Inaccurate Prior Knowledge Research Articles

Case-Based Peer Teaching for Pain Medicine Fellows: A Curriculum for Diverse Residency Backgrounds.

Pain medicine is an advanced medical subspecialty incorporating trainees from diverse primary disciplines. A curriculum that does not assess or promote the trainees' differential expertise developed from their primary specialties may silo instead of promoting collaborative learning in a multidisciplinary subspecialty. We created a Kern-guided curriculum for the pain medicine fellowship utilizing case-based modules and a novel note-taking and knowledge-sharing format. Nine faculty-led, case-based modules of increasing complexity were developed based on pain medicine competency content. Pain medicine fellows were instructed to actively designate prior and new knowledge into four categories: Prior knowledge that is accurate and relevant, inaccurate prior knowledge, new knowledge gained from peers, and new knowledge for all trainees. After each case study, each fellow acknowledged "new knowledge gained from peers" and prepared a brief teach-back of "new knowledge for all trainees" at the beginning of the next session. Fellow participants were assessed for their reactions to the curriculum via anonymized surveys. The curriculum was implemented at a single institution pain medicine fellowship from 2021 to 2023. At the end of this curriculum, 90% of participants reported improved knowledge retention through participating in this case-based curriculum compared to the traditional didactic format, and 80% participants reported increased respect for their peers. We created a novel note-taking and knowledge-sharing platform, anchored by a well-established case-based educational strategy, to improve the learning environment and knowledge retention for multidisciplinary trainees with heterogeneous baseline knowledge.

The Robust Ground Clutter Canceller Based on Inaccurate Prior Knowledge in Airborne Radar

Two-dimensional pulse-to-pulse canceller (TDPC) of ground clutter can effectively suppress the clutter along the clutter trace, and therefore the moving target detectability of the following space-time adaptive processing (STAP) algorithm can be improved after TDPC as the clutter prefilter. However, TDPC may greatly impair the energy of moving target when inaccurate knowledge is exploited, which is detrimental to target detection. Aiming at this problem, a robust two-dimensional pulse-to-pulse canceller (RTDPC) of ground clutter is proposed. In order to enhance the TDPC’s robustness with inaccurate radar system parameters, which are mainly the platform velocity and crab angle, the errors of estimated platform velocity and crab angle are taken as the prior knowledge and added into the design of the clutter filter coefficient matrix. By exploiting RTDPC as the clutter prefilter, the moving target detectability of the following nonadaptive detection algorithm or STAP algorithm can also be enhanced. The simulated and MCARM data are utilized to verify the clutter suppression performance of RTDPC with inaccurate platform velocity and crab angle.

An Adaptive Bayesian Melding Method for Reliability Evaluation Via Limited Failure Data: An Application to the Servo Turret

In the early stage of product development, reliability evaluation is an indispensable step before launching a product onto the market. It is not realistic to evaluate the reliability of a new product by a host of reliability tests due to the limiting factors of time and test costs. Evaluating the reliability of products in a short time is a challenging problem. In this paper, an approach is proposed that combines a group of experts’ judgments and limited failure data. Novel features of this approach are that it can reflect various kinds of information without considering the individual weight and reduces aggregation error in the uncertainty quantification of multiple inconsistent pieces of information. First, an expert system is established by the Bayesian best–worst method and fuzzy logic inference, which collects and aggregates a group of expert opinions to estimate the reliability improvement factor. Then, an adaptive Bayesian melding method is investigated to generate a posterior by inaccurate prior knowledge and limited test data; this method is made more computationally efficient by implementing an improved sampling importance resampling algorithm. Finally, an application for the reliability evaluation of a subsystem of a CNC lathe is discussed to illustrate the framework, which is shown to validate the reasonability and robustness of our proposal.

Robot exploration of indoor environments using incomplete and inaccurate prior knowledge

Exploration is a task in which autonomous mobile robots incrementally discover features of interest in initially unknown environments. We consider the problem of exploration for map building, in which a robot explores an indoor environment in order to build a metric map. Most of the current exploration strategies used to select the next best locations to visit ignore prior knowledge about the environments to explore that, in some practical cases, could be available. In this paper, we present an exploration strategy that evaluates the amount of new areas that can be perceived from a location according to a priori knowledge about the structure of the indoor environment being explored, like the floor plan or the contour of external walls. Although this knowledge can be incomplete and inaccurate (e.g., a floor plan typically does not represent furniture and objects and consequently may not fully mirror the structure of the real environment), we experimentally show, both in simulation and with real robots, that employing prior knowledge improves the exploration performance in a wide range of settings.

New algorithm for joint estimation of DOA and polarisation based on electrically large and complete electromagnetic vector sensor

With the development of estimation algorithms for polarisation sensitive array (PSA) parameters, research has been focused on complete/incomplete electromagnetic vector sensor (EMVS) that is mainly based on electrically ‘short’ dipoles and/or electrically ‘small’ circular loops. Recently, electrically ‘long’ dipoles and electrically ‘large’ circular loops are further preferred and have been proposed to utilise more efficiently radiation characteristics. However, almost all available such EMVSs are only composed of either long dipoles or large circular loops. To sufficiently utilise relevant electromagnetic information, in this study an electrically ‘large’ EMVS is proposed, which is composed of a triad of long dipoles and a triad of large circular loops, co-located, and orthogonally oriented. With formulated array manifold of the proposed EMVS, a close-form algorithm is developed to estimate an incident source's azimuth-elevation direction-of-arrival (DOA) and polarisation simultaneously without prior knowledge, and verified that its estimation accuracy is closely approaching corresponding to Cramer-Rao Bound. It is demonstrated that when with inaccurate prior knowledge, the proposed algorithm has better performance than existing algorithms that were designed for the incomplete EMVS with either long dipoles or large loops. Finally, simulation results also show that with increased electrical size of the EMVS, the estimation accuracy is significantly improved.

Judging own and peer performance when using feedback in elementary school

Children find it challenging to self-monitor the quality of their own test responses, and are typically overconfident. Inaccurate self-monitoring may not only be due to a metacognitive deficit, but also to self-protective biases. Therefore, monitoring peer performance and detecting others' errors may be easier than monitoring oneself. This study investigated 97 children's (52 fourth and 45 sixth grade) feedback use when scoring their own and their peers' concept learning. Children completed a concept-learning task, took a test, and then scored their own responses and the responses of one of their peers with use of feedback standards. Error detection was better for peer- than for self-score judgments. Further, monitoring was more accurate for older than younger children, and inaccurate prior knowledge led to less accurate peer and self-judgments. Findings imply that, when implementing co-scoring activities, it is important to be aware that its accuracy is affected by children's age and prior knowledge.

On the performance evaluation of a hierarchical-structure prototype product using inconsistent prior information and limited test data

Performance evaluation or parameter estimation for a hierarchical structure prototype product is an imperative yet difficult task. The challenge comes from both inaccurate prior knowledge and insufficient data acquisition at its early usage period. In this paper, we propose an innovative Bayesian inference framework for the uncertainty quantification of the performance of a newly developed product with hierarchical structure characteristics. We focus particularly on the presence of inconsistent prior distributions on one quantity. An adaptive Bayesian melding method is investigated to aggregate multi-source information in a flexible way. We realize this method via a modified sampling importance re-sampling algorithm. This algorithm is designed to substitute for the MCMC methods and is computationally efficient. We use a practical engineering case to validate our proposal.

Intention–Behavior Discrepancy of Foreign versus Domestic Brands in Emerging Markets: The Relevance of Consumer Prior Knowledge

Most research on the performance of foreign versus domestic brands in emerging markets has examined dependent measures of product evaluation or purchase intention. However, consumers who intend to buy a product may switch to competing brands, thus displaying an intention–behavior discrepancy (IBD). Drawing on literature on country associations and dual process theory, the authors examine the performance of foreign versus domestic brands on IBD in emerging markets and the moderating role of consumer prior knowledge. They conduct an intention survey followed by a postpurchase survey in the Chinese automobile and smartphone industries and find that foreign brands have an advantage on IBD relative to domestic brands, indicating that they have the dual advantage of higher evaluations and lower IBDs. Furthermore, foreign brands’ advantage on IBD is smaller for consumers with inaccurate prior knowledge because they are more likely to systematically reprocess information and discount foreign brands’ favorable country associations. For these consumers, overestimating the product reduces foreign brands’ advantage to a lesser degree than underestimating it as a result of confirmation bias. These findings provide implications for brands in emerging markets.

Enhanced knowledge-aided space–time adaptive processing exploiting inaccurate prior knowledge of the array manifold

The accuracy of the prior knowledge of the clutter environments is critical to the clutter suppression performance of knowledge-aided space–time adaptive processing (KA-STAP) algorithms in airborne radar applications. In this paper, we propose an enhanced KA-STAP algorithm to estimate the clutter covariance matrix considering inaccurate prior knowledge of the array manifold for airborne radar systems. The core idea of this algorithm is to incorporate prior knowledge about the range of the measured platform velocity and the crab angle, and other radar parameters into the assumed clutter model to obtain increased robustness against inaccuracies of the data. It first over-samples the space–time subspace using prior knowledge about the range values of parameters and the inaccurate array manifold. By selecting the important clutter space–time steering vectors from the over-sampled candidates and computing the corresponding eigenvectors and eigenvalues of the assumed clutter model, we can obtain a more accurate clutter covariance matrix estimate than directly using the prior knowledge of the array manifold. Some extensions of the proposed algorithm with existing techniques are presented and a complexity analysis is conducted. Simulation results illustrate that the proposed algorithms can obtain good clutter suppression performance, even using just one snapshot, and outperform existing KA-STAP algorithms in presence of the errors in the prior knowledge of the array manifold.

How Learning Works: Seven Research-Based Principles for Smart Teaching

How Learning Works: Seven Research-Based Principles for Smart Teaching

Activation of inaccurate prior knowledge affects primary-school students’ metacognitive judgments and calibration

The study investigated whether activation of inaccurate prior knowledge before study contributes to primary-school children’s commission errors and overconfidence in these errors when learning new concepts. Findings indicate that inaccurate prior knowledge affects children’s learning and calibration. The level of children’s judgments of learning for recall responses for which they would not receive credit was inappropriately high after activation of inaccurate prior knowledge.Moreover, results showed that activation of inaccurate prior knowledge was not only detrimental for monitoring judgments during learning, but also for calibration accuracy after test taking. When judging the quality of their recall responses on the posttest, children were more overconfident when they had activated inaccurate prior knowledge. Also, the children often discarded concepts from further study after activation of inaccurate prior knowledge. These results suggest that in order to improve self-regulated learning, it may be important to detect inaccuracies in children’s prior knowledge.

The use of a Bayesian weighted least-squares approach to accelerate empirical engine model generation

An alternative to the commonly used design-of-experiments (DoE) technique based on the use of prior knowledge to speed up the model generation phase of the test programme is presented. Data are gathered in areas where the prior knowledge is least accurate and is blended into an overall empirical model using a Bayesian weighted least-squares (BWLS) approach. The work demonstrates the potential of the BWLS mechanism to reduce the test effort by the incorporation of prior knowledge from neighbouring operating points. Where the prior knowledge is a good representation of the current operating point the number of test points required to allow model convergence may be half that required by a DoE approach. Conversely, the Bayesian technique with inaccurate prior knowledge can still generate an accurate model at the expense of an increased number of test points over a standard experimental design, because the prior will incorrectly influence the model. The algorithm was tested with and without an outlier detection mechanism. This mechanism proved effective in detecting and rejecting simulated outliers and retesting the spurious data point.

A handheld LED coloured-light mixer for students to learn collaboratively the primarycolours of light

To overcome students’ inaccurate prior knowledge on primary additive colours, acoloured-light mixer has been constructed to enable students to observe directly the coloursproduced and reach the conclusion by themselves that the three primary colours of light arered, green, and blue (NOT red, yellow, and blue). Three closely packed tiny light-emittingdiodes (LEDs) producing primary colours are combined with green intensity varyingcircuitry to generate the standard colour-triangle secondary colours and various shadesranging from yellow to orange and pale blue to cyan. In the laboratory, students workedcollaboratively, predicting, observing and explaining, and finally discussing until there wasa consensus.

The Effects of Readers' Misconceptions on Comprehension of Scientific Text.

The aim of the present study was to investigate the effects of readers' misconceptions on text compre- hension. College students with misconceptions in science were asked to read and recall a text that contradicted their misconceptions. Students with no misconceptions served as the control group. Both online (think-aloud, reading times) and offline (recall) measures were obtained. The results suggest that readers' misconceptions often do not affect the online processes themselves but do influence the content of those processes and, consequently, the offline memory representation for the text after reading is completed. Much of the learning that takes place in and out of schools is based on successful comprehension of texts. Readers actively construct a memory representation of the text that critically de- pends on their interpretation in light of prior knowledge. The success of the comprehension process depends on the integration of readers' prior knowledge with textual information (Goldman & Bisanz, 2002; Kintsch, 1988, 1998; van den Broek, Virtue, Ever- son, Tzeng, & Sung, 2002). The powerful effects of readers' prior knowledge in text comprehension have been documented early on (Bartlett, 1932), resulting in a large body of literature indicating that prior knowledge increases memory of texts for both young and adult readers (e.g., Chiesi, Spilich, & Voss, 1979; Dochy, Segers, & Buehl, 1999; Means & Voss, 1985; Recht & Leslie, 1988). The role of inaccurate prior knowledge, however, has received far less attention despite the fact that readers with inaccurate knowledge are the default case rather than the exception (Driver, Squires, Rushworth, & Wood-Robinson, 1994). Our aim in this article is to explore the effects of inaccurate prior knowledge on text compre- hension, focusing both on the final product of reading a text and on the actual processes that take place during reading and lead to this product. To assess the effects of prior knowledge on text comprehension, one must consider both offline products and online processes (Just