Bibliographic record
Abstract
The `taste of victory' is a phrase many associate with trained athletes preparing for intense competition. Indeed, Ed Chambers and colleagues at the University of Birmingham and Manchester Metropolitan University have started unravelling the physiology behind that phrase. When a cyclist swigs a sports drink prior to a race, we relate the energy boost to the sugary calories ingested. However, there may be more to this effect than the pure metabolic benefits of ingesting carbohydrates, as the benefits are lost if the carbohydrates are infused intravenously. Chambers' team hypothesized that because some taste receptors are connected to the brain region responsible for pleasure and reward, this boost phenomenon may start in the athlete's mouth. They wondered whether carbohydrates stimulate the brain so that it motivates the body to physiologically prepare to perform.Chambers' team tested this idea with trained cyclists exercising for 1 h. To determine whether sweetness or perceived calories influenced exercise,athletes rinsed their mouths prior to exercise with a `drink' containing artificial sweetener alone, artificial sweetener and sweet carbohydrate, or artificial sweetener and non-sweet carbohydrate. All three solutions tasted the same so that the athletes were unable to identify whether their mouth-rinse had included calories, which may have influenced their performance.The results revealed that the athletes completed a set amount of work 2%faster having tasted the sweetened and unsweetened carbohydrate solutions compared with athletes that had tasted the carbohydrate-free, artificially sweetened solution. Athletes also exercised with 3% more power after tasting the sweet carbohydrate solution. This suggested that performance enhancement may be independent of `sweetness' and more closely related to a drink's perceived caloric value.Next, Chambers and colleagues decided to use functional magnetic resonance imaging (fMRI), which detects brain activity via changes in blood flow, to determine how the athletes' brains responded to the different drinks. The sweet carbohydrate solution activated regions of the brain associated with emotions and eating behaviours, which are suppressed when a person is not hungry. This is a typical response to food, and not thought uniquely related to exercise. Non-sweet carbohydrates activated the same region of the brain but additionally activated regions thought to perceive energy and those associated with motivation and reward.Chambers' team believes that carbohydrates stimulate non-sweet receptors that activate the brain to enhance performance in similar ways that other stimulants, like caffeine, do. The response tempers messages coming from the body that accompany intense exercise like muscle fatigue, joint pain and increased temperature that could inhibit performance. When stimulated, these new taste receptors tell the brain, `you can do it!' Chambers' research,therefore, suggests that the brain could be dictating performance before the muscles, heart or lungs. So, if this `brain-before-body' phenomenon is influencing human performance, imagine what we could be missing when studying other high performance animals! Do other animals even need this motivational input like we do? If so, Chambers' idea could really change the way we assess exercise activities like migration, predator evasion and prey capture in other organisms; the potential for pre-performance brain input could be huge!Meanwhile humans, if you're getting ready for that big race, just remember to treat your tongues first.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".