect violates the expectation that RTs measured when sameness is defined by a conjunction of matching attributes should predict RTs measured when sameness is defined by a disjunction of matching attributes. The two criteria are symmetrical, yet empirically they differ greatly, disjunctive judgments being by far the slower of the two. This study sought the sources of these two effects. With the aid of a cue, a selective-comparison method deconfounded the contributions of stimulus encoding and comparisons to the two effects. The results were paradoxical. Each additional irrelevant (uncued) letter in a random string incremented RT for conjunctive judgments as much as an additional relevant letter did. Yet irrelevant letters were not compared and relevant letters had to be compared. These results appeared again in a second experiment that used words as stimuli. Contrary to intuition, a distinct comparison mechanism-the heart of relative judgment models-is not necessary in judgments of sameness and difference. It is shown here that encoding can carry out the comparison function without the operation of a separate comparison mechanism. Attention mediates the process by selecting from the set of stimulus alternatives, thereby partitioning the set into the 'Same' and 'Different' subsets. The fast-'Same' and criterion effects result from a structural limitation on what attention can select at any one time. With attention mediating the task, 'Same'-'Different' judgments become, in effect, the outcome of a testing of a hypothesis, bridging the distinction between absolute stimulus identification and relative judgments.
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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 | 0.027 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".