Topic: naturalistic decision-making

Dopamine (DA) in the nucleus accumbens (NAc) is a critical signal for both learning and motivation. While DA release dynamics are often associated with reward prediction errors, they also scale remarkably well with a state-value signal – expectations of reward from an animal’s current state. However, computing value in real-world environments poses a substantial computational problem. Expectations of future rewards from a given state depend on future actions and their reward likelihood. Computing state value, therefore, necessitates assumptions about future navigational choices. Furthermore, v

Animals readily make value-based decisions between discrete and static items, with decades of work elucidating neural circuits involved in such choices (Rangel et al., 2015). Much less is known about naturalistic decision-making contexts such as goal pursuit (e.g., hunting). Dynamic choices require determining the value of blending different goal-pursuit strategies, switching goals, and continuously monitoring the actions and goals most valuable (Yoo et al., 2021). Despite its prevalence, there is a lack of methods to identify continuous decision processes and thus a dearth of understanding th

Traditional approaches to studying decision-making in neuroscience focus on simplified behavioral tasks where animals perform repetitive, stereotyped actions to receive explicit rewards. While informative, these methods constrain our understanding of decision-making to short timescale behaviors driven by explicit goals. In natural environments, animals exhibit more complex, long-term behaviors driven by intrinsic motivations that are often unobservable. Recent works in time-varying inverse reinforcement learning (IRL) aim to capture shifting motivations in long-term, freely moving behaviors. H