The Most Effective Guide To SCH727965

66, p more swaps than any other condition (p = 0.01�C0.001), and reported less task difficulty (p-value range = 0.09�C0.002). Additionally, spot-checking of participant data revealed that these effects were not driven by any single outlier, but represent a uniform difference between conditions; participants in the Static w/Goal instructions condition ranged between an average of 134�C336 swaps during their sessions, exceeding the average number of swaps in any other condition (see Table ?Table1).1). This suggests that participants benefited from knowing that the goal of the game was to acquire points, and that the wiggle function added no value. In fact, without appropriate context (e.g., explicit instructions), the added functionality may have made the game more complicated for participants. These findings were further explored through both heuristic and quantitative decomposition of BP-HMM models. Table 1 Cell means and standard deviations for task-dependent performance and experience measures. BP-HMM modeling results The BP-HMM parameters characterize subjects' interactions with the computer game. We interpreted these parameters in the context of the actual dynamics of game-play, and developed qualitative heuristics and quantitative metrics for identifying distinct patterns of strategic interactions with the game interface. We then evaluated these metrics against independently collected data from intake and post-session questionnaires. This approach was designed to determine if the models were able to systematically explain variation in the independent data, or if the BP-HMM method was merely ��fitting noise,�� providing no inferential value for BGB324 clinical trial understanding participants' experiences with the game. The analysis was by necessity post-hoc, since the models generated by the BP-HMM are completely data driven, making traditional a priori hypothesis-testing approaches to model validation impossible. Shared feature activation patterns Our subject population was separated into sub-groups of people with/without the wiggle function enabled in the game. By jointly modeling all sequences together, we were able to identify patterns of behavior exhibited by participants in both the static and wiggle conditions, as well as patterns that were unique to the experimental conditions. The ensemble feature activation map captures the shared feature structure, as shown in Figure ?Figure5.5.