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Furthermore, at the level of mechanistic implementation, a biophysically based neural model grounded in reinforcement learning was also shown to reproduce matching behavior in a baiting task.

This width dependence allows explaining gate voltage matching behavior in the sub-threshold area used in low power analog applications.

Additionally, a new molecular matching feature provides an immediate feedback on a pattern's matching behavior across the molecule sets.

In this study, we propose a view that unifies matching behavior into the general computational framework of reward maximization.

Decision-making models to reproduce the matching behavior have been proposed[9], [16], [17], and recent computational studies pointed out possible origins of matching behavior in biological neural systems[18], [19].

They find matching behavior in the animals.

A question of particular interest is why the animals exhibited matching behavior in this task.

We observed matching behavior in a task in which more reward could be harvested if animals over-matched the reward proportions.

First, matching behavior in the above form is consistently observed specifically in tasks that use or can be characterized by concurrent variable interval (VI-VI) schedules of reinforcement.

However, whether matching and maximizing share a common computational principle and whether matching behavior is beneficial to decision making remain unclear.

We first prove that partial maximization of reward leads to matching behavior irrespective of the mathematical algorithm used for this computation.