Abstract
The impact of dopamine on adaptive behavior in a naturalistic environment is largely unexamined. Experimental work suggests that phasic dopamine is central to reinforcement learning whereas tonic dopamine may modulate performance without altering learning per se; however, this idea has not been developed formally or integrated with computational models of dopamine function. We quantitatively evaluate the role of tonic dopamine in these functions by studying the behavior of hyperdopaminergic DAT knockdown mice in an instrumental task in a seminaturalistic homecage environment. In this "closed economy" paradigm, subjects earn all of their food by pressing either of two levers, but the relative cost for food on each lever shifts frequently. Compared to wild-type mice, hyperdopaminergic mice allocate more lever presses on high-cost levers, thus working harder to earn a given amount of food and maintain their body weight. However, both groups show a similarly quick reaction to shifts in lever cost, suggesting that the hyperdominergic mice are not slower at detecting changes, as with a learning deficit. We fit the lever choice data using reinforcement learning models to assess the distinction between acquisition and expression the models formalize. In these analyses, hyperdopaminergic mice displayed normal learning from recent reward history but diminished capacity to exploit this learning: a reduced coupling between choice and reward history. These data suggest that dopamine modulates the degree to which prior learning biases action selection and consequently alters the expression of learned, motivated behavior.
Original language | English (US) |
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Article number | 170 |
Journal | Frontiers in Behavioral Neuroscience |
Volume | 4 |
Issue number | NOV |
DOIs | |
State | Published - Nov 4 2010 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Neuropsychology and Physiological Psychology
- Cognitive Neuroscience
- Behavioral Neuroscience
Keywords
- Behavioral flexibility
- DAT knock-down
- Dopamine
- Environmental adaptation
- Explore-exploit
- Reinforcement learning