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Modeling dopaminergic and other processes involved in learning from reward prediction error: contributions from an individual differences perspective

Pickering, Alan and Pesola, Francesca. 2014. Modeling dopaminergic and other processes involved in learning from reward prediction error: contributions from an individual differences perspective. Frontiers in Human Neuroscience, 8, pp. 1-20. ISSN 1662-5161 [Article]

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Abstract or Description

Phasic firing changes of midbrain dopamine neurons have been widely characterized 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 et al., 2011; Cooper et al., 2014) found that extraverts exhibited larger RPEs than introverts, as reflected in feedback related negativity (FRN) effects in EEG recordings. Using an established, biologically-localized 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 general approach are discussed.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.3389/fnhum.2014.00740

Keywords:

computational modeling of human behavior; dopamine; extraversion; feedback related negativity (FRN); reinforcement learning models; reward prediction error

Departments, Centres and Research Units:

Psychology

Dates:

DateEvent
30 September 2014Published

Item ID:

11415

Date Deposited:

13 Mar 2015 14:46

Last Modified:

28 Jul 2018 20:10

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/11415

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