Adaptive and Maladaptive Implications of Reinforcement Learning Processes: Fronto-Striatal Loops and Behavioural Correlates


Autoria(s): Garofalo, Sara <1986>
Data(s)

17/05/2016

Resumo

That humans and animals learn from interaction with the environment is a foundational idea underlying nearly all theories of learning and intelligence. Learning that certain outcomes are associated with specific actions or stimuli (both internal and external), is at the very core of the capacity to adapt behaviour to environmental changes. In the present work, appetitive and aversive reinforcement learning paradigms have been used to investigate the fronto-striatal loops and behavioural correlates of adaptive and maladaptive reinforcement learning processes, aiming to a deeper understanding of how cortical and subcortical substrates interacts between them and with other brain systems to support learning. By combining a large variety of neuroscientific approaches, including behavioral and psychophysiological methods, EEG and neuroimaging techniques, these studies aim at clarifying and advancing the knowledge of the neural bases and computational mechanisms of reinforcement learning, both in normal and neurologically impaired population.

Formato

application/pdf

Identificador

http://amsdottorato.unibo.it/7596/1/Garofalo_Sara_tesi.pdf

urn:nbn:it:unibo-18577

Garofalo, Sara (2016) Adaptive and Maladaptive Implications of Reinforcement Learning Processes: Fronto-Striatal Loops and Behavioural Correlates, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in International phd program in cognitive neuroscience <http://amsdottorato.unibo.it/view/dottorati/DOT517/>, 28 Ciclo. DOI 10.6092/unibo/amsdottorato/7596.

Relação

http://amsdottorato.unibo.it/7596/

Palavras-Chave #M-PSI/02 Psicobiologia e psicologia fisiologica
Tipo

Doctoral Thesis

PeerReviewed

Contribuinte(s)

Di Pellegrino, Giuseppe

Idioma(s)

en

Publicador

Alma Mater Studiorum - Università di Bologna

Direitos

info:eu-repo/semantics/openAccess