How humans and animals make decisions and decisions for rewards have been a subject of intense focus recently. These questions are compelling both for their critical relevance to real-world concerns, from daily purchases to economic policy, and their relationship to neurotransmitter systems underlying diseases from Parkinson's to schizophrenia. Much contemporary study of decisions has been spurred by the development of computational models that make specific predictions about how decisions arise. These models, based on the theories of reinforcement learning, have provided an invaluable tool for teasing apart the cognitive and physiological mechanisms of decision-making. However these models have to date only been applied to habitual decisions - those decisions that result from learning to expect a particular outcome from a particular action. Real-world decision- making also encompasses another class of decisions, which involve planning in spite of any experience with the outcome of your potential actions. When making non-habitual decisions, individuals may use information that they originally learned without any reward. For instance, we choose to sample new dishes or eat at entirely new restaurants even though we may have never before entered them. Modeling this sort of behavior has proven extremely difficult, due in part to the wide variety of information that may be brought to bear on such decisions. Recently, we have developed a reduced, constrained experimental learning task that allows us to separately measure both learned habits and non-habitual learning, simultaneously, in humans. We have modeled this second form of learning, and, using functional magnetic resonance imaging (fMRI), identified neural structures that represent the learned information. These include the hippocampus, a structure critical for normal memory, and whose dysfunction is implicated in several major mental health disorders, such as major depression and schizophrenia. The place of the hippocampus in decisions for reward is, however, unclear. This proposal builds on our previous results to identify how this information is used to make decisions, by asking participants to apply this information to making money. Specifically, we examine brain systems known to participate in decision-making, and ask what methods they use to parse through the information now available to them. We have reason to believe that these systems employ strategies to reduce the amount of information they need to work with, and that hippocampus is uniquely capable of implementing these strategies. Understanding these strategies is essential to understanding how decisions are made in the real world, and will provide valuable and novel insight into the fundamental mechanisms of hippocampal function.
I aim to elucidate the mechanisms by which hippocampus interacts with striatal and cortical decision structures to effect goal-directed planning behavior. This work is relevant to public health as functional and structural hippocampal deficits are strongly associated with numerous severe mental health disorders. In particular, several of these disorders - for example, schizophrenia and major depression - exhibit core symptoms which reflect dysfunction of exactly the sorts of associative learning mechanisms proposed to underlie goal-directed decisions. An understanding of these mechanisms will thus provide crucial insights into the nature and extent of such disruptions and inform increasingly sophisticated and targeted development of behavioral and physiological therapies.
| Bornstein, Aaron M; Khaw, Mel W; Shohamy, Daphna et al. (2017) Reminders of past choices bias decisions for reward in humans. Nat Commun 8:15958 |
| Wallisch, Pascal; Bornstein, Aaron M (2013) Enhanced motion perception as a psychophysical marker for autism? J Neurosci 33:14631-2 |
| Bornstein, Aaron M; Daw, Nathaniel D (2013) Cortical and hippocampal correlates of deliberation during model-based decisions for rewards in humans. PLoS Comput Biol 9:e1003387 |
| Bornstein, Aaron M; Daw, Nathaniel D (2012) Dissociating hippocampal and striatal contributions to sequential prediction learning. Eur J Neurosci 35:1011-23 |
