Age, Personal Characteristics, and the Speed of Psychological Time
Bibliographic record
Abstract
Abstract Adults often report the impression that time seems to pass more and more quickly as they get older. The purpose of this study is to identify how individual characteristics relate to this impression of acceleration. To do so, 894 participants aged 15 to 97 completed a questionnaire that surveyed sociodemographic characteristics, impulsivity, anxiety, personality, and relation to time. They also indicated how fast different lapses of time seemed to have passed: yesterday, the past week, the past month, the past year, the past three years, the past five years, and the past 10 years. For each period, except for one year, adolescents found that time passes more slowly than participants from older groups (18–29 years, 30–59 years, and 60 years and over). A composite score for all these periods also indicates that female participants found that time passes more rapidly than males. However, a multiple linear regression analysis reveals that the variables that best predict the impression that time passes faster as we get older are high anxiety, the belief in the phenomenon of temporal compression, as well as conscientiousness and agreeableness personality traits, with other factors explaining little variance. These results add further weight to the impression that time seems to pass more quickly as we age, but also indicate that other variables than age play a critical role in explaining this impression.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".