title: Adapting without reinforcement.
abstract: Our data rule out a broad class of behavioral models in which behavioral change is guided by differential reinforcement. To demonstrate this, we showed that the number of reinforcers missed before the subject shifted its behavior was not sufficient to drive behavioral change. What's more, many subjects shifted their behavior to a more optimal strategy even when they had not yet missed a single reinforcer. Naturally, differential reinforcement cannot be said to drive a process that shifts to accommodate to new conditions so adeptly that it doesn't miss a single reinforcer: it would have no input on which to base this shift.
authors: Department of Psychology; Rutgers University; Piscataway, NJ USA.
authors: Aaron
authors: A
authors: Kheifets
authors: C Randy
authors: CR
authors: Gallistel
keywords: decision under uncertainty; model-based control; probability estimation; reinforcement learning; timing