Evidence integration in model-based tree search.
Research on the dynamics of reward-based, goal-directed decision making has largely focused on simple choice, where participants decide among a set of unitary, mutually exclusive options. Recent work suggests that the deliberation process underlying simple choice can be understood in terms of evidence integration: Noisy evidence in favor of each option accrues over time, until the evidence in favor of one option is significantly greater than the rest. However, real-life decisions often involve not one, but several steps of action, requiring a consideration of cumulative rewards and a sensitivity to recursive decision structure. We present results from two experiments that leveraged techniques previously applied to simple choice to shed light on the deliberation process underlying multistep choice. We interpret the results from these experiments in terms of a new computational model, which extends the evidence accumulation perspective to multiple steps of action.
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544; asolway@vt.edu.
Alec
A
Solway
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544; Department of Psychology, Princeton University, Princeton, NJ 08544; Google DeepMind, London EC4A 3TW, United Kingdom.
Matthew M
MM
Botvinick
D001499:Bayes Theorem; D002755:Choice Behavior; D003198:Computer Simulation; D003657:Decision Making; D006801:Humans; D007858:Learning; D008959:Models, Neurological; D012054:Reinforcement, Psychology; D015203:Reproducibility of Results; D012201:Reward
drift-diffusion model; reinforcement learning; reward-based decision making