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. 2017 Mar 14:345:89-98.
doi: 10.1016/j.neuroscience.2016.02.010. Epub 2016 Feb 10.

Lateral habenula integration of proactive and retroactive information mediates behavioral flexibility

Affiliations

Lateral habenula integration of proactive and retroactive information mediates behavioral flexibility

P M Baker et al. Neuroscience. .

Abstract

The lateral habenula (LHb) is known to play an important role in signaling aversive or adverse events that have happened or are predicted by cues under Pavlovian conditions. In rodents, it is also required for behavioral flexibility when changes in reward outcomes signal that strategies should be changed. It is not known whether the LHb also controls appetitive behaviors when an animal is able to utilize external cues proactively to guide upcoming decisions. In order to test this, male Long-Evans rats were trained to switch between two arms of a figure eight maze based on the tone presented prior to the choice. Importantly, the tones were switched every three to six trials so rats were able establish a response pattern before being required to switch. This caused rats to rely on both proactive (tones) and retroactive information (reward feedback) to guide behavior. Inactivation of the LHb with the GABA agonists baclofen and muscimol impaired overall performance by increasing both errors when the tones are switched (switch errors) as well as on subsequent trials (perseverative errors) indicating that both proactive and retroactive information are utilized by the LHb to guide behavioral flexibility. Once a correct choice was made in a given block, LHb inactivated rats did not make more errors than controls. A control study revealed that the LHb is not required for tone or reward magnitude discrimination per se. These results demonstrate for the first time that the LHb contributes to behavioral flexibility through utilizing both proactive and retroactive information when performing appetitive tasks.

Keywords: behavioral flexibility; cognitive flexibility; habenula; reversal learning; reward prediction error; set-shifting.

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