The influence of strain on demand functions for water in rats (Rattus Norvegicus)
Notice bibliographique
Résumé
Demand functions generated by operant conditioning techniques may be used to assess animal priorities (Dawkins 1990, Matthews & Ladewig 1994, Sherwin & Nicol 1997, Fraser & Matthews 1997, Matthews 1998) . The animal needs to perform a number of simple responses (e.g. bar-presses), to obtain one unit of a reinforcer (Lea 1978, Dawkins 1990, Matthews & Ladewig 1994). This reinforcer enables the animal to perform a certain behaviour. The relationship between the workload (traditionally set by a fixed ratio (FR) reinforcement schedule) and the amount obtained of the reinforcer is described by a curve with FR-value on the horizontal axis as the independent variable and the amount consumed as the dependent variable on the vertical axis (Hursh 1980) . The resulting slope of the demand function will then provide a measure for the demand of the reinforcer. If the animal is highly motivated to obtain the reinforcer the animal will work at an increasing rate as the workload increases and thus keep its intake close to constant. The slope of the demand function will be close to zero, which indicates a high demand for the reinforcer. The steeper the demand function, the less important the reinforcer (Lea 1978, Hursh 1984). The slope of the demand function is nearly always negative and it is not influenced by e.g. the weight of the animal (Hursh 1984, Ladewig 1997). The size of an animal will influence the intercept of the demand function, but not the slope of the demand function. Using this method it should be possible to measure quantitatively to which extent an animal is motivated to obtain a given reinforcer and to compare demands for different reinforcers among different animals. It is important to note that the demand function reflects the demand for the reinforcer. The demand depends on both the internal preferences of the animal but also on the decisions made by the animal of how much to obtain of the reinforcer considering the workload imposed on the animal and the availability of alternatives. Many factors influence the slope of the demand curve; some are varied by the experimenter (e.g. FR-values and test-time), some factors might depend on the animal’s gender and genetics (e.g. sensory capacities and basic level of anxiety), and some are physiological factors influencing the motivational state (e.g. thirst, hunger, aggression, or phase in oestrous cycle). Furthermore, it is well known that, at least in rats, differences exist in performance in cognitive and operant tasks between different rat strains (Andrews et al. 1995). The purpose of this experiment was to determine if the method was able to detect differences in demand for water between two different strains. For the selection of the two strains of rats, three criteria were used. First, we wanted inbred strains in order to minimize the variation between rats in the two test groups. Second, in order to avoid any confounding influence of rats having difficulties in performing the operant response-task, we needed two strains, which were known to perform well in operant systems. Third, the strains should neither be transgenic nor spontaneous animal models of human disorders (Svendsen & Hau 1994). The two strains chosen were pigmented inbred Long Evans rats (LE/Mol) and albino inbred Wistar-Kyoto rats (WKY/Mol).
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».