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Enregistrement W4386245997 · doi:10.1242/jeb.245024

Guppies learn differently when their food is unpredictable

2023· article· en· W4386245997 sur OpenAlexaff
Andrea Murillo

Notice bibliographique

RevueJournal of Experimental Biology · 2023
Typearticle
Langueen
DomaineNeuroscience
ThématiqueMemory and Neural Mechanisms
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésPoeciliaFish <Actinopterygii>GuppyPoeciliidaeBiologySet (abstract data type)ZoologyEcologyPsychologyFisheryComputer science

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,012

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,129
Tête enseignante GPT0,336
Écart entre enseignants0,207 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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