Biomedical subjects

Nathaniel D Daw

Publications and source records attributed to Nathaniel D Daw.

3 recordsLinked to original sources

PubMed2003

A computational substrate for incentive salience.

Theories of dopamine function are at a crossroads. Computational models derived from single-unit recordings capture changes in dopaminergic neuron firing rate as a prediction error signal. These models employ the prediction error signal in two roles: learning to predict future rewarding events and biasing action choice. Conversely, pharmacological inhibition or lesion of dopaminergic neuron function diminishes the ability of an animal to motivate behaviors directed at acquiring rewards. These lesion experiments have raised the possibility that dopamine release encodes a measure of the incentive value of a contemplated behavioral act. The most complete psychological idea that captures this notion frames the dopamine signal as carrying 'incentive salience'. On the surface, these two competing accounts of dopamine function seem incommensurate. To the contrary, we demonstrate that both of these functions can be captured in a single computational model of the involvement of dopamine in reward prediction for the purpose of reward seeking.

Algorithms↗

PubMed2002

Long-term reward prediction in TD models of the dopamine system.

This article addresses the relationship between long-term reward predictions and slow-timescale neural activity in temporal difference (TD) models of the dopamine system. Such models attempt to explain how the activity of dopamine (DA) neurons relates to errors in the prediction of future rewards. Previous models have been mostly restricted to short-term predictions of rewards expected during a single, somewhat artificially defined trial. Also, the models focused exclusively on the phasic pause-and-burst activity of primate DA neurons; the neurons' slower, tonic background activity was assumed to be constant. This has led to difficulty in explaining the results of neurochemical experiments that measure indications of DA release on a slow timescale, results that seem at first glance inconsistent with a reward prediction model. In this article, we investigate a TD model of DA activity modified so as to enable it to make longer-term predictions about rewards expected far in the future. We show that these predictions manifest themselves as slow changes in the baseline error signal, which we associate with tonic DA activity. Using this model, we make new predictions about the behavior of the DA system in a number of experimental situations. Some of these predictions suggest new computational explanations for previously puzzling data, such as indications from microdialysis studies of elevated DA activity triggered by aversive events.

Animals↗

PubMedDate not supplied

Opponent interactions between serotonin and dopamine.

Anatomical and pharmacological evidence suggests that the dorsal raphe serotonin system and the ventral tegmental and substantia nigra dopamine system may act as mutual opponents. In the light of the temporal difference model of the involvement of the dopamine system in reward learning, we consider three aspects of motivational opponency involving dopamine and serotonin. We suggest that a tonic serotonergic signal reports the long-run average reward rate as part of an average-case reinforcement learning model; that a tonic dopaminergic signal reports the long-run average punishment rate in a similar context; and finally speculate that a phasic serotonin signal might report an ongoing prediction error for future punishment.

Animals↗