Essentially, this trend entails that theories about the cognitive Z-VAD-FMK ic50 processes under consideration are explicated in mathematical or computational form, and these formal models are used to make inferences about the neural data. The model-based approach has been successfully applied in perceptual decision neurosciences [3••]. Perceptual decision neurosciences

study the neural networks underlying simple perceptual choices. By relating these networks to properties of cognitive models, the model-based neuroscience approach has greatly increased our understanding of how the brain controls the behavioral outcome of simple choices. A prominent model that has been instrumental in the success of model-based perceptual decision neurosciences is the diffusion model of choice reaction time [4••].

Essentially, the diffusion model assumes that the difference in evidence for two response alternatives is represented by Enzalutamide ic50 a biased random walk process (Figure 1). The bias in this process is referred to as drift rate. Decisions are made as soon as the random walk hits one of two boundaries, with each boundary representing one response alternative. Because the drift is a random walk process, each boundary can in principle be reached. However, a positive drift rate means that it is more likely that the random walk will be towards the upper boundary, making the associated response more likely. The time required to reach a boundary represents the decision time, which is a function of both the drift rate and the boundary separation. That is, higher drift rates as well as boundaries that are closer together lead to lower decision times. The observed response time is then the sum

of the decision time and the time required for additional, non-decision related processes. Crucially, because of the stochastic nature of the random walk process, the diffusion model PRKD3 predicts the proportion in which each boundary is reached, and the distributions of finishing times of the process. Thus, when fit to experimental data, the diffusion model explains both the proportion of correct and erroneous responses, as well as the distribution of response times of each of these response types. Only recently, the diffusion model and related models have been applied to more complex cognitive behaviors in which control is exerted over a decision (e.g. 5, 6•, 7, 8, 9 and 10). That is, certain paradigms require that decision makers ignore an interfering irrelevant stimulus feature and focus on a task-relevant feature instead. Often, the irrelevant feature relates to a direct mapping between stimulus and response, making the task to ignore this feature difficult [11].