Cognition of dairy cattle: Implications for animal welfare and dairy science
Bibliographic record
Abstract
The study of dairy cattle cognition has gained increasing attention over the past several decades, offering insights into the relationship between cognition and animal welfare. The objectives of this narrative review are to summarize a selection of studies exploring different cognitive processes in dairy cattle, discuss how these processes relate to common management practices and animal welfare, and identify knowledge gaps to guide future research. We begin with a brief overview of research into how dairy cattle perceive and sense the world around them, followed by a description of different types of learning and memory studied in dairy cattle, including nonassociative and associative learning, as well as short- and long-term memory. We then discuss how researchers have explored cognitive processes in dairy cows to understand their social lives, their ability to cope with challenges, and how they feel under different management conditions. Continued research into dairy cattle cognition is encouraged, including both foundational studies asking questions about the cognitive abilities of dairy cattle, as well as applied questions that can lead to improvements to their housing and management. We end by offering several avenues of future research into the cognition of dairy cattle, including a better understanding of competence and resilience, factors that influence cognition such as sleep and individual differences, as well as other under-investigated topics, such as problem-solving and metacognition.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".