Beta-adrenergic receptors support attention to extinction learning that occurs in the absence, but not the presence, of a context change

The noradrenergic (NA)-system is an important regulator of cognitive function. It contributes to extinction learning (EL), and in disorders where EL is impaired NA-dysfunction has been postulated. We explored whether NA acting on beta-adrenergic-receptors (β-AR), regulates EL that depends on context, but is not fear-associated. We assessed behavior in an “AAA” or “ABA” paradigm: rats were trained for 3 days in a T-maze (context-A) to learn that a reward is consistently found in the goal arm, despite low reward probability. This was followed on day 4 by EL (unrewarded), whereby in the ABA-paradigm, EL was reinforced by a context change (B), and in the AAA-paradigm, no context change occurred. On day 5, re-exposure to the A-context (unrewarded) occurred. Typically, in control “AAA” animals EL occurred on day 4 that progressed further on day 5. In control “ABA” animals, EL also occurred on day 4, followed by renewal of the previously learned (A) behavior on day 5, that was succeeded (on day 5) by extinction of this behavior, as the animals realised that no food reward would be given. Treatment with the β-AR-antagonist, propranolol, prior to EL on day 4, impaired EL in the AAA-paradigm. In the “ABA” paradigm, antagonist treatment on day 4, had no effect on extinction that was reinforced by a context change (B). Furthermore, β-AR-antagonism prior to renewal testing (on day 5) in the ABA-paradigm, resulted in normal renewal behavior, although subsequent extinction of responses during day 5 was prevented by the antagonist. Thus, under both treatment conditions, β-AR-antagonism prevented extinction of the behavior learned in the “A” context. β-AR-blockade during an overt context change did not prevent EL, whereas β-AR were required for EL in an unchanging context. These data suggest that β-AR may support EL by reinforcing attention towards relevant changes in the previously learned experience, and that this process supports extinction learning in constant-context conditions.