Bonsai trees in your head: how the Pavlovian system sculpts goal-directed choices by pruning decision trees (original) (raw)

Decision field theory-planning: A cognitive model of planning and dynamic decision making

The world is full of complex environments in which individuals must plan a series of choices to obtain some desired outcome. In these situations entire sequences of events, including one’s future decisions, should be considered before taking an action. Backward induction provides a normative strategy for planning, in which one works backward, deterministically, from the end of a scenario. However, it often fails to account for human behavior. I propose an alternative account, Decision Field Theory-Planning, in which individuals plan future choices on the fly through repeated mental simulations. A key prediction of DFT-P is that payoff variability produces noisy simulations and reduces sensitivity to utility differences. In two multistage risky decision tree experiments I obtained this payoff variability effect, with choice proportions moving toward 0.50 as variability increased. I showed that DFT-P provides valuable insight into the strategies that people used to plan future choices and allocate cognitive resources across decision stages.

Third Symposium on Biology of Decision-making Third Symposium on Biology of Decision-making Morning -neuro-physiology Session Afternoon -neuro-computations Session Third Symposium on Biology of Decision-making Morning -neuro-systems Session

2013

Selective attention in real-world environments enables subjects to direct information gathering systems towards relevant stimuli in the presence of distractors. In a dynamic visual environment that lacks cues identifying the relevance of stimuli for reaching goals, attention requires internal mechanisms to track valuable targets. Reinforcement learning (RL) provides a theoretical framework that is typically applied to describe the mechanisms underlying optimal action control (Lee et al., 2012; Schultz et al., 1997; Sutton and Barto, 1998). Here we propose a RL approach to resolve the credit assignment problem for the deployment of covert attentional selection (Balcarras et al., 2013; Kaping et al., 2011). We compared two types of RL models to explain behavior of two macaques performing a selective attention task during foraging in the space of stimulus features: we show that despite being extensively reinforced on the relevance of stimulus color for reward, monkey behavior does not ...