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Decision making is the process of choosing among alternatives associated with different outcomes. Deciding among multiple options can take the form of selecting a course of action or judging among ideas.
Complex decision-making tasks can be addressed with artificial intelligence (AI) systems that complement human capabilities. In this Perspective, Gonzalez and Heidari provide a conceptual framework to integrate human and AI decision-making, highlighting technical challenges and ethical risks.
The mediodorsal thalamus (MD) is central to flexible decision-making. Here, the authors identify MD’s unique roles in arbitrating between different reinforcement learning strategies through prefrontal-striatal brain circuits.
The face reveals more than just emotion. Cazettes, Reato and colleagues show that subtle facial movements reveal hidden cognitive states, reflecting the brain’s ongoing computations and offering a noninvasive window into unexpressed thoughts and decisions.
Simultaneous recordings were made of hundreds of neurons in the rat frontal cortex and striatum, showing that decision commitment involves a rapid, coordinated transition in dynamical regime and neural mode.
Prior learning, rather than decision-making-related processes, primarily shapes the subjective experience-based weighting humans assign to potential losses and gains during choices involving monetary risk-taking.
Movement-related dopamine neuronal activity in the tail of the striatum encodes a value-free action prediction error that reinforces state-action associations, biasing mice to repeat past actions.
A study in mice helps to resolve a debate surrounding striatal DA dynamics and reward benefit or cost and also reveals motivation and transient striatal DA release have a bidirectional causal relationship.