Deliberation entails the serial examination and evaluation of outcomes. While there are detailed, mechanistic, and quantitative theories of non-deliberative decision-making, such theories of deliberative decision-making are still lacking. This is due to a lack of key experimental knowledge of the mechanisms of deliberation. Deliberative decision-making entails the sequential consideration of possibilities, requiring three steps in a repeated cycle: (1) prediction of the consequences of one's actions, (2) evaluation of those predicted consequences, and (3) selection of the best action. An important difficulty that has limited our ability to study deliberative decision-making is that the process of deliberation is covert, that is, the transient information being considered is not reflected in immediate behavior. However, new mathematical techniques now allow decoding of represented variables from neural ensembles at very fast timescales, enabling the observation of those transient, covert processes. The goal of this proposal is to track the covert prediction of reward outcomes as alternatives are evaluated. We have preliminary data that structures known to be involved in motivation and evaluation (ventral striatum, orbitofrontal cortex) show a transient activation of reward-related activity at certain deliberative decision-points. Combining newly available multi-structure recording techniques, newly developed tasks, newly improved neural ensemble analysis techniques, and computational modeling, we will examine the relationship between the representations of future possibilities in hippocampus and the covert reappearance of reward-related information in structures known to be involved in motivation and reward and decision-making.
The disease model of addiction suggests that addiction is fundamentally a dysfunction of decision-making. As our understanding of decision-making has improved, our understanding of how it can break down has also improved. The goal of this proposal is to improve our understanding of deliberative decision-making which will enable a better understanding of the potential dysfunction that can occur therein.
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|Sweis, Brian M; Abram, Samantha V; Schmidt, Brandy J et al. (2018) Sensitivity to ""sunk costs"" in mice, rats, and humans. Science 361:178-181|
|Sweis, Brian M; Larson, Erin B; Redish, A David et al. (2018) Altering gain of the infralimbic-to-accumbens shell circuit alters economically dissociable decision-making algorithms. Proc Natl Acad Sci U S A 115:E6347-E6355|
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|Redish, A David; Schultheiss, Nathan W; Carter, Evan C (2016) The Computational Complexity of Valuation and Motivational Forces in Decision-Making Processes. Curr Top Behav Neurosci 27:313-33|
|Stott, Jeffrey J; Redish, A David (2015) Representations of Value in the Brain: An Embarrassment of Riches? PLoS Biol 13:e1002174|
|Regier, Paul S; Amemiya, Seiichiro; Redish, A David (2015) Hippocampus and subregions of the dorsal striatum respond differently to a behavioral strategy change on a spatial navigation task. J Neurophysiol 114:1399-416|
|Redish, A David; Mizumori, Sheri J Y (2015) Memory and decision making. Neurobiol Learn Mem 117:1-3|
|Regier, Paul S; Redish, A David (2015) Contingency Management and Deliberative Decision-Making Processes. Front Psychiatry 6:76|
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