One-shot learning and behavioral eligibility traces in sequential decision making.
Elife
; 82019 11 11.
Article
em En
| MEDLINE
| ID: mdl-31709980
ABSTRACT
In many daily tasks, we make multiple decisions before reaching a goal. In order to learn such sequences of decisions, a mechanism to link earlier actions to later reward is necessary. Reinforcement learning (RL) theory suggests two classes of algorithms solving this credit assignment problem:
In classic temporal-difference learning, earlier actions receive reward information only after multiple repetitions of the task, whereas models with eligibility traces reinforce entire sequences of actions from a single experience (one-shot). Here, we show one-shot learning of sequences. We developed a novel paradigm to directly observe which actions and states along a multi-step sequence are reinforced after a single reward. By focusing our analysis on those states for which RL with and without eligibility trace make qualitatively distinct predictions, we find direct behavioral (choice probability) and physiological (pupil dilation) signatures of reinforcement learning with eligibility trace across multiple sensory modalities.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Reforço Psicológico
/
Recompensa
/
Pupila
/
Cognição
/
Tomada de Decisões
/
Aprendizagem
/
Memória
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Elife
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Suíça