Affective states, exploration, and learning are tightly inter-twined. For example, research has connected surprise to playand learning in early development (Stahl & Feigenson, 2015),but less is known about the potential impact of other affec-tive states and how they might influence exploration and sub-sequent discovery. Given that past research has suggested thatawe may increase feelings of uncertainty and lead to pursuitof cognitive accommodation in adults (Valdesolo & Graham,2014), we posit that awe-induced uncertainty may similarlylead children to think-outside-the-box and explore more duringplay. In Experiment 1, we modify emotion-inducing videos(Awe, Happy and Calm) and validate them on adult partic-ipants using the perceived self-size Circle Task (Bai et al.,2017). In Experiment 2, children were presented with one ofthe three videos and their exploratory play with a novel toy wasrecorded. Results revealed both a significant effect of the ma-nipulation (children associated with smaller selves in the Awecondition) and also an influence of the videos on play. Childrenin the Awe condition played more and explored more variablythan children in the control conditions. These results suggestthat awe influences motivation that increases variability anddiscovery in exploration.

Affective components are frequently overlooked in computational modelling, despite the notable role of emotions in learn-ing. Towards the goal of measuring affect in learning, we developed a theory-based Bayesian model that predicts surprisebased on a learners prior beliefs and the evidence observed, and then compared the model to a physiological measure com-monly suggested to capture surprise: pupil dilation. Critically, we also investigate whether this correlation is strong whenparticipants predict the events. Comparing our model predictions to the first four test trial responses from 93 participants(mean age: 8.00 years) revealed a significant, positive correlation when making predictions (r(9)=.55, p=0.04), a negativecorrelation when only evaluating (r(9)=-.50, p=0.07), and significant difference between groups (z=2.34, p¡0.01). Nextsteps will allow us to build on this result by developing a modified Bayesian model, that takes physiological surprise as acomponent in predicting the participants learning.