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Enregistrement W7028130812

The Development of an Object-Recognition Task for Rats and the Evaluation of the Internal Validity of the Novel-Object-Preference Test

2020· dissertation· en· W7028130812 sur OpenAlexfundno aff

Notice bibliographique

RevueSpectrum Research Repository (Concordia University) · 2020
Typedissertation
Langueen
DomaineMedicine
ThématiqueBiomedical and Chemical Research
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésTask (project management)Set (abstract data type)InterchangeabilityReliability (semiconductor)Test (biology)Measure (data warehouse)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Object-recognition—the ability to discriminate the familiarity of previously presented stimuli—is assessed in laboratory rats using the delayed nonmatching-to-sample (DNMS) task and the novel-object-preference (NOP) test. The DNMS task provides a fairly precise measure of a rat’s object-recognition abilities, however, it suffers from certain drawbacks. In particular, rats require extensive training and it cannot be used to assess memory for objects following periods lasting longer than several minutes. For these reasons, most researchers have abandoned it in favour of the NOP test. The NOP test is easy to use, as it relies on measuring a rat’s natural tendency to spend more time investigating a novel object over a familiar one when both are presented in a familiar context. Some concerns have been raised, however, regarding the internal validity of the NOP test. Accordingly, the goal of the present thesis was to develop a new object-recognition task that addresses the known limitations of the existent tests. A secondary goal of the thesis was to evaluate rats’ performance on the new task to that on the NOP test as a means to validate the latter. The first experiment describes rats’ performance on the new task –the modified DNMS (mDNMS) task. Rats required significantly fewer trials to learn the nonmatching rule compared to conventional DNMS tasks, and their scores showed good test re-test reliability. The same rats’ exhibited significant novelty-preference scores on the NOP test, however their scores showed poor test re-test reliability and were not significantly correlated with mDNMS scores. The latter finding suggests that the two tasks may not tax similar underlying cognitive processes. In the experiment presented in Chapter 3, memory for objects was assessed following delays lasting 72 hr, 3 weeks, and ~45 weeks on both the mDNMS task and NOP test. Rats successfully discriminated between novel and sample objects on the mDNMS task following all three delays, however, the same rats failed to exhibit significant novelty preferences following all three delays on the NOP test. These findings reveal that the mDNMS task can be used to assess long-term memory for objects, and that a failure to exhibit a novelty preference may not necessarily reflect the status of object-recognition memory. Next, we assessed rats’ performance on the mDNMS task and NOP test following surgical lesions made to either the hippocampus (HPC) or perirhinal cortex (PRh)—two brain areas implicated in object-recognition memory. Neither HPC nor PRh lesions failed to disrupt performance on the mDNMS task, but rats with PRh lesions failed to display a novelty preference on the NOP test. The discrepancy in the PRh rats’ performance on both tasks further adds to concerns regarding the internal validity of the NOP test, such that a lack of novelty preference is not necessarily indicative of an object-recognition memory impairment. The final experiment focused on refining the mDNMS task to include an additional behavioural measure—latency to make a choice. We incorporated a Go/No-go procedure and found that latency to make a choice provided a more sensitive measure of object-recognition memory than choice-accuracy on the test. Collectively, these findings confirmed the utility of the mDNMS task as a means to gauge object-recognition memory in rats. The results also highlight the limitations of the NOP test, and raise concerns regarding the internal validity of it as a means to measure object-recognition abilities in rats.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,123
Score d'incertitude au seuil0,618

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,111
Tête enseignante GPT0,343
Écart entre enseignants0,232 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
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
Publié2020
Routes d'admission1
Résumé présentoui

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