Does basic science knowledge correlate with clinical reasoning in assessments of first-year medical students?

AbstractPrevious research has investigated the outcomes of Problem-based Learning (PBL), but little research has compared competencies in PBL and associated clinical reasoning skills with other competencies in medical education. We used results from formative and summative exams during the first block of medical education to investigate how the performance of beginning, undergraduate medical students on online clinical cases and additional clinical-reasoning questions related to their basic-science knowledge. We found a moderate correlation between clinical-reasoning and basic-science performance. However, the level of correlation suggests that distinct knowledge and skills are involved in clinical reasoning beyond those associated with basic-science knowledge

Perceptual Learning with Adaptively-triggered Comparisons

Recent research has shown that learning technology combining adaptive and perceptual learning (PL) methods can improve pattern recognition, transfer, and fluency in complex learning domains (e.g., Mettler & Kellman, 2014). Both classic research and recent work suggest the benefit of paired comparisons in PL, but no previous work has used adaptive techniques to trigger comparisons. We asked whether PL can be enhanced by adaptively triggered comparison trials, in which erroneous responses led to comparisons designed to distinguish confusable categories. Undergraduates learned to interpret basic patterns from electrocardiograms (ECGs) with either: (1) adaptive PL based on single category exemplars, (2) adaptive PL combined with adaptively triggered comparisons, (3) adaptive PL combined with non-adaptive comparisons. Results showed strong learning in all conditions. Comparison conditions produced the strongest learning gains and showed smaller performance declines over a one-week delay. The results also suggested that adaptively triggered comparisons may enhance training efficienc

Adaptive Perceptual Learning in Electrocardiography: The Synergy of Passive and Active Classification

Recent research suggests that combining adaptive learning algorithms with perceptual learning (PL) methods can accelerate perceptual classification learning in complex domains (e.g., Mettler & Kellman, 2014). We hypothesized that passive presentation of category exemplars might act synergistically with active adaptive learning to further enhance PL. Passive presentation and active adaptive methods were applied to PL and transfer in a complex real-world domain. Undergraduates learned to interpret real electrocardiogram (ECG) tracings by either: (1) making active classifications and receiving feedback, (2) studying passive presentations of correct classifications, or (3) learning with a combination of initial passive presentations followed by active classification. All conditions showed strong transfer to novel ECGs at posttest and after a one-week delay. Most notably, the combined passive-active condition proved the most effective, efficient, and enjoyable. These results help illuminate the processes by which PL advances and have direct implications for perceptual and adaptive learning technology

Connecting Adaptive Perceptual Learning and Signal Detection Theory in Skin Cancer Screening

Combining perceptual learning techniques with adaptive learning algorithms has been shown to accelerate the development of expertise in medical and STEM learning domains (Kellman & Massey, 2013; Kellman, Jacoby, Massey & Krasne, 2022). Virtually all adaptive learning systems have relied on simple accuracy data that does not take into account response bias, a problem that may be especially consequential in multi-category perceptual classifications. We investigated whether adaptive perceptual learning in skin cancer screening can be enhanced by incorporating signal detection theory (SDT) methods that separate sensitivity from criterion. SDT-style concepts were used to alter sequencing, and separately to define mastery (category retirement). SDT retirement used a running d’ estimate calculated from a recent window of trials based on hit and false alarm rates. Undergraduate participants used a Skin Cancer PALM (perceptual adaptive learning module) to learn classification of 10 cancerous and readily-confused non-cancerous skin lesion types. Four adaptive conditions varied either the type of adaptive sequencing (standard vs. SDT) or retirement criteria (standard vs. SDT). A non-adaptive control condition presented didactic instruction on dermatologic screening in video form, including images, classification schemes, and detailed explanations. All adaptive conditions robustly outperformed the non-adaptive control in both learning efficiency and fluency (large effect sizes). Between adaptive conditions, SDT retirement criteria produced greater learning efficiency than standard, accuracy-based mastery criteria at both immediate and delayed posttests (medium effect sizes). SDT sequencing and standard adaptive sequencing did not differ. SDT enhancements to adaptive perceptual learning procedures have potential to enhance learning efficiency

Multiple expressions of “expert” abnormality gist in novices following perceptual learning

Abstract With a brief half-second presentation, a medical expert can determine at above chance levels whether a medical scan she sees is abnormal based on a first impression arising from an initial global image process, termed “gist.” The nature of gist processing is debated but this debate stems from results in medical experts who have years of perceptual experience. The aim of the present study was to determine if gist processing for medical images occurs in naïve (non-medically trained) participants who received a brief perceptual training and to tease apart the nature of that gist signal. We trained 20 naïve participants on a brief perceptual-adaptive training of histology images. After training, naïve observers were able to obtain abnormality detection and abnormality categorization above chance, from a brief 500 ms masked presentation of a histology image, hence showing “gist.” The global signal demonstrated in perceptually trained naïve participants demonstrated multiple dissociable components, with some of these components relating to how rapidly naïve participants learned a normal template during perceptual learning. We suggest that multiple gist signals are present when experts view medical images derived from the tens of thousands of images that they are exposed to throughout their training and careers. We also suggest that a directed learning of a normal template may produce better abnormality detection and identification in radiologists and pathologists