The impact of reimbursed Primary Psychological Care in Belgium.

Decisions should be based on evidence. Once sufficient evidence has been sampled, the agent can decide which option to select (Fig. , top). But in addition to guiding the choice, evidence should also simultaneously be used to evaluate whether there is sufficient information to warrant a decision or whether further information must be sampled (Fig. , bottom). When sampling information, attentional constraints mean that decision-makers typically focus on only one option at a time. There is therefore a further decision as to whether more evidence should be gathered from the currently attended option or whether attention should be shifted to an alternative (Fig. , bottom). Gathering more information can improve decision quality; however, it also comes at the expense of time, energy and lost opportunities to engage in other activities. An extreme illustration is the fourteenth-century thought experiment of Buridan’s ass (Fig. ): unable to choose between two equal piles of hay, the ass starves. Although several studies have shed light on the computational and neural mechanisms underlying value learning and option selection, the factors that determine when to stop gathering information and commit to a final decision, as well as which options to sample from, remain an active area of investigation. , The decision-making process involves two key decisions: whether to gather more information or make a selection (top versus bottom) and, if gathering information, whether to sample from the currently attended option or switch to the alternative (top right versus top left). , Three approaches to computing the VoI as a function of the number of samples. Top: a linear function where value decreases at a constant rate with each additional sample. Middle: UCB algorithm that captures diminishing returns, with steeper initial decline that flattens as samples accumulate. Bottom: an ANN that learns the mapping between samples and information value; the learned function’s form is not specified a priori. , Task structure showing the two phases of the information-sampling task. In phase 1, participants are presented with three patches of dots covered by green or gray covers. After revealing the green-covered dots in each patch, one patch is blocked (gray circle). In phase 2, participants can freely sample information by hovering over patches, with gray-covered dots revealing their true colors sequentially, before making a final selection. When participants switched patches, previously revealed dots in the unattended patch returned to their gray-covered state, requiring reliance on memory (as illustrated by the gray patch in phase 2, right panel). , Brain ROIs: LC, DRN, VSN, SN and VTA, which have been implicated in uncertainty processing and information sampling. A standard approach in cognitive neuroscience is to formalize hypotheses about cognitive computations as mathematical models that generate quantitative predictions for behavior and neural activity. Two such hypotheses can be considered for how individuals guide their information sampling. The first posits that people compute the value of gathering additional information as a linear function of an option’s uncertainty (for example, the amount of missing knowledge; Fig. , top). This approach would prioritize further sampling from more uncertain options. However, theories of sequential sampling processes and Bayesian updating indicate that repeatedly sampling from the same option yields diminishing returns (Fig. , middle), motivating a second hypothesis: that the value of information (VoI) is computed using a nonlinear function of uncertainty. The upper confidence bound (UCB) algorithm, a widely used exploration heuristic that captures this nonlinear relation, can formalize this hypothesis. Both linear and UCB models aim to characterize the functional form of a cognitive computation, such as the value of sampling, in a psychologically interpretable way.