A serious-gamification blueprint towards a normalized attention

RADHunters: gamification in radiology perceptual education

Purpose

Gamification is used in several fields as an adjunct to standard educational methods but has found limited application in radiology to date. Gamification may be useful for teaching radiology skills typically acquired through experience, such as perceptual skills. The goal of our study is to use a gamified radiology workstation to teach skills related to identification of pulmonary nodules and evaluate for changes in trainee performance.

Approach

We constructed a game called RADHunters to teach perceptual skills related to identification of pulmonary nodules on chest radiographs. Control and experimental groups were tasked with identifying nodules on chest radiographs on two sets of cases. The experimental group received gamified training for nodule identification using RADHunters between case sets, while the control group did not. Performance at nodule identification, localization, and confidence were compared. A poststudy survey was administered to assess for participants’ thoughts about the gamified nodule detection training.

Results

Survey responses were very positive with p-values for all survey responses <0.001, indicating subjects felt this training was beneficial. Experimental and control groups had a statistically significant improvement in their ability to identify and localize nodules with p-values <0.05. There was no significant difference between control and experimental groups. Neither group showed a statistically significant increase in their confidence in nodule localization.

Conclusions

Perceptual training using gamification may be a useful adjunct to conventional methods of radiology education.

Different Markov chains modulate visual stimuli processing in a Go-Go experiment in 2D, 3D, and augmented reality

The introduction of Augmented Reality (AR) has attracted several developments, although the people's experience of AR has not been clearly studied or contrasted with the human experience in 2D and 3D environments. Here, the directional task was applied in 2D, 3D, and AR using simplified stimulus in video games to determine whether there is a difference in human answer reaction time prediction using context stimulus. Testing of the directional task adapted was also done. Research question: Are the main differences between 2D, 3D, and AR able to be predicted using Markov chains? Methods: A computer was fitted with a digital acquisition card in order to record, test and validate the reaction time (RT) of participants attached to the arranged RT for the theory of Markov chain probability. A Markov chain analysis was performed on the participants' data. Subsequently, the way certain factors influenced participants RT amongst the three tasks time on the accuracy of the participants was sought in the three tasks (environments) were statistically tested using ANOVA. Results: Markov chains of order 1 and 2 successfully reproduced the average reaction time by participants in 3D and AR tasks, having only 2D tasks with the variance predicted with the current state. Moreover, a clear explanation of delayed RT in every environment was done. Mood and coffee did not show significant differences in RTs on a simplified videogame. Gender differences were found in 3D, where endogenous directional goals are in 3D, but no gender differences appeared in AR where exogenous AR buttons can explain the larger RT that compensate for the gender difference. Our results suggest that unconscious preparation of selective choices is not restricted to current motor preparation. Instead, decisions in different environments and gender evolve from the dynamics of preceding cognitive activity can fit and improve neurocomputational models.