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        <br> You are done! Thank you for taking part in the experiment. </br>
        <br> You earned ${{  payout  }}. If you would like to receive the money, please provide your venmo information on the next page.  </br>

        <br> <b> Debriefing </b> </br>
        <br> <b> Purpose </b>
        The purpose of this study was to investigate whether your inference abilities help you to learn from others, and, whether your inference and learning performance was impacted by the presence of another observer—if you did not see another observer you were in the control condition without another observer.
        Casually speaking, “making an inference” means that you deduce something unknown from information you have. For example, if you didn’t see the selection but only the points, could you infer what the choice was? And did this information and your ability to infer help you to learn more about the environment?
        Between participants we varied who the other observer was (i.e., no observer, an algorithmic observer, or another human participant) and how often you had access to the same information as the other observer.  The player you observed was based on real human behavior, translated into a computational model to represent human decision-making.
        Overall, this study will provide information about mechanisms of social learning, which is highly relevant for learning as individuals, in groups and the society.
        If you have any questions concerning this research, please feel free to ask. Also, we request that you please do not discuss this experiment with others as they may become future participants in this research study and it may affect our results. </br>

        <br> <b> Further details and supporting research (if participant is interested) </b>
        <br> Relevant for the current project, there have been a number of studies in which research volunteers participate in experimental versions of tasks similar to the one in the current study, which are called multi-armed bandits (Erev et al., 2010; Gershman, 2018, 2019; Hertwig & Erev, 2009; Wilson et al., 2014).
        Specifically, we followed a spatially-correlated multi-armed bandit that was first proposed by Wu et al. (2017). The spatial correlation of rewards should make it a bit easier to make inferences about unknown cells in the grid.
        However, given that you had to deal with missing information should make the game you played more difficult than the originally published study.</br>

        <br> <b> Contact Information </b>
        <br> If you would like additional information about the study or have questions regarding the experiment, please contact Alexandra Ortmann at alexandra.ortmann@stonybrook.edu.  </br>

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