Personalized Training Schedules for Retention and Sustainment of Cardiopulmonary Resuscitation Skills.

INTRODUCTION: The study examined how the spacing of training during initial acquisition of cardiopulmonary resuscitation (CPR) skill affects longer-term retention and sustainment of these skills. METHODS: This was a multiphased, longitudinal study. Nursing students were randomly assigned to 2 initial acquisition conditions in which they completed 4 consecutive CPR training sessions spaced by shorter (1 or 7 days) or longer (30 or 90 days) training intervals. Students were additionally randomized to refresh skills for 1 year every 3 months, 6 months, or at a personalized interval prescribed by the Predictive Performance Optimizer (PPO), a cognitive tool that predicts learning and decay over time. RESULTS: At the end of the acquisition period, performance was better if training intervals were shorter. At 3 or 6 months after acquisition, performance was better if initial training intervals were longer. At 1 year after acquisition, compression and ventilation scores did not differ by initial training interval nor by 3-month or PPO-prescribed sustainment interval refreshers. However, 6-month interval refreshers were worse than the PPO for compressions and worse than 3 months for ventilations. At the final test session, participants in the personalized PPO condition had less variability in compression scores than either the 3- or 6-month groups. CONCLUSIONS: Results suggest that CPR learning trajectories may be accelerated by first spacing training sessions by days and then expanding to longer intervals. Personalized scheduling may improve performance, minimize performance variability, and reduce overall training time.

Baseline Cardiopulmonary Resuscitation Skill Performance of Nursing Students Is Improved After One Resuscitation Quality Improvement Skill Refresher.

This article reports the results of baseline cardiopulmonary resuscitation (CPR) skills performance measurements from 467 nursing students. All participants had completed a CPR course. Baseline measurements were compared to performance after one 10-minute refresher training session on the Resuscitation Quality Improvement system. Significant improvements were made after the computer- and practice-based refresher. Findings suggest that staff developers should evaluate the use of audio and visual feedback devices to improve the quality of CPR provided by clinical staff.

Training interval in cardiopulmonary resuscitation.

AIM: Although evidence supports brief, frequent CPR training, optimal training intervals have not been established. The purpose of this study was to compare nursing students' CPR skills (compressions and ventilations) with 4 different spaced training intervals: daily, weekly, monthly, and quarterly, each for 4 times in a row. METHODS: Participants were nursing students (n = 475) in the first year of their prelicensure program in 10 schools of nursing across the United States. They were randomly assigned into the 4 training intervals in each of the schools. Students were trained in CPR on a Laerdal Resusci Anne adult manikin on the Resuscitation Quality Improvement (RQI) mobile simulation station. The outcome measures were quality of compressions and ventilations as measured by the RQI program. RESULTS: Although students were all certified in Basic Life Support prior to the study, they were not able to adequately perform compressions and ventilations at pretest. Overall compression scores improved from sessions 1 to 4 in all training intervals (all p < .001), but shorter intervals (daily training) resulted in larger increases in compression scores by session 4. There were similar findings for ventilation skills, but at session 4, both daily and weekly intervals led to better skill performance. CONCLUSION: For students and other novices learning to perform CPR, the opportunity to train on consecutive days or weeks may be beneficial: if learners are aware of specific errors in performance, it may be easier for them to correct performance and refine skills when there is less time in between practice sessions.

Keeping Bystanders Active: Resuscitating Resuscitation Skills.

Introduction: Sufficient CPR skills in the general population are essential to make them active bystanders and contribute to an effective chain of survival in cardiac arrest emergencies. However, having a large proportion of the population regularly retrained is practically infeasible. Objective: The aim of this study was to assess and retrain cardiopulmonary resuscitation (CPR) skills of individuals who received (limited) CPR training several months to years prior. Method: Ninety-nine German adults in a possession of a driver's license were asked to perform CPR on a Laerdal Resusci Anne® QCPR manikin (Laerdal, Stavanger, Norway). After initial assessment, participants watched an instructional video and completed short, isolated compression, and ventilation practice with live feedback. CPR competency was assessed again after retraining and after a retention interval of 45 min. Results: Our results indicate that only 2% of participants managed to reach the performance criteria set by the European Resuscitation Council Guidelines, with most failing to reach even the lowest levels of performance. This corroborates earlier observations that CPR skills have deteriorated almost completely after a long retention interval, calling into question "one-and-done" certification of this basic life-saving. However, we also demonstrated that performance strikingly increased after watching a 6-min instructional video and a short opportunity for isolated practice. This increase in performance was stable over 45 min with 96% of participants meeting performance levels specified in the Guidelines. Closer inspection of the isolated compression practice data suggests that performance was very high at the start of the practice already, indicating that short refresher videos might suffice to change bystanders that would not have initiated CPR due to lack of knowledge into active first responders. Conclusion: We suggest that short refresher trainings could be an effective and affordable means of improving basic lifesaving skills to increase the effective contribution of bystanders during emergencies.

Mechanisms underlying the spacing effect in learning: A comparison of three computational models.

The spacing effect is one of the most widely replicated results in experimental psychology: Separating practice repetitions by a delay slows learning but enhances retention. The current study tested the suitability of the underlying, explanatory mechanism in three computational models of the spacing effect. The relearning of forgotten material was measured, as the models differ in their predictions of how the initial study conditions should affect relearning. Participants learned Japanese-English paired associates presented in a massed or spaced manner during an acquisition phase. They were tested on the pairs after retention intervals ranging from 1 to 21 days. Corrective feedback was given during retention tests to enable relearning. The results of 2 experiments showed that spacing slowed learning during the acquisition phase, increased retention at the start of tests, and accelerated relearning during tests. Of the 3 models, only 1, the predictive performance equation (PPE), was consistent with the finding of spacing-accelerated relearning. The implications of these results for learning theory and educational practice are discussed. (PsycINFO Database Record

Evaluating the Theoretic Adequacy and Applied Potential of Computational Models of the Spacing Effect.

The spacing effect is among the most widely replicated empirical phenomena in the learning sciences, and its relevance to education and training is readily apparent. Yet successful applications of spacing effect research to education and training is rare. Computational modeling can provide the crucial link between a century of accumulated experimental data on the spacing effect and the emerging interest in using that research to enable adaptive instruction. In this paper, we review relevant literature and identify 10 criteria for rigorously evaluating computational models of the spacing effect. Five relate to evaluating the theoretic adequacy of a model, and five relate to evaluating its application potential. We use these criteria to evaluate a novel computational model of the spacing effect called the Predictive Performance Equation (PPE). Predictive Performance Equation combines elements of earlier models of learning and memory including the General Performance Equation, Adaptive Control of Thought-Rational, and the New Theory of Disuse, giving rise to a novel computational account of the spacing effect that performs favorably across the complete sets of theoretic and applied criteria. We implemented two other previously published computational models of the spacing effect and compare them to PPE using the theoretic and applied criteria as guides.

Cognitive Model of Trust Dynamics Predicts Human Behavior within and between Two Games of Strategic Interaction with Computerized Confederate Agents.

When playing games of strategic interaction, such as iterated Prisoner's Dilemma and iterated Chicken Game, people exhibit specific within-game learning (e.g., learning a game's optimal outcome) as well as transfer of learning between games (e.g., a game's optimal outcome occurring at a higher proportion when played after another game). The reciprocal trust players develop during the first game is thought to mediate transfer of learning effects. Recently, a computational cognitive model using a novel trust mechanism has been shown to account for human behavior in both games, including the transfer between games. We present the results of a study in which we evaluate the model's a priori predictions of human learning and transfer in 16 different conditions. The model's predictive validity is compared against five model variants that lacked a trust mechanism. The results suggest that a trust mechanism is necessary to explain human behavior across multiple conditions, even when a human plays against a non-human agent. The addition of a trust mechanism to the other learning mechanisms within the cognitive architecture, such as sequence learning, instance-based learning, and utility learning, leads to better prediction of the empirical data. It is argued that computational cognitive modeling is a useful tool for studying trust development, calibration, and repair.

Model flexibility analysis.

A good fit of model predictions to empirical data are often used as an argument for model validity. However, if the model is flexible enough to fit a large proportion of potential empirical outcomes, finding a good fit becomes less meaningful. We propose a method for estimating the proportion of potential empirical outcomes that the model can fit: Model Flexibility Analysis (MFA). MFA aids model evaluation by providing a metric for gauging the persuasiveness of a given fit. We demonstrate that MFA can be more informative than merely discounting the fit by the number of free parameters in the model, and show how the number of free parameters does not necessarily correlate with the flexibility of the model. Additionally, we contrast MFA with other flexibility assessment techniques, including Parameter Space Partitioning, Model Mimicry, Minimum Description Length, and Prior Predictive Evaluation. Finally, we provide examples of how MFA can help to inform modeling results and discuss a variety of issues relating to the use of MFA in model validation. (PsycINFO Database Record

Mechanisms for Robust Cognition.

To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness in cognitive systems. We identify three mechanisms that enhance robustness in biological and engineered systems: system control, redundancy, and adaptability. After surveying the psychological literature for evidence of these mechanisms, we provide simulations illustrating how each contributes to robust cognition in a different psychological domain: psychomotor vigilance, semantic memory, and strategy selection. These simulations highlight features of a mathematical approach for quantifying robustness, and they provide concrete examples of mechanisms for robust cognition.

SAwSu: an integrated model of associative and reinforcement learning.

Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid-navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data.

Using computational cognitive modeling to predict dual-task performance with sleep deprivation.

OBJECTIVE: The effects of fatigue on multiple-task performance were explored through computational cognitive modeling. BACKGROUND: Fatigue typically has a negative impact on human performance. Biomathematical models exist that characterize the dynamics of human alertness, but the link between alertness and in situ performance on specific tasks is tenuous. Cognitive architectures offer a principled means of establishing that link. METHOD: We implemented mechanisms for fatigue, which produce microlapses in cognitive processing, into an existing model, adaptive control of thought-rational, and validated the performance predictions with Bratzke, Rolke, Ulrich, and Peters' data on fatigue and multiple-task performance. RESULTS: The microlapse model replicated the human performance results very well with zero free parameters, although the fit was improved when we allowed two individual differences parameters to vary. CONCLUSION: Increased frequency of microlapses as a result of fatigue provides a parsimonious explanation for the impact of fatigue on dual-task performance and is consistent with previous research. APPLICATION: Our results illustrate how using biomathematical models of fatigue in conjunction with a cognitive architecture can result in accurate predictions of the effects of fatigue on dual-task performance. Extending and generalizing this capability has potential utility in any safety-critical domain in which fatigue may affect performance.

Sleep deprivation and sustained attention performance: integrating mathematical and cognitive modeling.

A long history of research has revealed many neurophysiological changes and concomitant behavioral impacts of sleep deprivation, sleep restriction, and circadian rhythms. Little research, however, has been conducted in the area of computational cognitive modeling to understand the information processing mechanisms through which neurobehavioral factors operate to produce degradations in human performance. Our approach to understanding this relationship is to link predictions of overall cognitive functioning, or alertness, from existing biomathematical models to information processing parameters in a cognitive architecture, leveraging the strengths from each to develop a more comprehensive explanation. The integration of these methodologies is used to account for changes in human performance on a sustained attention task across 88 h of total sleep deprivation. The integrated model captures changes due to time awake and circadian rhythms, and it also provides an account for underlying changes in the cognitive processes that give rise to those effects. The results show the potential for developing mechanistic accounts of how fatigue impacts cognition, and they illustrate the increased explanatory power that is possible by combining theoretical insights from multiple methodologies.

A computational model of spatial visualization capacity.

Visualizing spatial material is a cornerstone of human problem solving, but human visualization capacity is sharply limited. To investigate the sources of this limit, we developed a new task to measure visualization accuracy for verbally-described spatial paths (similar to street directions), and implemented a computational process model to perform it. In this model, developed within the Adaptive Control of Thought-Rational (ACT-R) architecture, visualization capacity is limited by three mechanisms. Two of these (associative interference and decay) are longstanding characteristics of ACT-R's declarative memory. A third (spatial interference) is a new mechanism motivated by spatial proximity effects in our data. We tested the model in two experiments, one with parameter-value fitting, and a replication without further fitting. Correspondence between model and data was close in both experiments, suggesting that the model may be useful for understanding why visualizing new, complex spatial material is so difficult.