In many research domains, researchers have employed gradually morphing pictures to study perception under ambiguity. Despite their inherent utility, only a limited number of stimulus sets are available, and those sets vary substantially in quality and perceptual complexity. Here we present normative data for 40 morphing picture series. In all sets, line drawings of pictures of common objects are morphed over 15 iterations into a completely different object. Objects are either morphed from an animate to an inanimate object (or vice versa) or morphed within the animate and inanimate object categories. These pictures, together with the normative naming data presented here, will be of value for research on a diverse range of questions, from perceptual processing to decision making.

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Right brain damaged patients show impairments in sequential decision making tasks for which healthy people do not show any difficulty. We hypothesized that this difficulty could be due to the failure of right brain damage patients to develop well-matched models of the world. Our motivation is the idea that to navigate uncertainty, humans use models of the world to direct the decisions they make when interacting with their environment. The better the model is, the better their decisions are. To explore the model building and updating process in humans and the basis for impairment after brain injury, we used a computational model of non-stationary sequence learning. RELPH (Reinforcement and Entropy Learned Pruned Hypothesis space) was able to qualitatively and quantitatively reproduce the results of left and right brain damaged patient groups and healthy controls playing a sequential version of Rock, Paper, Scissors. Our results suggests that, in general, humans employ a sub-optimal reinforcement based learning method rather than an objectively better statistical learning approach, and that differences between right brain damaged and healthy control groups can be explained by different exploration policies, rather than qualitatively different learning mechanisms.