An examination of learning processes during critical incident training: Implications for the development of adaptable trainees

Three experiments are reported that examined the process by which trainees learn decision-making skills during a critical incident training program. Formal theories of category learning were used to identify two processes that may be responsible for the acquisition of decision-making skills: rule learning and exemplar learning. Experiments I and 2 used the process dissociation procedure (L. L. Jacoby, 1998) to evaluate the contribution of these processes to performance. The results suggest that trainees used a mixture of rule and exemplar learning. Furthermore, these learning processes were influenced by different aspects of training structure and design. The goal of Experiment 3 was to develop training techniques that enable trainees to use a rule adaptively. Trainees were tested on cases that represented exceptions to the rule. Unexpectedly, the results suggest that providing general instruction regarding the kinds of conditions in which a decision rule does not apply caused them to fixate on the specific conditions mentioned and impaired their ability to identify other conditions in which the rule might not apply. The theoretical, methodological, and practical implications of the results are discussed.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Does affective learning exist in the absence of contingency awareness?

The proposal that affective learning, the learning of likes and dislikes, can exist in the absence of contingency awareness, whereas signal learning, the learning of stimulus relationships, cannot, was investigated in a differential conditioning paradigm that was embedded in a visual masking task. Startle magnitude modulation and changes in verbal ratings served as measures of affective learning, whereas skin conductance was taken to reflect signal learning. Awareness was assessed online with an expectancy dial and in a postexperimental questionnaire. Both between-subject comparisons of verbalizers and nonverbalizers and within-subject comparisons of verbalizers before and after verbalization failed to reveal any evidence for learning, whether affective or otherwise, in the absence of knowledge of the stimulus contingencies. (C) 2001 Academic Press.

Fate of urea nitrogen applied to a banana crop in the wet tropics of Queensland

This paper reports a study in the wet tropics of Queensland on the fate of urea applied to a dry or wet soil surface under banana plants. The transformations of urea were followed in cylindrical microplots (10.3 cm diameter x 23 cm long), a nitrogen (N) balance was conducted in macroplots (3.85 m x 2.0 m) with N-15 labelled urea, and ammonia volatilization was determined with a mass balance micrometeorological method. Most of the urea was hydrolysed within 4 days irrespective of whether the urea was applied onto dry or wet soil. The nitrification rate was slow at the beginning when the soil was dry, but increased greatly after small amounts of rain; in the 9 days after rain 20% of the N applied was converted to nitrate. In the 40 days between urea application and harvesting, the macroplots the banana plants absorbed only 15% of the applied N; at harvest the largest amounts were found in the leaves (3.4%), pseudostem (3.3%) and fruit (2.8%). Only 1% of the applied N was present in the roots. Sixty percent of the applied N was recovered in the soil and 25% was lost from the plant-soil system by either ammonia volatilization, leaching or denitrification. Direct measurements of ammonia volatilization showed that when urea was applied to dry soil, and only small amounts of rain were received, little ammonia was lost (3.2% of applied N). In contrast, when urea was applied onto wet soil, urea hydrolysis occurred immediately, ammonia was volatilized on day zero, and 17.2% of the applied N was lost by the ninth day after that application. In the latter study, although rain fell every day, the extensive canopy of banana plants reduced the rainfall reaching the fertilized area under the bananas to less than half. Thus even though 90 mm of rain fell during the volatilization study, the fertilized area did not receive sufficient water to wash the urea into the soil and prevent ammonia loss. Losses by leaching and denitrification combined amounted to 5% of the applied N.

Early intervention: Narratives of learning, discipline and enculturation

Current understandings about literacy have moved away from the belief that literacy is simply a process that individuals do in their heads. These understandings do not negate the importance of the individual aspects of literacy learning, but they emphasize understandings of literacy as a social practice. In many cases, responses to early literacy intervention seem to be grounded in theories that appear out of step with current literacy research and consequent evidence that literacy is socially and culturally constructed. One such response is the Reading Recovery programme based on Clay’s theory of literacy acquisition. Clay (1992) describes the programme as a second chance to learn. However, others have suggested that programmes like Reading Recovery may in fact work toward the marginalization of particular groups, thereby helping to maintain the status quo along class, gender and ethnic lines. This article allows two professionals to bring their insider’s knowledge of Reading Recovery to an analysis of the construction of the programme. The article interweaves this analysis with the personal narratives of the researchers as they negotiated the borders between different understandings and beliefs about literacy and literacy pedagogy.

Learning of foraging skills by fish

This chapter outlines the relationships between a number of key factors that influence learning and memory, and illustrates them by reference to studies on the foraging behaviour of fish. Learning can lead to significant improvements in foraging performance in only a few exposures, and at least some fish species are capable of adjusting their foraging strategy as patterns of patch profitability change. There is also evidence that the memory window for prey varies between fish species, and that this may be a function of environmental predictability. Convergence between behavioural ecology and comparative psychology offers promise in terms of developing more mechanistically realistic foraging models and explaining apparently 'suboptimal' patterns of behaviour. Foraging decisions involve the interplay between several distinct systems of learning and memory, including those that relate to habitat, food patches, prey types, conspecifics and predators. Fish biologists, therefore, face an interesting challenge in developing integrated accounts of fish foraging that explain how cognitive sophistication can help individual animals to deal with the complexity of the ecological context.