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, 28 Ciclo. DOI 10.6092/unibo/amsdottorato/7596.
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Abstract
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.
Abstract
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.
Tipologia del documento
Tesi di dottorato
Autore
Garofalo, Sara
Supervisore
Dottorato di ricerca
Scuola di dottorato
Scienze umanistiche
Ciclo
28
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
neuroscience, reinforcement learning, EEG, fMRI, SCR
URN:NBN
DOI
10.6092/unibo/amsdottorato/7596
Data di discussione
17 Maggio 2016
URI
Altri metadati
Tipologia del documento
Tesi di dottorato
Autore
Garofalo, Sara
Supervisore
Dottorato di ricerca
Scuola di dottorato
Scienze umanistiche
Ciclo
28
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
neuroscience, reinforcement learning, EEG, fMRI, SCR
URN:NBN
DOI
10.6092/unibo/amsdottorato/7596
Data di discussione
17 Maggio 2016
URI
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