Probabilistic Inference in Goal-Directed Human and Animal Decision-Making

Dear colleagues,

Since Helmholtz’s intuition on visual perception, the hypothesis that the brain performs inferential processing to accomplish perceptual tasks has acquired numerous confirmations and, more generally, the Bayesian probabilistic framework has proven capable of providing computational theories of cognitive functioning with high explanatory value. According to this idea, incoming sensory evidence is modulated by prior information to estimate the state of the external world. Similarly, inferential processing might underlie decision making: Humans and animals are thought to combine habitual control (model-free decision) with predictions from internal models of their interactions with the environment (model-based decision) to flexibly and efficiently guide behavior. However, while model-free inference takes advantage of a consolidated mathematical framework inherited from classical reinforcement learning with highly efficient recent algorithmic-level explanations, model-based decision-making still lacks converging computational theories that are both biologically plausible and cost-effective.

This Special Issue aims to focus on recent advances in probabilistic inference in goal-directed human and animal decision making, and we welcome submissions that:

• Shed light on the computations of neuronal circuits involved in goal-directed decision making, with a focus on the inferential mechanisms involved;
• Propose novel probabilistic models and methods in decision making including (but not limited to) information-theory approaches, statistical and free-energy minimization, hierarchical models, and deep networks;
• Introduce decision-making applications in ethological, social, psychological, psychiatric, robotics, and computer science research.

Dr. Francesco Donnarumma
Dr. Domenico Maisto
Dr. Ivilin Stoianov
Guest Editors

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