Abstract
The evidence that a portion of the neurophysiological processes that are involved in performing an action are also involved during observation of that action (Higuchi et al., 2012) supports the idea that skill learning is enhanced by skill observation (Hayes et al., 2010). Recent research on this phenomenon indicates that skill learning through observation is optimized when the observation includes a combination of expert and novice models (Rohbanford and Proteau, 2011). The experts demonstrate blueprints of the task, while novices demonstrate the mistakes that may occur. In this study we explored whether mixed-model observational learning is subject to contextual interference effects. Contextual interference refers to manipulations of cognitive events during learning that facilitate skill retention (Lee and Magill, 1983). Three groups of participants engaged in an observational learning study of a precision bi-manual endoscopic task, which involved picking up a bean with a grasper implement, transferring it to another, and placing it into a small pot all within the confines of simulated minimal access surgical environment. Skill practice involved sets of physical practice (3 blocks; 6 trials/block) that were interspersed with sets of observational practice (4 blocks; 10 trials/block). The first group’s (Blocked; n=15) observational sets were organized such that the first 2 blocks consisted of expert content followed by 2 blocks of novice content. The observational sets for the second group (Semi-Interleaved; n=15) were organized to include interleaving expert and novice content that alternated every 5 attempts, within each set. The third group’s (Interleaved; n=15) observational set consisted of expert and novice content alternating after each attempt. All three groups werer counterbalanced. All participants performed post-practice, retention, and transfer tests. Preliminary analyses indicate that organizing mixed-model observational practice in an interleaved order elicits increased transfer of skill learning (F1,18= 4.34, p=0.052).Acknowledgments: SIM-one, Ontario Simulation Network