Topic: sequential decision-making

Our understanding of how the brain makes decisions has benefited greatly from normative theories that have, for example, established benchmarks for how specific forms of evidence accumulation and decision rules can optimally balance quantities like decision speed and accuracy. These theories have recently been expanded to include adaptive components that are necessary for effective decision making in dynamic environments. However, these extensions remain incomplete, because they have focused primarily on how an unconstrained agent manages single, independent decisions [1]. In contrast, real-wo

Weber’s law concisely describes a mapping between a true change in a physical stimulus and the resulting perceived change. It states that the perceived change governing the behavior is a relative difference, i.e. the true change divided by the magnitude of the two stimuli (Fechner, 1966). This psychophysical phenomenon is observed in domains as varied as time, weight, numerosity, length, brightness, and statistical inference (Dehaene, 2003; Namboodiri et al., 2014). In sequential decision-making, on the other hand, it is usually assumed that behavior reflects the true physical evidence, even w

Humans tend to repeat past actions, with habits as extreme case. With each execution of an action this repetition bias increases the probability to repeat this action. However, there is a lack of experiments investigating repetition bias.To test for the presence of a repetition bias, we developed a new sequential decision-making task (see Figure). For this task, participants had to collect points with four moves and meet a trial-specific goal as closely as possible. To ensure frequent repetition of at least one sequence of actions, we informed participants about a default action sequence that

Sequential decision-making in a dynamic environment requires foresight into future states often influenced by other agents. This study seeks to uncover the neural correlates underlying the computation of self and opponent action values in the brain. To achieve this, rhesus monkeys were trained in a competitive strategy board game, called “four-in-a-row,” in which they placed a series of stones on a 4-by-9 board, taking turns with a computer opponent. In this game, players win by placing a series of four stones horizontally, vertically, or diagonally, and the animal was rewarded only for winnin