Putting perception into action: Inverse optimal control for continuous psychophysics

Psychophysical methods are a cornerstone of psychology, cognitive science, and neuroscience where they have been used to quantify behavior and its neural correlates for a vast range of mental phenomena. Their power derives from the combination of controlled experiments and rigorous analysis through signal detection theory. Unfortunately, they require many tedious trials and preferably highly trained participants. A recently developed approach, continuous psychophysics, promises to transform the field by abandoning the rigid trial structure involving binary responses and replacing it with continuous behavioral adjustments to dynamic stimuli. However, what has precluded wide adoption of this approach is that current analysis methods recover perceptual thresholds, which are one order of magnitude larger compared to equivalent traditional psychophysical experiments. Here we introduce a computational analysis framework for continuous psychophysics based on Bayesian inverse optimal control. We show via simulations and on previously published data that this not only recovers the perceptual thresholds but additionally estimates subjects' action variability, internal behavioral costs, and subjective beliefs about the experimental stimulus dynamics. Taken together, we provide further evidence for the importance of including acting uncertainties, subjective beliefs, and, crucially, the intrinsic costs of behavior, even in experiments seemingly only investigating perception.

Clinical characteristics of Australian osteopaths who teach: A national sample

Background: Health professionals involved in teaching future practitioners have been studied to some extent, but our knowledge of their clinical characteristics is variable. Our study sought to profile the clinical characteristics of osteopaths who teach in the three Australian universities delivering pre-professional osteopathy education.Materials: This study is a secondary analysis of data collected via the Australian Osteopathy Research and Innovation Network (ORION) project. Descriptive statistics were generated for each of the 27-item questionnaire variables. For binary responses, unadjusted odds ratios were calculated, and for continuous variables, independent t-tests were used. Backward step-wise regression modelling was used to identify significant characteristics associated with university teaching in osteopathy. Results: The survey demonstrated 9.9% of Australian osteopaths reported being involved in university teaching. Compared to non-teaching survey respondents, the osteopaths involved in university teaching were more likely to be female (OR 1.56), older (p 0.01) and in clinical practice for longer (p 0.01) but report fewer patient care hours (p 0.01) and patient visits per week (p 0.01). Osteopaths involved in university teaching were also more likely to be involved in research (OR 18.54) and clinical supervision (OR 12.39). They also reported a broader range of patient presentations and therapeutic modalities than their counterparts.Conclusions: This nationally representative survey demonstrates a small percentage of the Australian osteopathy profession are engaged in university teaching. Our secondary analysis has highlighted several characteristics associated with involvement in university teaching that begin to shed light on the composition of the Australian osteopathy teaching workforce. This data may inform development of a skilled and experienced teaching workforce.

HELP-DKT: An Interpretable Cognitive Model of How Students Learn Programming Based on Deep Knowledge Tracing

Student cognitive models are playing an essential role in intelligent online tutoring for programming courses. These models capture students' learning interactions and store them in the form of a set of binary responses, thereby failing to utilize rich educational information in the learning process. Moreover, the recent development of these models has been focused on improving the prediction performance and tended to adopt deep neural networks in building the end-to-end prediction frameworks. Although this approach can provide an improved prediction performance, it may also cause difficulties in interpreting the student's learning status, which is crucial for providing personalized educational feedback. To address this problem, this paper provides an interpretable cognitive model named HELP-DKT, which can infer how students learn programming based on deep knowledge tracing. HELP-DKT has two major advantages. First, it implements a feature-rich input layer, where the raw codes of students are encoded to vector representations, and the error classifications as concept indicators are incorporated. Second, it can infer meaningful estimation of student abilities while reliably predicting future performance. The experiments confirm that HELP-DKT can achieve good prediction performance and present reasonable interpretability of student skills improvement. In practice, HELP-DKT can personalize the learning experience of novice learners.

A Test for Multiple Binary Endpoints with Continuous Latent Distribution in Clinical Trials

In clinical trials, two or more binary responses obtained by dichotomizing continuous responses are often employed as multiple primary endpoints. Testing procedures for multiple binary variables with latent distribution have not yet been adequately discussed. Based on the association measure among latent variables, we provide a statistic for testing the superiority of at least one binary endpoint. In addition, we propose a testing procedure with a framework in which the trial efficacy is confirmed only when there is superiority of at least one endpoint and non-inferiority of the remaining endpoints. The performance of the proposed procedure is evaluated through simulations.

A novel technique of detecting DIF in items using z-score based on IRT model

A novel technique of detecting DIF in items using z-score based on IRT model. A simple technique based on z-score is proposed and derived for Rasch Model and also compared with traditional DIF detection methods. The data set used for application is the State Level Achievement Survey data set from West Bengal, India. Binary responses on 40-item tests of 12518 examinees are taken.

A novel technique of detecting DIF in items using z-score based on IRT model

A novel technique of detecting DIF in items using z-score based on IRT model. A simple technique based on z-score is proposed and derived for Rasch Model and also compared with traditional DIF detection methods. The data set used for application is the State Level Achievement Survey data set from West Bengal, India. Binary responses on 40-item tests of 12518 examinees are taken.

A novel technique of detecting DIF in items using z-score based on IRT model

A novel technique of detecting DIF in items using z-score based on IRT model. A simple technique based on z-score is proposed and derived for Rasch Model and also compared with traditional DIF detection methods. The data set used for application is the State Level Achievement Survey data set from West Bengal, India. Binary responses on 40-item tests of 12518 examinees are taken.