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ABSTRACT From navigating a crowded hallway to skiing down a treacherous hill, humans are constantly making decisions while moving. Insightful past work has provided a glimpse of decision deliberation at the moment of movement onset. Yet it is unknown whether ongoing deliberation can be expressed during movement, following movement onset and prior to any decision. Here we tested the idea that an ongoing deliberation continually influences motor processes—prior to a decision—directing online movements. Over three experiments, we manipulated evidence to influence deliberation during movement. The deliberation process was manipulated by having participants observe evidence in the form of tokens that moved into a left or right target. Supporting our hypothesis we found that lateral hand movements reflected deliberation, prior to a decision. We also found that a deliberation urgency signal, which more heavily weighs later evidence, was fundamental to predicting decisions and explains past movement behaviour in a new light. Our paradigm promotes the expression of ongoing deliberation through movement, providing a powerful new window into understanding the interplay between decision and action.
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This content will become publicly available on July 1, 2026
Neural dynamics encoding risky choices during deliberation reveal separate choice subspaces
Human decision-making involves the coordinated activity of multiple brain areas, acting in concert, to enable humans to make choices. Most decisions are carried out under conditions of uncertainty, where the desired outcome may not be achieved if the wrong decision is made. In these cases, humans deliberate before making a choice. The neural dynamics underlying deliberation are unknown and intracranial recordings in clinical settings present a unique opportunity to record high temporal resolution electrophysiological data from many (hundreds) brain locations during behavior. Combined with dynamic systems modeling, these allow identification of latent brain states that describe the neural dynamics during decision-making, providing insight into these neural dynamics and computations. Results show that the neural dynamics underlying risky decisions, but not decisions without risk, converge to separate subspaces depending on the subject’s preferred choice and that the degree of overlap between these subspaces declines as choice approaches, suggesting a network level representation of evidence accumulation. These results bridge the gap between regression analyses and data driven models of latent states and suggest that during risky decisions, deliberation and evidence accumulation toward a final decision are represented by the same neural dynamics, providing novel insights into the neural computations underlying human choice.
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- Award ID(s):
- 2319580
- PAR ID:
- 10624988
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Progress in Neurobiology
- Volume:
- 250
- Issue:
- C
- ISSN:
- 0301-0082
- Page Range / eLocation ID:
- 102776
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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