The value of what’s to come: neural mechanisms coupling prediction error and reward anticipation
Iigaya, K.; Hauser, T. U.; Kurth-Nelson, Z.; O'Doherty, J. P.; Dayan, P.; Dolan, R. J.
Show abstract
Having something to look forward to is a keystone of well-being. Anticipation of a future reward, like an upcoming vacation, can often be more gratifying than the very experience itself. Theories of anticipation have described how it induces behaviors ranging from beneficial information-seeking through to harmful addiction. However, it remains unclear how neural systems compute an attractive value from anticipation, instead of from the reward itself. To address this gap, we administered a decision-making task to human participants that allowed us to analyze brain activity during receipt of information predictive of future pleasant outcomes. Using a computational model of anticipatory value that captures participants decisions, we show that an anticipatory value signal is orchestrated by influences from three brain regions. Ventromedial prefrontal cortex (vmPFC) tracks the value of anticipation; dopaminergic midbrain responds to information that enhances anticipation, while sustained hippocampal activity provides a functional coupling between these regions. This coordinating function of the hippocampus is consistent with its known role in episodic future thinking. Our findings shed new light on the neural underpinnings of anticipations influence over decision-making, while also unifying a range of phenomena associated with risk and time-delay preference.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Tonic dopamine and biases in value learning linked through a biologically inspired reinforcement learning model 96%
- The ascending arousal system shapes low-dimensional brain dynamics to mediate awareness of changes in intrinsic cognitive states 96%
- Value and choice as separable, stable representations in orbitofrontal cortex 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.