What is in the feedback? Effect of induced happiness vs. sadness on probabilistic learning with vs. without...

According to dominant neuropsychological theories of affect, emotions signal salience of events and in turn facilitate a wide spectrum of response options or action tendencies. Valence of an emotional experience is pivotal here, as it alters reward and punishment processing, as well as the balance between safety and risk taking, which can be translated into changes in the exploration-exploitation trade-off during reinforcement learning (RL). To test this idea, we compared the behavioral performance of three groups of participants that all completed a variant of a standard probabilistic learning task, but who differed regarding which mood state was actually induced and maintained (happy, sad or neutral). To foster a change from an exploration to an exploitation-based mode, we removed feedback information once learning was reliably established. Although changes in mood were successful, learning performance was balanced between the three groups. Critically, when focusing on exploitation-driven learning only, they did not differ either. Moreover, mood valence did not alter the learning rate or exploration per se, when titrated using complementing computational modeling. By comparing systematically these results to our previous study (Bakic et al., 2014), we found that arousal levels did differ between studies, which might account for limited modulatory effects of (positive) mood on RL in the present case. These results challenge the assumption that mood valence alone is enough to create strong shifts in the way exploitation or exploration is eventually carried out during (probabilistic) learning. In this context, we discuss the possibility that both valence and arousal are actually necessary components of the emotional mood state to yield changes in the use and exploration of incentives cues during RL.