Neural systems for choice and valuation with counterfactual learning signals.

The purpose of this experiment was to test a computational model of reinforcement learning with and without fictive prediction error (FPE) signals to investigate how counterfactual consequences contribute to acquired representations of action-specific expected value, and to determine the functional neuroanatomy and neuromodulator systems that are involved. 80 male participants underwent dietary depletion of either tryptophan or tyrosine/phenylalanine to manipulate serotonin (5HT) and dopamine (DA), respectively. They completed 80 rounds (240 trials) of a strategic sequential investment task that required accepting interim losses in order to access a lucrative state and maximize long-term gains, while being scanned. We extended the standard Q-learning model by incorporating both counterfactual gains and losses into separate error signals. The FPE model explained the participants' data significantly better than a model that did not include counterfactual learning signals. Expected value from the FPE model was significantly correlated with BOLD signal change in the ventromedial prefrontal cortex (vmPFC) and posterior orbitofrontal cortex (OFC), whereas expected value from the standard model did not predict changes in neural activity. The depletion procedure revealed significantly different neural responses to expected value in the vmPFC, caudate, and dopaminergic midbrain in the vicinity of the substantia nigra (SN). Differences in neural activity were not evident in the standard Q-learning computational model. These findings demonstrate that FPE signals are an important component of valuation for decision making, and that the neural representation of expected value incorporates cortical and subcortical structures via interactions among serotonergic and dopaminergic modulator systems.

The effect of novelty on reinforcement learning.

Recent research suggests that novelty has an influence on reward-related learning. Here, we showed that novel stimuli presented from a pre-familiarized category can accelerate or decelerate learning of the most rewarding category, depending on the condition. The extent of this influence depended on the individual trait of novelty seeking. Different reinforcement learning models were developed to quantify subjects' choices. We introduced a bias parameter to model explorative behavior toward novel stimuli and characterize individual variation in novelty response. The theoretical framework allowed us to test different assumptions, concerning the motivational value of novelty. The best fitting-model combined all novelty components and had a significant positive correlation with both the experimentally measured novelty bias and the independent novelty-seeking trait. Altogether, we have not only shown that novelty by itself enhances behavioral responses underlying reward processing, but also that novelty has a direct influence on reward-dependent learning processes, consistently with computational predictions.