Age-Related Differences in the Lexical Priming Effect Can Be Predominantly Attributed to Differences in Response Bias
Bibliographic record
Abstract
Abstract Age-related declines in visual perception challenge older adults’ ability to locate and identify objects in noisy (e.g., fog, glare, inadequate lighting) or cluttered visual environments. To compensate, older adults often increase reliance on contextual information, which could be helpful, but may also lead to increased risk for “false seeing” – reporting expected objects based on contextual information rather than those actually presented – a phenomenon that Jacoby et al., (2012) observed in older adults under challenging viewing conditions. However, it remains unaddressed whether such false seeing is driven by older adults’ elevated tendency to rely on context, or by age-related declines in visual perceptual processes. The current study addressed this issue by examining the lexical priming effects in 18 younger and 18 older adults ( M = 21.4 years; M = 76.2 years) in Experiment 1, and 13 younger and 13 older adults ( M = 21.2 years; M = 74.8 years) in Experiment 2 using a two-alternative, forced-choice (2AFC) paradigm, while controlling for age-related perceptual differences. On each trial, participants identified a visually degraded target word preceded by an identical, semantically related, or unrelated prime. We applied Signal Detection Theory to isolate perceptual sensitivity ( d ’) and response bias ( c ). Results revealed that once stimulus parameters were adjusted to eliminate age-differences in performance when the prime was unrelated to the target, age-related differences in target identification in the related priming conditions were predominantly driven by age-related differences in response bias rather than in any residual age-differences in perceptual sensitivity. Public Significance Statement To compensate for age-related losses in visual processes, older adults tend to rely more on the overall context provided by the visual scene to identify individual objects than do younger adults. Using a word priming task, our study found that older adults’ use of supporting context to correctly identify target words masked by visual noise persisted even when overall word identification performance (correctly identifying words independent of whether the context was supporting or misleading) was equated for both age groups. Understanding such age-relate differences in visual perception can inform interventions that would support older adults’ visual processing in daily perceptually challenging environments.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".