A new model is presented to simultaneously study the latent cognitive processes underlying decision making and instrumental learning.

Local disinhibition provides a biologically plausible mechanism for flexible top-down control of network states that integrates normalized value coding, winner-take-all choice, and persistent activity in a single circuit of decision-making.

Ketamine, an NMDA receptor antagonist and experimental model for schizophrenia, produces decision-making deficits in monkeys, which are predicted by a lowering of cortical excitation-inhibition balance in a spiking circuit model.

The value of any choice is not static but dynamically changes as a function of the context of the alternatives even if they are seemingly irrelevant.

In a drug choice setting, rats’ preference is initially driven by deliberative processes but shifts to more automatic selection processes after extended training, in accordance with the sequential choice model.

A novel animal model of economic decision-making captures complex patterns of choice behavior similar to those of humans, opening the way for mechanistic studies to probe the neural basis for this important form of executive function.

A model framework and toolbox to quantify metacognitive biases and sources of metacognitive noise in animal and human confidence data.

A comparison between hierarchical and flat models of decision-making refutes flat models because they lack flexibility and are not supported by behavioral and neural data.

Sophisticated decision-making mechanisms and complex experimental paradigms can be modeled, simulated, and fit to empirical response time data, using a flexible and efficient computational modeling framework.

Intra-individual variability in choice, response time, subjective effort, confidence, and choice-induced preference change and certainty gain is explained by a cost–benefit model of cognitive resource allocation.