It also presents new avenues to explore with relation to disorders of compulsivity, where an imbalance between systems has been implicated.ДЄ number of studies have framed goal-directed and habitual decision making from the perspective of different reinforcement learning algorithms (вЂњmodel-basedвЂќ and вЂњmodel-freeвЂќ), and further suggested that they are supported by separate though potentially overlapping systems. Our work highlights the importance of non-linear conflict effects, and provides new constraints for more detailed process models of decision making.

We also found that between-system conflict was highly correlated with within-system conflict, and although it is less clear a priori why the latter might influence the strength of each system above its standard linear contribution, we could not rule it out. Action conflict itself (the binary state) acted in the opposite direction, although its effects were less prominent. We found that decisions with greater value conflict were characterized by reduced model-based control and increased caution both with and without action conflict. We differentiated two forms of conflict: action conflict, a binary state representing whether the systems disagreed on the best action, and value conflict, a continuous measure of the extent to which the two systems disagreed on the difference in value between the available options. Using an integrated reinforcement learning/drift-diffusion model, we tested the hypothesis that the two systems interact in a non-linear fashion similar to other situations with cognitive conflict. Most previous work has assumed that while the systems operate in parallel, they do so independently, and they combine linearly to influence decisions. However, it is not clear how the two systems interact during deliberation and how a single decision emerges from this process, especially when they disagree. The вЂњmodel-freeвЂќ system caches and uses stimulusвЂ“value or stimulusвЂ“response associations, and the вЂњmodel-basedвЂќ system implements more flexible planning using a model of the world.

A large literature has accumulated suggesting that human and animal decision making is driven by at least two systems, and that important functions of these systems can be captured by reinforcement learning algorithms.

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