Pattern Recognition Skills Research Articles

A naturalistic decision-making approach to managing non-routine fire incidents: evidence from expert firefighters

The current paper builds on the naturalistic decision-making paradigm to advance the meaning and application of expert intuition in real-life settings. While conventional models of expert intuition often link intuitive competence to pattern recognition skills, the current paper advances prior knowledge by demonstrating that intuitive expertise is inextricably linked to actors’ ability to discriminate between subtle informational cues through the process of information filtering. Drawing on data from a phenomenological study involving thirty (30) fireground commanders (UK = 15, Nigeria = 15), the study utilised the critical decision method (CDM) to explore the cognitive strategies utilised by these expert fire-fighters. The method entailed that each expert participant shared a retrospective non-routine fire incident that particularly challenged their expertise, allowing the resulting qualitative data to be coded and analysed using a combination of emergent themes analysis (ETA) and interpretative phenomenological analysis (IPA). Specifically, the paper develops and discusses: (i) a theoretical model of intuition that emerged directly from the expert incident accounts (ii) an inventory of forty-two critical cues that aided expert decision-making on the fireground, alongside a cue classification framework. Competence on the fireground was found to be a function of experts’ cue discriminatory and information filtering abilities that subsequently allowed information from multiple sources to be processed and utilised efficiently. Although the firefighting domain formed the central focus of this study, findings are deemed generalisable across other high-risk organisations.

Why Is Clive Bell, an Artist With No Training in Logic, Mathematics, Statistics or Probability, Being Cited as an Expert on J M Keynes’s a Treatise on Probability?

Keynes had already answered Ramsey’s incoherent( bizarre?) criticism that “… the obvious one is that there really do not seem to be any such things as the probability relations he describes ...on page 36 of the A Treatise on Probability BEFORE Ramsey ever made his criticism. Keynes pointed out that the “…analogy between orders of similarity and probability is so great that its apprehension will greatly assist that of the ideas I wish to convey.” Ramsey completely failed to grasp the analogy between similarity and probability before criticizing Keynes. No where does Ramsey show that he understands the analogy between probability and similarity that Keynes is using. Nor has any philosopher, economist, psychologist, sociologist, historian or decision theorist demonstrated any understanding of Keynes’s argument in the 20th or 21st century. Only in the writings of cognitive psychologists and cognitive scientists is their evidence that Keynes’s position has been understood. I know of NO such specialist who has critiqued Keynes’s position. This means that Ramsey’s critique is based on ignorance on his part. Before one can rationally criticize any argument of any type, the person doing the critique MUST demonstrate that he understands the position he is critiquing. Keynes’s introductory comments on p.36 were then explored in Part III of the A Treatise on Probability in far, far, far greater depth and detail by Keynes. Keynes’s objective probability relations are simply objective similarity relations existing between old known situations and new unknown situations. Human pattern recognition skills involve using resemblance functions based on past memory that projects past knowledge of old situations into new situations, where there are similarities that are seen to exist between the old, known situation and a new, unexplored situation by SOME researchers. One then can come up with a rational degree of belief regarding how some new situation will play out, given the similarities between the old and new situations. Herbert Simon independently rediscovered some parts of Keynes’s Part III analysis in the A Treatise on Probability that dealt with the connection between intuition and induction when he started to analyze the decision making capabilities of tournament chess players in Over the Board competition, where the players must make decisions under time constrain(a clock),in the 1950’s. Simon was a part of the developing fields of Cognitive Science and cognitive psychology that started in the 1950’s.Keynes ‘s positions on intuition, induction, similarity (dissimilarity), resemblances, analogy and pattern recognition are all accepted basic conclusions in these fields. Ramsey’s concept that supposed humans were capable of calculating exact, precise probabilities, using the purely mathematical laws of the probability calculus to make decisions, is completely rejected except as very special case. One very special case would be correspondence (postal) chess where games can last for years. Another special case would be chess computers, like Deep Blue, that were allowed to bring their “book” knowledge of opening, middle, and end game positions (gigantic libraries containing all known variations of past played games) with them to the chess board. Garry Kasparov, the then World Chess champion, defeated Deep Blue 4-2 in 1996 and lost a rematch 2.5-3.5 in 1997. No cognitive scientist or cognitive psychologist would accept Ramsey’s claims about decision making by humans, be it in 1950 or 2020, because all of the empirical and experimental evidence is completely against Ramsey’s position on the ability of humans to use precise probability. It is very annoying to find in recent reviewers of Misak’s book, including Misak’s own commentary on Ramsey, a reliance on a completely and technically ignorant artist friend of Keynes, Clive Bell, who had no idea about what Keynes was doing in the A Treatise on Probability.The reason is simply because he was completely illiterate of any knowledge of mathematics statistics logic, and probability. I find it bizarre that he is considered a legitimate source that supposedly validates Ramsey’s silly, ignorant comments about Keynes’s “objective probability relations”. The argument seems to go like this ,where (a) ,(b) and (c) are the premises of the argument and (d) is the conclusion of the argument: (a) Ramsey was a great mathematical and logical genius (b) Clive Bell lived and talked with Keynes (c) Clive Bell stated that Ramsey’s critique demolished Keynes’s logical theory of probability (d) Ramsey’s critique demolished Keynes’s logical theory of probability This is what Misak’s argument amounts to.

Representation of dark skin images of common dermatologic conditions in educational resources: A cross-sectional analysis

Representation of dark skin images of common dermatologic conditions in educational resources: A cross-sectional analysis

Novel Biased Normalized Cuts Approach for the Automatic Segmentation of the Conjunctiva

Anemia is a common public health disease diffused worldwide. In many cases it affects the daily lives of patients needing medical assistance and continuous monitoring. Medical literature states empirical evidence of a correlation between conjunctival pallor on physical examinations and its association with anemia diagnosis. Although humans exhibit a natural expertise in pattern recognition and associative skills based on hue properties, the variance of estimates is high, requiring blood sampling even for monitoring. To design automatic systems for the objective evaluation of pallor utilizing digital images of the conjunctiva, it is necessary to obtain reliable automatic segmentation of the eyelid conjunctiva. In this study, we propose a graph partitioning segmentation approach. The semantic segmentation procedure of a diagnostically meaningful region of interest has been proposed for exploiting normalized cuts for perceptual grouping, thereby introducing a bias towards spectrophotometry features of hemoglobin. The reliability of the identification of the region of interest is demonstrated both with standard metrics and by measuring the correlation between the color of the ROI and the hemoglobin level based on 94 samples distributed in relation to age, sex and hemoglobin concentration. The region of interest automatically segmented is suitable for diagnostic procedures based on quantitative hemoglobin estimation of exposed tissues of the conjunctiva.

What's Important: A Call for a Preoperative Psychiatric Risk-Assessment Metric for Orthopaedic Surgery.

What's Important: A Call for a Preoperative Psychiatric Risk-Assessment Metric for Orthopaedic Surgery.

Short term Predication of Risk Management Integrating Artificial Neural Network ANN

The IT industry has boomed in the past few years with an ever increasing number of risk management applications being developed. There are inherent risks in software development projects and failure to deliver software projects within deadline or failure to develop software according to specifications can be costly. The software risks may occur during the project process. The management process of software risks consists the risk refinement, risk identification, risk monitoring, risk maintenance, risk estimation and risk mitigation. Neural Network has ability to stimulate hidden pattern recognition skill. The primary study of this paper is to focus on various risk management models and how risk tools may help in mitigating software risks during the project development. With the application of Neural Network, We propose short term risk management model which can predict the risk involvement with the upcoming project risks, analyzing from the previous projects causing serious loss in the IT project in terms of values on certain risk factors. Neural Network model can also ability to evaluate the assessment of risks in software development and acts as an effective instrument in analysis and minimizing risks that enable continuous improvement in software processes and products.

Why does my brain hurt? Pathologist performance and pattern recognition

Aim: To test whether indicators of pattern recognition moderate performance of new tasks. Background: You, or your colleagues may have participated in this research, where we recruited Anatomical Pathologists and Registrars at the 2019 RCPA Update meeting, and online, to complete an online series of tasks. Methods: We recruited 54 participants, who completed a series of 5 online tasks.1 To measure ‘brain strain’, 27 participants wore an infrared head band measuring pre-frontal cortex blood flow.2 Result: Higher pattern recognition was associated with greater accuracy on diagnostic tasks, and diagnosis of uncommon entities. Those participants with lower pattern recognition were less likely to select unlisted diagnoses for example, ‘none of the above’ in multiple choice questions. High pattern recognition was associated with higher cerebral blood flow. Conclusion: The results validated our online task tool.1 The finding that those participants with lower pattern recognition skills were more likely to select an incorrect diagnosis from a list of answers indicates susceptibility to confirmation, and availability biases.3 Those participants with higher pattern recognition were less prone these cognitive errors. The finding of higher ‘brain strain’ in those with high pattern recognition suggests that higher levels of accuracy were achieved with greater cognitive effort. Reproducibility and longitudinal studies are planned.

Leaf detective: using evidence of damage on mangrove leaves to measure the effects of different herbivores

Herbivores are important to ecosystems because they transfer energy stored in plant matter to other organisms. However, when herbivores occur in high abundances, they can become pests and harm the plants that form the basis of food webs. Mangroves are saltwater tolerant trees found along most tropical and subtropical shorelines. Because mangroves live between land and sea, a wide range of herbivores and other organisms, such as pathogens and parasites, affect them. Many of these organisms leave distinctive marks on the leaves that they eat or infect; scientists can use those marks to help identify the organisms that caused the damage. In this activity, students will take on the role of “Leaf Detectives” and examine patterns of leaf damage on red mangrove leaves (Rhizophora mangle) to infer which organism(s) are harming the mangroves on fictional Lobster Island. Students will collect data from their sample leaves, summarize the results with a graph, and then make a conclusion based on the scientific evidence. By classifying and measuring different damage patterns on leaves, students will learn how different organisms affect plants and food webs, develop pattern recognition skills, and practice using the scientific method to make evidence-based conclusions.

Computer-aided detection tool development for teaching chest radiograph pattern recognition to undergraduate radiography students: A context needs and capability analysis.

BackgroundMedical imaging (MI) education has experienced a shift aligned with the advances in technology and the role played by radiographers in pattern recognition. This has led to increased use of technology-enhanced teaching and simulated learning approaches (e.g. computer-aided detection [CAD] tools) which also support the increasing requirement to develop pattern-recognition skills at undergraduate level. However, the development of these approaches need to be explored and planned carefully to be context-relevant.AimThe aim of this study was to explore and describe the need for and capability of a CAD tool for teaching chest radiography pattern recognition in an undergraduate radiography programme.SettingThe setting was a university that offers MI education.MethodThe study employed a qualitative descriptive design with an interpretive research paradigm. Purposive sampling was used to recruit information-rich participants for a focus group interview. Information-rich participants were considered to be those who were involved in teaching clinical skills, such as those required in pattern recognition, to radiography students. Data were transcribed verbatim and analysed in a step-by-step approach.ResultsThree main themes emerged: (1) a structured approach to enhance implicit skills is critical in the CAD tool design; (2) an authentic tool which is able to simulate real-world experiences in image analysis is essential; and (3) a tool which encourages self-directed learning using a wide variety of pathological conditions would be ideal.ConclusionThe results of this study are essential in guiding radiography educators in designing CAD tools for teaching chest radiography pattern recognition.

De novo protein design by citizen scientists.

Online citizen science projects such as GalaxyZoo1, Eyewire2 and Phylo3 have been very successful for data collection, annotation, and processing, but for the most part have harnessed human pattern recognition skills rather than human creativity. An exception is the game EteRNA4, in which game players learn to build new RNA structures by exploring the discrete two-dimensional space of Watson-Crick base pairing possibilities. Building new proteins, however, is a more challenging task to present in a game, as both the representation and evaluation of a protein structure are intrinsically three-dimensional. We posed the challenge of de novo protein design in the online protein folding game Foldit5. Players were presented with a fully extended peptide chain and challenged to craft a folded protein structure with an amino acid sequence encoding that structure. After many iterations of player design, analysis of the top scoring solutions, and subsequent game improvement, Foldit players can now, starting from an extended polypeptide chain, generate a diversity of protein structures and sequences which encode them in silico. 146 Foldit player designs with sequences unrelated to naturally occurring proteins were encoded in synthetic genes; 56 were found to be expressed in E. coli with good solubility and to adopt stable monomeric folded structures in solution. The diversity of these structures is unprecedented in de novo protein design, representing 20 different folds—including a new fold not observed in natural proteins. High resolution structures were determined for four of the designs, and are nearly identical to the player models. This work makes explicit the considerable implicit knowledge contributing to success in de novo protein design, and shows that citizen scientists can discover creative new solutions to outstanding scientific challenges, such as the protein design problem.

Does “See One and Do One” Work in Histology?: A Two‐Phase Cross‐Over Eye Tracking Study on Complex Histological Pattern Recognition Skills Acquisition

Histology, the study of tissues, is a visual subject that requires competency in pattern recognition. Histology education has evolved over the past several decades, driven by both the advent of technology like virtual microscopy and curricular changes in medical education which have significantly reduced classroom contact hours. As a result, much histology education now occurs in integrated curricula and uses a virtual platform to teach pattern recognition. Although the many advantages of virtual microscopy have been well documented, few studies have investigated how students acquire pattern recognition skills in histology. In a previous eye‐tracking study using simple blood smear visual fields, feedforward training – seeing how an expert scans a visual field to identify leukocyte types – seemed to help students gain visual literacy more efficiently, as evidenced by increased average fixation duration (eyes lingering on visually salient areas) and decreased scan path distance (less eye roaming). Expanding on this work, the goal of the current study was to investigate the educational efficacy of feedforward training in histology with much more complex systemic tissue images, containing significantly more visually salient regions and distractors. In a two‐phase cross‐over design, first year graduate students in a histology course were randomized into two groups: Group A and Group B. In the first phase, Group A was exposed to feedforward training with gastrointestinal tract tissue images over which an expert's eye scan paths were displayed, while Group B, as a control group, viewed the same series of tissue images but without the expert's scan paths. In the second phase, Group B was exposed to feedforward training with lymphatic system tissue images with the expert eye scan paths, while Group A served as the control group. In both phases, all other visual content, exposure time, and data collection tools were identical between the two groups. Participants were tasked to identify various organs or regions from series of relevant tissue images. During these tasks, participants' eye movements were recorded and analyzed to calculate identification accuracy and speed, and fixation number and duration. A total of 1848 data sets were collected following COMIRB No. 17‐1324 protocol and analyzed using MATLAB. Preliminary results reveal no significant differences in identification accuracy (t= −1.0914, p= 0.28808), identification speed (t= 0.18468, p= 0.85534), fixation number (t= 0.49266, p= 0.62762), or fixation duration (t=0.36483, p= 0.71906) between the experimental and control groups. The current study suggests that with more complex histological visual fields, the educational impact of feedforward training may be attenuated, and suggests a revised feedforward mechanism or alternate educational modalities for histology education should be explored.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Surgeons see anatomical structures faster and more accurately compared to novices: Development of a pattern recognition skill assessment platform

BackgroundThis study aimed to identify differences in pattern recognition skill among individuals with varying surgical experience. MethodsParticipants reviewed laparoscopic cholecystectomy videos of various difficulty, and paused them when the cystic duct or artery was identified to outline each structure on the monitor. Time taken to identify each structure, accuracy and work load, which was assessed using the NASA-Task Load Index (TLX), were compared among the three groups. ResultsTen students, ten residents and eight attendings participated in the study. Attendings identified the cystic duct and artery significantly faster and more accurately than students, and identified the cystic artery faster than residents. The NASA-TLX score of attendings was significantly lower than that of students and residents. ConclusionsAttendings identified anatomical structures faster, more accurately, and with less effort than students or residents. This platform may be valuable for the assessment and teaching of pattern recognition skill to novice surgeons. Short summaryAccurate anatomical recognition is paramount to proceeding safely in surgery. The assessment platform used in this study differentiated recognition skill among individuals with varing surgical experience.

Accelerating expertise: Perceptual and adaptive learning technology in medical learning

Rationale: Recent advances in the learning sciences offer remarkable potential for improving medical learning and performance. Difficult to teach pattern recognition skills can be systematically accelerated using techniques of perceptual learning (PL). The effectiveness of PL interventions is amplified when they are combined with adaptive learning (AL) technology in perceptual–adaptive learning modules (PALMs).Innovation: Specifically, PALMs incorporate the Adaptive Response Time-based Sequencing (ARTS) system, which leverages learner performance (accuracy and speed) in interactive learning episodes to guide the course of factual, perceptual, or procedural learning, optimize spacing, and lead learners to comprehensive mastery. Here we describe elements and scientific foundations of PL and its embodiment in learning technology. We also consider evidence that AL systems utilizing both accuracy and speed enhance learning efficiency and provide a unified account and potential optimization of spacing effects in learning, as well as supporting accuracy, transfer, and fluency as goals of learning.Results: To illustrate this process, we review some results of earlier PALMs and present new data from a PALM designed to accelerate and improve diagnosis in electrocardiography.Conclusions: Through relatively short training interventions, PALMs produce large and durable improvements in trainees’ abilities to accurately and fluently interpret clinical signs and tests, helping to bridge the gap between novice and expert clinicians.

A new clinical model for facilitating the development of pattern recognition skills in clinical pain assessment

Common, enigmatic musculoskeletal conditions such as whiplash-associated disorder, myofascial pain syndrome, low back pain, headache, fibromyalgia, osteoarthritis, and rotator cuff pathology, account for significant social, economic, and personal burdens on a global scale. Despite their primacy (and shared sequelae) there remains a paucity of available and effective management options for patients with both acute and chronic conditions. Establishing an accurate prognostic or diagnostic profile on a patient-by-patient basis can challenge the insight of both novice and expert clinicians. Questions remain on how and when to choose the right tool(s), at the right time(s), for the right patient(s), for the right problem(s).The aim of this paper is to introduce a new clinical reasoning framework that is simple in presentation but allows interpretation of complex clinical patterns, and is adaptable across patient populations with acute or chronic, traumatic or non-traumatic pain. The concepts of clinical phenotyping (e.g. identifying observable characteristics of an individual resulting from the interaction of his/her genotype and their environment) and triangulation serve as the foundation for this framework. Based on our own clinical and research programs, we present these concepts using two patient cases; a) whiplash-associated disorder (WAD) following a motor vehicle collision and b) mechanical low back pain.

Monkey‐see, Monkey‐do: An Eye‐tracking Study Assessing the Efficacy of Feed‐forward Training in Histology Visual Literacy Development

Histology is a foundational subject in professional health curricula requiring competency in visual identification of cells and tissues on microscope slides. The emergence of virtual microscopy (VM), an app that simulates optical microscopy (OM) in a digital interface, has transformed histology curricula in health sciences education by increasing teaching efficiency and accessibility to students while reducing costs and space associated with OM labs. Over 70% of North American medical schools now report using VM solely or in combination with OMs in histology education. Despite the numerous advantages reported in the literature, no studies have shown VM's ability to enhance acquisition of visual literacy. One method shown to be effective in training novices in a virtual environment is a feed‐forward system used in aircraft inspection training. Novices who are shown expert's eye scan‐paths conducting aircraft inspection perform better on subsequent tasks than those who are not. The objective of this study was to assess whether feed‐forward training would yield higher efficiency in visual literacy development in histology. An expert's eye scan path was recorded using an eye‐tracker while completing white blood cell identification tasks on VM. A scan path includes an overlay of the expert's fixations and saccades (rapid eye movements between fixations). In an IRB‐exempt study, first‐year graduate students enrolled in a histology course, who have not yet encountered the content, were randomly assigned to two groups. The experimental group (n=11) viewed videos of the experts' scan‐path completing blood cell identification tasks on VM. The control group (n=9) viewed the same videos, but without the expert's scan‐path overlay, instead, the correct cells were highlighted at the end of each video. Both groups then completed a series of blood cell identification tasks. Linear mixed model regression analyses were used to analyze group differences for the following parameter averages: (1) time to identification; (2) total number of fixations; (3) total number of saccades; and (4) average length of saccade path. These parameters have been used previously to characterize eye movement profiles for experts and novices. On average, identification time was 1.4% lower [95% CI: 24.6% lower to 17.5% higher] and average saccade length was 17.0% lower [95% CI: 54.5% lower to 11.3% higher] in the experimental group compared to the control group. The number of saccades per slide was 13.8% lower [95% CI: 31.2% lower to 8% higher], and the number of fixations per slide was 12.2% lower [95% CI: 27.7% lower to 6.6% higher] in the experimental group compared to the control group. These results indicate the eye movement parameters between the two groups are noticeably different, with the experimental groups being more similar to expert parameters. This study provides preliminary data that feed‐forward training may be a viable method to teach pattern recognition skills and visual literacy development in histology. Further investigation of this feed‐forward training method could have implications on developing educational interventions that influence future histology curricula.Support or Funding InformationThis abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches

Intermetallic compounds are bestowed by diverse compositions, complex structures, and useful properties for many materials applications. How metallic elements react to form these compounds and what structures they adopt remain challenging questions that defy predictability. Traditional approaches offer some rational strategies to prepare specific classes of intermetallics, such as targeting members within a modular homologous series, manipulating building blocks to assemble new structures, and filling interstitial sites to create stuffed variants. Because these strategies rely on precedent, they cannot foresee surprising results, by definition. Exploratory synthesis, whether through systematic phase diagram investigations or serendipity, is still essential for expanding our knowledge base. Eventually, the relationships may become too complex for the pattern recognition skills to be reliably or practically performed by humans. Complementing these traditional approaches, new machine-learning approaches may be a viable alternative for materials discovery, not only among intermetallics but also more generally to other chemical compounds. In this Account, we survey our own efforts to discover new intermetallic compounds, encompassing gallides, germanides, phosphides, arsenides, and others. We apply various machine-learning methods (such as support vector machine and random forest algorithms) to confront two significant questions in solid state chemistry. First, what crystal structures are adopted by a compound given an arbitrary composition? Initial efforts have focused on binary equiatomic phases AB, ternary equiatomic phases ABC, and full Heusler phases AB2C. Our analysis emphasizes the use of real experimental data and places special value on confirming predictions through experiment. Chemical descriptors are carefully chosen through a rigorous procedure called cluster resolution feature selection. Predictions for crystal structures are quantified by evaluating probabilities. Major results include the discovery of RhCd, the first new binary AB compound to be found in over 15 years, with a CsCl-type structure; the connection between "ambiguous" prediction probabilities and the phenomenon of polymorphism, as illustrated in the case of TiFeP (with TiNiSi- and ZrNiAl-type structures); and the preparation of new predicted Heusler phases MRu2Ga and RuM2Ga (M = first-row transition metal) that are not obvious candidates. Second, how can the search for materials with desired properties be accelerated? One particular application of strong current interest is thermoelectric materials, which present a particular challenge because their optimum performance depends on achieving a balance of many interrelated physical properties. Making use of a recommendation engine developed by Citrine Informatics, we have identified new candidates for thermoelectric materials, including previously unknown compounds (e.g., TiRu2Ga with Heusler structure; Mn(Ru0.4Ge0.6) with CsCl-type structure) and previously reported compounds but counterintuitive candidates (e.g., Gd12Co5Bi). An important lesson in these investigations is that the machine-learning models are only as good as the experimental data used to develop them. Thus, experimental work will continue to be necessary to improve the predictions made by machine learning.

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on the visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography (OCT) has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and, in conjunction with decision-theoretic approaches, used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue.

Improvisation Unfolding: Process, Pattern, and Prediction

The main aim of this article is to argue the case for understanding improvising as a real-time emergent process grounded in collaborative action, while noting that talking about improvisation, bluntly put, is not the same as improvising. The ways in which improvisers respond and adapt to changing circumstances in the moment and over time, it is argued, connect directly to the discipline of process philosophy and involve pattern recognition and creation skills as well as the ability to predict the actions of conspecifics. The conclusion considers some benefits and challenges in approaching improvisation from a process-based and systems-theoretical perspective.

A Case Suspended in Time: The Educational Value of Case Reports.

Although medical case reports have fallen out of favor in the era of the impact factor, there is a long tradition of using case reports for teaching and discovery. Some evidence indicates that writing case reports might improve medical students' critical thinking and writing skills and help prepare them for future scholarly work. From 2009 through 2015, students participating in the case reporting program at a VA hospital produced 250+ case reports, 35 abstracts, and 15 journal publications. Here, three medical students who published their case reports comment on what they learned from the experience. On the basis of their comments, the authors propose five educational benefits of case reporting: observation and pattern recognition skills; hypothesis-generating skills; understanding of patient-centered care; rhetorical versatility; and use of the case report as a rapidly publishable "mini-thesis," which could fulfill MD thesis or scholarly concentration requirements. The authors discuss the concept of the case report as a "hybrid narrative" with simultaneous medical and humanistic significance, and its potential use to teach students about their dual roles as engaged listeners and scientists. Finally, the authors consider the limitations and pitfalls of case reports, including patient confidentiality issues, overinterpretation, emphasis on the rare, and low initial publication rates. Case reports allow students to contribute to medical literature, learn useful scholarly skills, and participate in a tradition that links them with past generations of physicians. The authors conclude that the case report can be an effective teaching tool with a broad range of potential educational benefits.

Experiential (informal/non-formal) practice does not improve sight-reading skills

This study investigates whether experiential (informal/non-formal) sight-reading practice has an impact on sight-reading skills of classical university-level pianists. Data collected from 80 self-report practice diaries of students participating in trials of new sight-reading pedagogies were analysed to evaluate the effect of experiential sight-reading practice on improvement in sight-reading performance, focusing on the overall duration of sight-reading activities accumulated over the 10-week period and different patterns across this time. Data were subjected to bivariate regressions and bivariate correlations analyses. Results showed that improvement in sight-reading performance on three out of the four key indicators (beat adjustment, extra notes and missing notes) could not be predicted by participants’ overall hours of experiential practice. Yet the total number of hours engaged in sight-reading practice did predict an improvement in participant RMS accuracy (average timing errors for each correct note played). Findings also revealed no consistent pattern of practice across the 10 weeks that was associated with any of the four performance indicators. Implications for education include the need to embrace multi-faceted approaches to sight-reading training by focusing on skills contributing to expert sight-reading and their development, individually and in combinations. Rhythm and pitch training, development of pattern recognition and prediction skills, and collaborative playing activities have the potential to enhance sight-reading skills of music undergraduates and have positive flow-on effects for their preparation for 21st century music careers.