This paper investigates the low level bodily correlates of affective states, such as boredom, confusion, anxiety, and frustration, that spontaneously emerge during complex problem solving tasks. Participants were video recorded while they solved difficult analytical reasoning problems after which they self-reported their affective states via a retrospective affect-judgment protocol. Time series of bodily motions were automatically extracted from the videos of participants faces and upper bodies via a motion filtering algorithm. Recurrence quantification analyses revealed that participants who reported increased levels of anxiety and frustration had less recurrent and deterministic movements compared to their counterparts. Importantly, these patterns could not be explained by the mere amount of movement or the variability in movement, but by non-obvious dynamical patterns in movement. We orient our findings towards theories that emphasize complex systems approaches to studying emotion.