Modelling human decision under risk and uncertainty
<p>Humans are unique in their ability to flexibly and rapidly adapt their behaviour and select courses of action that lead to future reward. Several ‘component processes’ must be implemented by the human brain in order to facilitate this behaviour. This thesis examines two such components; (i)...
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| Format: | Thesis |
| Language: | English |
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2011
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| _version_ | 1826315934117134336 |
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| author | Hunt, L |
| author2 | Behrens, T |
| author_facet | Behrens, T Hunt, L |
| author_sort | Hunt, L |
| collection | OXFORD |
| description | <p>Humans are unique in their ability to flexibly and rapidly adapt their behaviour and select courses of action that lead to future reward. Several ‘component processes’ must be implemented by the human brain in order to facilitate this behaviour. This thesis examines two such components; (i) the neural substrates supporting action selection during value- guided choice using magnetoencephalography (MEG), and (ii) learning the value of environmental stimuli and other people’s actions using functional magnetic resonance imaging (fMRI). In both situations, it is helpful to formally model the underlying component process, as this generates predictions of trial-to-trial variability in the signal from a brain region involved in its implementation.</p><p>In the case of value-guided action selection, a biophysically realistic implementation of a drift diffusion model is used. Using this model, it is predicted that there are specific times and frequency bands at which correlates of value are seen. Firstly, there are correlates of the overall value of the two presented options, and secondly the difference in value between the options. Both correlates should be observed in the local field potential, which is closely related to the signal measured using MEG. Importantly, the <em>content</em> of these predictions is quite distinct from the <em>function</em> of the model circuit, which is to transform inputs relating to the value of each option into a categorical decision.</p><p>In the case of social learning, the same reinforcement learning model is used to track both the value of two stimuli that the subject can choose between, and the advice of a confederate who is playing alongside them. As the confederate advice is actually delivered by a computer, it is possible to keep prediction error and learning rate terms for stimuli and advice orthogonal to one another, and so look for neural correlates of both social and non-social learning in the same fMRI data. Correlates of intentional inference are found in a network of brain regions previously implicated in social cognition, notably the dorsomedial prefrontal cortex, the right temporoparietal junction, and the anterior cingulate gyrus.</p> |
| first_indexed | 2024-03-06T19:52:06Z |
| format | Thesis |
| id | oxford-uuid:244ce799-7397-4698-8dac-c8ca5d0b3e28 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:35:08Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | oxford-uuid:244ce799-7397-4698-8dac-c8ca5d0b3e282024-12-01T18:42:30ZModelling human decision under risk and uncertaintyThesishttp://purl.org/coar/resource_type/c_db06uuid:244ce799-7397-4698-8dac-c8ca5d0b3e28Social cognitionBehavioural NeuroscienceLearningComputational NeuroscienceCognitive NeuroscienceExperimental psychologyEnglishOxford University Research Archive - Valet2011Hunt, LBehrens, T<p>Humans are unique in their ability to flexibly and rapidly adapt their behaviour and select courses of action that lead to future reward. Several ‘component processes’ must be implemented by the human brain in order to facilitate this behaviour. This thesis examines two such components; (i) the neural substrates supporting action selection during value- guided choice using magnetoencephalography (MEG), and (ii) learning the value of environmental stimuli and other people’s actions using functional magnetic resonance imaging (fMRI). In both situations, it is helpful to formally model the underlying component process, as this generates predictions of trial-to-trial variability in the signal from a brain region involved in its implementation.</p><p>In the case of value-guided action selection, a biophysically realistic implementation of a drift diffusion model is used. Using this model, it is predicted that there are specific times and frequency bands at which correlates of value are seen. Firstly, there are correlates of the overall value of the two presented options, and secondly the difference in value between the options. Both correlates should be observed in the local field potential, which is closely related to the signal measured using MEG. Importantly, the <em>content</em> of these predictions is quite distinct from the <em>function</em> of the model circuit, which is to transform inputs relating to the value of each option into a categorical decision.</p><p>In the case of social learning, the same reinforcement learning model is used to track both the value of two stimuli that the subject can choose between, and the advice of a confederate who is playing alongside them. As the confederate advice is actually delivered by a computer, it is possible to keep prediction error and learning rate terms for stimuli and advice orthogonal to one another, and so look for neural correlates of both social and non-social learning in the same fMRI data. Correlates of intentional inference are found in a network of brain regions previously implicated in social cognition, notably the dorsomedial prefrontal cortex, the right temporoparietal junction, and the anterior cingulate gyrus.</p> |
| spellingShingle | Social cognition Behavioural Neuroscience Learning Computational Neuroscience Cognitive Neuroscience Experimental psychology Hunt, L Modelling human decision under risk and uncertainty |
| title | Modelling human decision under risk and uncertainty |
| title_full | Modelling human decision under risk and uncertainty |
| title_fullStr | Modelling human decision under risk and uncertainty |
| title_full_unstemmed | Modelling human decision under risk and uncertainty |
| title_short | Modelling human decision under risk and uncertainty |
| title_sort | modelling human decision under risk and uncertainty |
| topic | Social cognition Behavioural Neuroscience Learning Computational Neuroscience Cognitive Neuroscience Experimental psychology |
| work_keys_str_mv | AT huntl modellinghumandecisionunderriskanduncertainty |