Value has often been used as a synonym for reward, with a focus on its hedonic aspect while overlooking its functional concept-like nature. Recent research has started to highlight its functional and goal-dependent aspects by directly manipulating the goal of the task and introducing the concept of usefulness.
Constructing value representations of usefulness involves the process of abstraction, thus reducing dimensions and coding only relevant information. Neural mixed selectivity may be the underlying coding principle of abstract value representations.
Information is selected through cognitive mechanisms like memory and attention. This process requires crosstalk between different brain regions that include sensory cortices, the hippocampus, and the prefrontal cortex.
Metacognition can provide a mechanism for the concurrent roles of monitoring and updating abstract value representations. Algorithmic architectures and their neural implementations are presented and discussed.
Abstract
Colombian drug lord Pablo Escobar, while on the run, purportedly burned two million dollars in banknotes to keep his daughter warm. A stark reminder that, in life, circumstances and goals can quickly change, forcing us to reassess and modify our values on-the-fly. Studies in decision-making and neuroeconomics have often implicitly equated value to reward, emphasising the hedonic and automatic aspect of the value computation, while overlooking its functional (concept-like) nature. Here we outline the computational and biological principles that enable the brain to compute the usefulness of an option or action by creating abstractions that flexibly adapt to changing goals. We present different algorithmic architectures, comparing ideas from artificial intelligence (AI) and cognitive neuroscience with psychological theories and, when possible, drawing parallels.
