Topic: normative models

Classical studies in reinforcement learning (RL) heavily rely on normative models of behavior, models that often stress interpretability over predictive capabilities. More recently, neural network models emerged as a descriptive modeling paradigm capable of high predictive power, however, with limited interpretation. To address these issues, we sought to augment the expressiveness of theoretically interpretable RL models with the high flexibility and predictive power of neural networks. Specifically, we introduce a novel framework whereby a neural network is trained to predict theoretically me

Decision making is essential to cognition, often requiring the integration of sequences of observations over time. Choice rules trigger decisions once sufficient evidence is acquired for a choice. Models often describe single accumulation-to-bound processes, but organisms often decide in groups or accumulate multiple information streams. A better understanding of naturalistic decision making thus requires probing the neural computations and behaviors of evidence accumulation processes involving the integration of multiple information streams. Normative models of information-sharing in multi-ag