Modelling dopaminergic and other processes involved in learning from reward prediction error: Contributions from an individual differences perspective
Phasic firing changes of midbrain dopamine neurons have been widely characterised as reflecting a reward prediction error (RPE). Major personality traits (e.g. extraversion) have been linked to inter-individual variations in dopaminergic neurotransmission. Consistent with these two claims, recent re...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-09-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00740/full |
| _version_ | 1811208202909384704 |
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| author | Alan David Pickering Francesca ePesola |
| author_facet | Alan David Pickering Francesca ePesola |
| author_sort | Alan David Pickering |
| collection | DOAJ |
| description | Phasic firing changes of midbrain dopamine neurons have been widely characterised as reflecting a reward prediction error (RPE). Major personality traits (e.g. extraversion) have been linked to inter-individual variations in dopaminergic neurotransmission. Consistent with these two claims, recent research (Smillie, Cooper, & Pickering, 2011; Cooper, Duke, Pickering, & Smillie, 2014) found that extraverts exhibited larger RPEs than introverts, as reflected in feedback related negativity (FRN) effects in EEG recordings. Using an established, biologically-localised RPE computational model, we successfully simulated dopaminergic cell firing changes which are thought to modulate the FRN. We introduced simulated individual differences into the model: parameters were systematically varied, with stable values for each simulated individual. We explored whether a model parameter might be responsible for the observed covariance between extraversion and the FRN changes in real data, and argued that a parameter is a plausible source of such covariance if parameter variance, across simulated individuals, correlated almost perfectly with the size of the simulated dopaminergic FRN modulation, and created as much variance as possible in this simulated output. Several model parameters met these criteria, while others did not. In particular, variations in the strength of connections carrying excitatory reward drive inputs to midbrain dopaminergic cells were considered plausible candidates, along with variations in a parameter which scales the effects of dopamine cell firing bursts on synaptic modification in ventral striatum. We suggest possible neurotransmitter mechanisms underpinning these model parameters. Finally, the limitations and possible extensions of our approach are discussed. |
| first_indexed | 2024-04-12T04:18:13Z |
| format | Article |
| id | doaj.art-c681ef8c20d8452383d1786530c70946 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-04-12T04:18:13Z |
| publishDate | 2014-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-c681ef8c20d8452383d1786530c709462022-12-22T03:48:20ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612014-09-01810.3389/fnhum.2014.0074099398Modelling dopaminergic and other processes involved in learning from reward prediction error: Contributions from an individual differences perspectiveAlan David Pickering0Francesca ePesola1Goldsmiths, University of LondonInstitute of Psychiatry, King's College, University of LondonPhasic firing changes of midbrain dopamine neurons have been widely characterised as reflecting a reward prediction error (RPE). Major personality traits (e.g. extraversion) have been linked to inter-individual variations in dopaminergic neurotransmission. Consistent with these two claims, recent research (Smillie, Cooper, & Pickering, 2011; Cooper, Duke, Pickering, & Smillie, 2014) found that extraverts exhibited larger RPEs than introverts, as reflected in feedback related negativity (FRN) effects in EEG recordings. Using an established, biologically-localised RPE computational model, we successfully simulated dopaminergic cell firing changes which are thought to modulate the FRN. We introduced simulated individual differences into the model: parameters were systematically varied, with stable values for each simulated individual. We explored whether a model parameter might be responsible for the observed covariance between extraversion and the FRN changes in real data, and argued that a parameter is a plausible source of such covariance if parameter variance, across simulated individuals, correlated almost perfectly with the size of the simulated dopaminergic FRN modulation, and created as much variance as possible in this simulated output. Several model parameters met these criteria, while others did not. In particular, variations in the strength of connections carrying excitatory reward drive inputs to midbrain dopaminergic cells were considered plausible candidates, along with variations in a parameter which scales the effects of dopamine cell firing bursts on synaptic modification in ventral striatum. We suggest possible neurotransmitter mechanisms underpinning these model parameters. Finally, the limitations and possible extensions of our approach are discussed.http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00740/fullDopaminecomputational modelling of human behaviourExtraversionreward prediction errorReinforcement learning modelsfeedback related negativity (FRN) |
| spellingShingle | Alan David Pickering Francesca ePesola Modelling dopaminergic and other processes involved in learning from reward prediction error: Contributions from an individual differences perspective Frontiers in Human Neuroscience Dopamine computational modelling of human behaviour Extraversion reward prediction error Reinforcement learning models feedback related negativity (FRN) |
| title | Modelling dopaminergic and other processes involved in learning from reward prediction error: Contributions from an individual differences perspective |
| title_full | Modelling dopaminergic and other processes involved in learning from reward prediction error: Contributions from an individual differences perspective |
| title_fullStr | Modelling dopaminergic and other processes involved in learning from reward prediction error: Contributions from an individual differences perspective |
| title_full_unstemmed | Modelling dopaminergic and other processes involved in learning from reward prediction error: Contributions from an individual differences perspective |
| title_short | Modelling dopaminergic and other processes involved in learning from reward prediction error: Contributions from an individual differences perspective |
| title_sort | modelling dopaminergic and other processes involved in learning from reward prediction error contributions from an individual differences perspective |
| topic | Dopamine computational modelling of human behaviour Extraversion reward prediction error Reinforcement learning models feedback related negativity (FRN) |
| url | http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00740/full |
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