Diving into a pool or volcano? Examining the influence of sentence context and task demands on sentence reading in younger and older adults
Bibliographic record
Abstract
Cognitive ageing is often associated with slower lexical processing, which might influence both language production and comprehension. Words are typically used in context, which can further influence word processing and potential age differences. However, it remains unclear how older adults are affected by context during reading. Older adults are reported to have in-tact semantic knowledge, which could potentially help them to process words predicted by semantic information in the preceding context. However, potential difficulties with semantic control might mean older adults have greater difficulty suppressing interfering information from mismatching contexts. In this study we examined the influence of contexts that either predicted a specific target word ("matched", e.g., "The man watched the lava erupt from the volcano") or predicted another word than the target ("mismatched", e.g., "The swimmer dived into the volcano") as compared to neutral contexts (e.g., "They went to see the volcano"). We also examined the potential role of task demands by asking participants to either just read the sentences for comprehension or to answer questions. Forty younger adults (18-35 years old) and forty older adults (65-80 years old) completed a self-paced reading task in which we measured reading times for the target words. Older adults showed slower reading times overall. Matched sentence contexts facilitated reading times in both age groups. Surprisingly, mismatched sentence contexts did not hinder reading times in either age group. Furthermore, reading times were not influenced by task demands. Together, this shows the importance of studying language in context. While interference from mismatching sentence contexts might have not been substantial enough to delay reading, reading was faster when processing expected words. This suggests older adults can indeed benefit from semantic knowledge to facilitate word processing during comprehension. This occurred even when no additional task was presented and people were purely reading for comprehension.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".