Topic: counterfactual reasoning

The human brain finds solutions to complex multi-stage decision problems that are far more flexible than those learned by artificial systems. Cognitive theories attribute this flexibility to specific algorithms such as hierarchical information processing and counterfactual reasoning. We first developed a moderately challenging multi-stage decision-making task as a baseline paradigm for evaluating the computational basis of cognitive strategies in humans. Comparing behavior to inference models implementing various cognitive algorithms, we found that humans use a hierarchical strategy to solve t

When faced with challenging multi-step decisions, humans are unable to evaluate all branches of the decision tree simultaneously and instead rely on piecemeal hierarchical and counterfactual reasoning strategies. To investigate the neural basis of these cognitive strategies, we developed a challenging multi-step decision task for monkeys in which they had to track an invisible ball through a maze using partial cues about the times when the ball makes turns within the maze. Animals were able to use temporal cues to infer the ball’s exit point. To evaluate the animals’ decision strategy, we comp

Animal behavior results from the integration of sensory stimuli and internal models of the functioning of the environment. Adaptation hinges on optimizing actions for survival: maximizing rewards, and minimizing punishments. For this project, humans and rats were tested in non-sensory tasks, where informed gambling strategies enhance reward outcomes.Our two-alternative forced-choice foraging task involves rats initiating trials with a central nose-poke, followed by withdrawal and selection of a water reward spout based on the estimated reward likelihood on either side. Manipulating the probabi