Cargando…
The NK1 antagonist L-733,060 facilitates sequence learning
BACKGROUND: Although several brain regions and electrophysiological patterns have been related to sequence learning, less attention has been paid to the role that different neuromodulators play. AIMS: Here we sought to investigate the role of substance P (SP) in sequence learning in an operant condi...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10291388/ https://www.ncbi.nlm.nih.gov/pubmed/36988219 http://dx.doi.org/10.1177/02698811231161582 |
Sumario: | BACKGROUND: Although several brain regions and electrophysiological patterns have been related to sequence learning, less attention has been paid to the role that different neuromodulators play. AIMS: Here we sought to investigate the role of substance P (SP) in sequence learning in an operant conditioning preparation, supported by a reinforcement learning model. METHODS: Two experiments were performed to test the effects of an NK1 receptor (at which SP primarily acts) antagonist on learning and performing action sequences. In experiment 1, rats were trained to perform an action sequence until stable performance was achieved, and then, in phase 2, they were switched to perform the reverse sequence. In experiment 2, rats were trained to perform an action sequence, and in phase 2, they continued to do the same sequence. In both experiments in the first 3 days of phase 2, rats were injected with an NK1 receptor antagonist (L-733,060, i.p.) or with vehicle. Additionally, we developed a reinforcement learning model which allowed the in silico replication of our experimental tasks. RESULTS: We found that administering an NK1 receptor antagonist weakened the stable retention of a well-learned sequence, allowing the faster acquisition of a new sequence, without impairing the continued performance of a crystallized sequence. Using our reinforcement learning model, we suggest that SP could be acting through the state value learning rate, modulating the effects of the reward prediction error. CONCLUSIONS: Our results suggest that SP could be involved in the consolidation of a sequence representation through a modulatory effect on the reward prediction error. |
---|