Bibliographic record
Abstract
Scientists have long been fascinated by how learning varies among different taxa such as humans, monkeys and fish. Moreover, in humans, it is well known that some people learn differently from others. These learning differences between individuals can also be seen in other animals. For example, male and female guppies (Poecilia reticulata) differ in their ability to learn self-control. Tyrone Luccon-Xiccato, Giulia Montalbano and Cristiano Berlucci from the University of Ferrara, Italy, were interested in exploring what can cause these individual differences in learning. So, they set out to investigate if learning was different for guppies based on whether they were given food at predictable times and places or if this unpredictability helped them learn in other ways.First, the team separated infant guppies into two different environments for 20 days: a predictable one where the guppies were fed once a day at the same time and in the same place, and an unpredictable one where the guppies were fed at a random time during the day in different places in the aquarium. Afterwards, the researchers tested how well the guppies learned by placing the fish in an aquarium with two chambers connected by a corridor. The fish chose between two different coloured discs, one of which was associated with an appetizing reward. The researchers counted every time the fish chose the right or wrong colour. The team repeated this every day until the fish made fewer than four mistakes for two consecutive days. All the guppies eventually learned to pick the correct colour, but the fish in the predictable environment learned to pick the right colour faster than the fish in the unpredictable environment. This suggested that knowing when your food is coming and where it's going to be enabled the guppies to learn fast, allowing them to make the most of their reliable resources.The team then reversed the colour that gave the guppies a reward to test their flexibility in learning. The test was performed just like the previous one, by counting the number of right and wrong answers every day until the guppies made fewer mistakes. Again, all the guppies eventually learned the reverse colour, but fish from the unpredictable environments decreased the number of mistakes they made faster than the other fish. Interestingly, the guppies raised in an unpredictable environment learned slower at first, but when the researchers changed the colour that was rewarded, these guppies were faster at understanding the new colour now meant a reward.Lastly, the team tested the self-control of the guppies by enticing them with a tube full of delicious, but inaccessible, brine shrimp snacks, and counting how many times the guppies tried to eat these tantalizing treats. Luccon-Xiccato and colleagues discovered that guppies raised in an unpredictable environment also attempted to eat the unattainable brine shrimp fewer times than guppies raised in a predictable environment. If the fish don't know where their food is coming from, self-control and being flexible in learning are advantageous, allowing them to change their behaviour.The researchers stated that food predictability could be one of the many factors causing differences in learning. They also state the importance of more research being done to see whether this adaptability is constant throughout the guppies’ life, or whether it can alter based on changes in the environment. This flexibility in learning based on when and where your food comes from could be one of the reasons why individuals have different learning abilities. For fish, knowing when your food is coming and where you're going to get it from can have major effects on your learning.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".