Naturally occurring affect does not predict hyper-binding across three age groups: Individual difference evidence for hyper-binding not only being shown in older adults
Bibliographic record
Abstract
Hyper-binding is shown when irrelevant information is processed and bound to relevant information and this binding carries over to a subsequent task. For example, when participants perform a 1-back task with line drawings superimposed with irrelevant words, and then are later asked to perform an ostensibly unrelated paired-associate memory task with picture-word pairs, their memory is better for pairs that appeared in the 1-back task than for rearranged pairs or novel pairs. Hyper-binding has been repeatedly observed in older (60+ years) participants but not in younger (<30 years) participants and has been attributed to older individuals’ failure to filter out irrelevant information. However, an individual’s emotions can influence how they attend to and process information, and older individuals have been shown to have a positivity bias in that they report greater positive affect than their younger counterparts. Low arousal positive affect is associated with greater cognitive breadth where individuals are more inclusive and take in more irrelevant information. Therefore, it is possible that differences in positive affect contribute to the differences in hyper-binding observed for older versus younger adults. In a series of four studies, we measure hyper-binding using the above task and examine whether individual differences in hyper-binding can be predicted by individual differences in self-reported state or trait affect. No relationships were found between affect and hyper-binding amongst older adults, university undergraduate students, or middle-aged adults, or when examined across age samples. Surprisingly, significant hyper-binding was observed for all age groups and was not larger for older individuals. The results suggest that hyper-binding does not result from greater positive affect but that it may not be a unique age effect.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".