A Behavioral Demonstration of Overconfidence in Judgment

Overprecision—an excessive confidence that one knows the truth—is both the most durable and the least understood form of overconfidence. This article outlines an approach to the study of overprecision that avoids some of the methodological problems of other approaches and better reflects the way uncertainty affects choices in everyday life. We measured the precision in judgment implied by people’s tendency to adjust their point estimates of an uncertain quantity in response to the costs of overestimating or underestimating the correct answer. The results revealed robust overprecision. People adjusted their estimates less than they should have given their actual knowledge, and this effect was driven by their subjective confidence

Learning emotions in virtual environments

A modular hybrid neural network architecture, called SHAME, for emotion learning is introduced. The system learns from annotated data how the emotional state is generated and changes due to internal and external stimuli. Part of the modular architecture is domain independent and part must be\ud adapted to the domain under consideration.\ud The generation and learning of emotions is based on the event appraisal model.\ud The architecture is implemented in a prototype consisting of agents trying to survive in a virtual world. An evaluation of this prototype shows that the architecture is capable of\ud generating natural emotions and furthermore that training of the neural network modules in the architecture is computationally feasible.\ud Keywords: hybrid neural systems, emotions, learning, agents

On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach

We present a map-less path planning algorithm based on Deep Reinforcement Learning (DRL) for mobile robots navigating in unknown environment that only relies on 40-dimensional raw laser data and odometry information. The planner is trained using a reward function shaped based on the online knowledge of the map of the training environment, obtained using grid-based Rao-Blackwellized particle filter, in an attempt to enhance the obstacle awareness of the agent. The agent is trained in a complex simulated environment and evaluated in two unseen ones. We show that the policy trained using the introduced reward function not only outperforms standard reward functions in terms of convergence speed, by a reduction of 36.9\% of the iteration steps, and reduction of the collision samples, but it also drastically improves the behaviour of the agent in unseen environments, respectively by 23\% in a simpler workspace and by 45\% in a more clustered one. Furthermore, the policy trained in the simulation environment can be directly and successfully transferred to the real robot. A video of our experiments can be found at: https://youtu.be/UEV7W6e6Zq

Human-Computer Interaction for BCI Games: Usability and User Experience

Brain-computer interfaces (BCI) come with a lot of issues, such as delays, bad recognition, long training times, and cumbersome hardware. Gamers are a large potential target group for this new interaction modality, but why would healthy subjects want to use it? BCI provides a combination of information and features that no other input modality can offer. But for general acceptance of this technology, usability and user experience will need to be taken into account when designing such systems. This paper discusses the consequences of applying knowledge from Human-Computer Interaction (HCI) to the design of BCI for games. The integration of HCI with BCI is illustrated by research examples and showcases, intended to take this promising technology out of the lab. Future research needs to move beyond feasibility tests, to prove that BCI is also applicable in realistic, real-world settings

Bacteria Hunt: Evaluating multi-paradigm BCI interaction

The multimodal, multi-paradigm brain-computer interfacing (BCI) game Bacteria Hunt was used to evaluate two aspects of BCI interaction in a gaming context. One goal was to examine the effect of feedback on the ability of the user to manipulate his mental state of relaxation. This was done by having one condition in which the subject played the game with real feedback, and another with sham feedback. The feedback did not seem to affect the game experience (such as sense of control and tension) or the objective indicators of relaxation, alpha activity and heart rate. The results are discussed with regard to clinical neurofeedback studies. The second goal was to look into possible interactions between the two BCI paradigms used in the game: steady-state visually-evoked potentials (SSVEP) as an indicator of concentration, and alpha activity as a measure of relaxation. SSVEP stimulation activates the cortex and can thus block the alpha rhythm. Despite this effect, subjects were able to keep their alpha power up, in compliance with the instructed relaxation task. In addition to the main goals, a new SSVEP detection algorithm was developed and evaluated

Lifetime Assessment of Load-Bearing Polymer Glasses: The Influence of Physical Ageing

The timescale at which ductile failure occurs in loaded glassy polymers can be successfully predicted using the engineering approach presented in a previous publication. In this paper the influence of progressive physical ageing on the plastic deformation behaviour of unplasticised poly(vinyl chloride) (uPVC) is characterised and incorporated in the existing approach. With the modification it is possible to quantitatively predict long-term failures which show a so-called endurance limit. The predictions are compared with failure data of uPVC specimens which were subjected to constant or dynamic loads. In dynamic loading conditions a second type of failure mode was observed: fatigue crack growth. A brief study on the influence of the frequency and stress ratio of the applied stress signal shows that crack growth failure is not expected to occur within experimentally reasonable timescales for constant loading conditions

Bacteria Hunt: A multimodal, multiparadigm BCI game

Brain-Computer Interfaces (BCIs) allow users to control applications by brain activity. Among their possible applications for non-disabled people, games are promising candidates. BCIs can enrich game play by the mental and affective state information they contain. During the eNTERFACE’09 workshop we developed the Bacteria Hunt game which can be played by keyboard and BCI, using SSVEP and relative alpha power. We conducted experiments in order to investigate what difference positive vs. negative neurofeedback would have on subjects’ relaxation states and how well the different BCI paradigms can be used together. We observed no significant difference in mean alpha band power, thus relaxation, and in user experience between the games applying positive and negative feedback. We also found that alpha power before SSVEP stimulation was significantly higher than alpha power during SSVEP stimulation indicating that there is some interference between the two BCI paradigms

Complication Prediction after Esophagectomy with Machine Learning

Esophageal cancer can be treated effectively with esophagectomy; however, the postoperative complication rate is high. In this paper, we study to what extent machine learning methods can predict anastomotic leakage and pneumonia up to two days in advance. We use a dataset with 417 patients who underwent esophagectomy between 2011 and 2021. The dataset contains multimodal temporal information, specifically, laboratory results, vital signs, thorax images, and preoperative patient characteristics. The best models scored mean test set AUROCs of 0.87 and 0.82 for leakage 1 and 2 days ahead, respectively. For pneumonia, this was 0.74 and 0.61 for 1 and 2 days ahead, respectively. We conclude that machine learning models can effectively predict anastomotic leakage and pneumonia after esophagectomy

The role of Comprehension in Requirements and Implications for Use Case Descriptions

Within requirements engineering it is generally accepted that in writing specifications (or indeed any requirements phase document), one attempts to produce an artefact which will be simple to comprehend for the user. That is, whether the document is intended for customers to validate requirements, or engineers to understand what the design must deliver, comprehension is an important goal for the author. Indeed, advice on producing ‘readable’ or ‘understandable’ documents is often included in courses on requirements engineering. However, few researchers, particularly within the software engineering domain, have attempted either to define or to understand the nature of comprehension and it’s implications for guidance on the production of quality requirements. Therefore, this paper examines thoroughly the nature of textual comprehension, drawing heavily from research in discourse process, and suggests some implications for requirements (and other) software documentation. In essence, we find that the guidance on writing requirements, often prevalent within software engineering, may be based upon assumptions which are an oversimplification of the nature of comprehension. Hence, the paper examines guidelines which have been proposed, in this case for use case descriptions, and the extent to which they agree with discourse process theory; before suggesting refinements to the guidelines which attempt to utilise lessons learned from our richer understanding of the underlying discourse process theory. For example, we suggest subtly different sets of writing guidelines for the different tasks of requirements, specification and design

Distribution of moisture in reconstructed oil paintings on canvas during absorption and drying: a neutron radiography and NMR study

Moisture is a driving factor in the long-term mechanical deterioration of canvas paintings, as well as for a number of physico–chemical degradation processes. Since the 1990s a number of publications have addressed the equilibrium hygroscopic uptake and the hygro-mechanical deformation of linen canvas, oil paint, animal glue, and ground paint. In order to visualise and quantify the dynamic behaviour of these materials combined in a painting mock-up or reconstruction, we have performed custom-designed experiments with neutron radiography and nuclear magnetic resonance (NMR) imaging. This paper reports how both techniques were used to obtain spatially and temporally resolved information on moisture content, during alternate exposure to high and low relative humidity, or in contact with liquids of varying water activities. We observed how the canvas, which is the dominant component in terms of volumetric moisture uptake, absorbs and dries rapidly, and, due to its low vapour resistance, allows for vapour transfer towards the ground layer. Moisture desorption was generally found to be faster than absorption. The presence of sizing glue leads to a local increase of moisture content. It was observed that lining a painting with an extra canvas results in a damping effect: i.e. absorption and drying are significantly slowed down. The results obtained by NMR are complementary to neutron radiography in that they allow accurate monitoring of water ingress in contact with a liquid reservoir. Quantitative results are in good agreement with adsorption isotherms. The findings can be used for risk analysis of paintings exposed to changing micro-climates or subjected to conservation treatments using water. Future studies addressing moisture-driven deformation of paintings can make use of the proposed experimental techniques