Categories and Range Effects in Human Spatial Memory
Bibliographic record
Abstract
After learning a particular target stimulus, such as a location, humans' judgments of whether a particular stimulus is the target or not is affected by the range of stimuli presented on tests. In such frequently found range effects, the peak of "yes" responses shifts toward the middle of the range of tested stimuli. Humans also code both the metric value and categorical information regarding a target stimulus, and use both forms of codes, such that responses are biased toward the category middle (category adjustment model, Duffy et al., 2010). Categorical codes should also affect range effects, with a test range crossing category boundaries producing less range effect than a test range within a category. We examined a set of past results presented in a review of range effects in humans (Thomas, 1993) for functional explanations in light of categorical coding, and found that all results could be reasonably explained. Additional experiments comparing range effects across vs. within a category found limited supporting evidence, perhaps because the range effects were weak. The adaptive functions of using (in part) categorical coding accounts for many seemingly peculiar biases in human cognition.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".