Pet ownership is associated with greater cognitive and brain health in a cross-sectional sample across the adult lifespan
Bibliographic record
Abstract
Human-animal interactions that stem from pet ownership have a wide range of benefits for social, emotional, and physical health. These factors also tend to improve cognition. Following this logic, owning a pet could indirectly enhance cognitive and brain health through mechanisms like improvements in well-being, socialization, and decreased stress. In the present study, cross-sectional data were drawn from the Alabama Brain Study on Risk for Dementia in which 95 participants aged 20-74 were recruited. Specifically, 56 adults were pet-owners and 39 adults were not pet-owners. Multivariate analyses revealed that pet ownership was related to higher levels of cognition and larger brain structures, and these effects were largest in dog owners. The most consistent cognitive relationships were found with better processing speed, attentional orienting, and episodic memory for stories, and with dorsal attention, limbic, and default mode networks. Moreover, we show that owning a pet can reduce one's brain age by up to 15 years. Pet ownership was not related to indirect factors including social, emotional, and physical health. We found also that older adults' brain health benefited from owning more than one pet versus owning one or fewer pets. These findings indicate that pet ownership, especially dog ownership, may play a role in enhancing cognitive performance across the adult lifespan, which could in turn influence protection against age-related cognitive decline.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".