Modeling the Cognitive Task Load and Performance of Naval Operators

Abstract. Operators on naval ships have to act in dynamic, critical and highdemand task environments. For these environments, a cognitive task load (CTL) model has been proposed as foundation of three operator support functions: adaptive task allocation, cognitive aids and resource feedback. This paper presents the construction of such a model as a Bayesian network with probability relationships between CTL and performance. The network is trained and tested with two datasets: operator performance with an adaptive user interface in a lab-setting and operator performance on a high-tech sailing ship. The “Naïve Bayesian network ” tuned out to be the best choice, providing performance estimations with 86 % and 74 % accuracy for respectively the lab and ship data. Overall, the resulting model nicely generalizes over the two datasets. It will be used to estimate operator performance under momentary CTL-conditions, and to set the thresholds of the load-mitigation strategies for the three support functions

Priming to induce paranoid thought in a non clinical population.

Freeman et al. reported that a substantial minority of the general population has paranoid thoughts while exposed in a virtual environment. This suggested that in a development phase of a virtual reality exposure system for paranoid patients initially a non-clinical sample could be used to evaluate the system's ability to induce paranoid thoughts. To increase the efficiency of such an evaluation, this paper takes the position that when appropriately primed a larger group of a non-clinical sample will display paranoid thoughts. A 2-by-2 experiment was conducted with priming for insecurity and vigilance as a withinsubject factor and prior-paranoid thoughts (low or high) as a between-subjects factor. Before exposure into the virtual world, participants (n = 24) were shown a video and read a text about violence or about mountain animals. While exposed, participants were asked to comment freely on their virtual environment. The results of the experiment confirmed that exposure in a virtual environment could induce paranoid thought. In addition, priming with an aim to create a feeling of insecurity and vigilance increased paranoid comments in the non-clinical group that otherwise would less often exhibit ideas of persecutio

Exploring Effectiveness of Explanations for Appropriate Trust: Lessons from Cognitive Psychology

The rapid development of Artificial Intelligence (AI) requires developers and designers of AI systems to focus on the collaboration between humans and machines. AI explanations of system behavior and reasoning are vital for effective collaboration by fostering appropriate trust, ensuring understanding, and addressing issues of fairness and bias. However, various contextual and subjective factors can influence an AI system explanation's effectiveness. This work draws inspiration from findings in cognitive psychology to understand how effective explanations can be designed. We identify four components to which explanation designers can pay special attention: perception, semantics, intent, and user & context. We illustrate the use of these four explanation components with an example of estimating food calories by combining text with visuals, probabilities with exemplars, and intent communication with both user and context in mind. We propose that the significant challenge for effective AI explanations is an additional step between explanation generation using algorithms not producing interpretable explanations and explanation communication. We believe this extra step will benefit from carefully considering the four explanation components outlined in our work, which can positively affect the explanation's effectiveness.Comment: 2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX

How should a virtual agent present psychoeducation?

BACKGROUND AND OBJECTIVE: With the rise of autonomous e-mental health applications, virtual agents can play a major role in improving trustworthiness, therapy outcome and adherence. In these applications, it is important that patients adhere in the sense that they perform the tasks, but also that they adhere to the specific recommendations on how to do them well. One important construct in improving adherence is psychoeducation, information on the why and how of therapeutic interventions. In an e-mental health context, this can be delivered in two different ways: verbally by a (virtual) embodied conversational agent or just via text on the scree

Growing-up hand in hand with robots: Designing and evaluating child-robot interaction from a developmental perspective

Robots are becoming part of children's care, entertainment, education, social assistance and therapy. A steadily growing body of Human-Robot Interaction (HRI) research shows that child-robot interaction (CRI) holds promises to support children's development in novel ways. However, research has shown that technologies that do not take into account children's needs, abilities, interests, and developmental characteristics may have a limited or even negative impact on their physical, cognitive, social, emotional, and moral development. As a result, robotic technology that aims to support children via means of social interaction has to take the developmental perspective into consideration. With this workshop (the third of a series of workshops focusing CRI research), we aim to bring together researchers to discuss how a developmental perspective play a role for smart and natural interaction between robots and children. We invite participants to share their experiences on the challenges of taking the developmental perspective in CRI, such as long-term sustained interactions in the wild, involving children and other stakeholders in the design process and more. Looking across disciplinary boundaries, we hope to stimulate thought-provoking discussions on epistemology, methods, approaches, techniques, interaction scenarios and design principles focused on supporting children's development through interaction with robotic technology. Our goal does not only focus on the conception and formulation of the outcomes in the context of the workshop venue, but also on their establishment and availability for the HRI community in different forms

OligoRAP – an Oligo Re-Annotation Pipeline to improve annotation and estimate target specificity

Background - High throughput gene expression studies using oligonucleotide microarrays depend on the specificity of each oligonucleotide (oligo or probe) for its target gene. However, target specific probes can only be designed when a reference genome of the species at hand were completely sequenced, when this genome were completely annotated and when the genetic variation of the sampled individuals were completely known. Unfortunately there is not a single species for which such a complete data set is available. Therefore, it is important that probe annotation can be updated frequently for optimal interpretation of microarray experiments. Results - In this paper we present OligoRAP, a pipeline to automatically update the annotation of oligo libraries and estimate oligo target specificity. OligoRAP uses a reference genome assembly with Ensembl and Entrez Gene annotation supplemented with a set of unmapped transcripts derived from RefSeq and UniGene to handle assembly gaps. OligoRAP produces alignments of each oligo with the reference assembly as well as with unmapped transcripts. These alignments are re-mapped to the annotation sources, which results in a concise, as complete as possible and up-to-date annotation of the oligo library. The building blocks of this pipeline are BioMoby web services creating a highly modular and distributed system with a robust, remote programmatic interface. OligoRAP was used to update the annotation for a subset of 791 oligos from the ARK-Genomics 20 K chicken array, which were selected as starting material for the oligo annotation session of the EADGENE/SABRE Post-analysis workshop. Based on the updated annotation about one third of these oligos is problematic with regard to target specificity. In addition, the accession numbers or ids the oligos were originally designed for no longer exist in the updated annotation for almost half of the oligos. Conclusion - As microarrays are designed on incomplete data, it is important to update probe annotation and check target specificity regularly. OligoRAP provides both and due to its design based on BioMoby web services it can easily be embedded as an oligo annotation engine in customised applications for microarray data analysis. The dramatic difference in updated annotation and target specificity for the ARK-Genomics 20 K chicken array as compared to the original data emphasises the need for regular updates

Using R in Taverna: RShell v1.2

Background: R is the statistical language commonly used by many life scientists in (omics) data by the open source workflow management system Taverna. However, Taverna had limited support for R, because it supported just a few data types and only a single output. Also, there was no support for graphical output and persistent sessions. Altogether this made using R in Taverna impractical.\ud \ud Findings: We have developed an R plugin for Taverna: RShell, which provides R functionality within workflows designed in Taverna. In order to fully support the R language, our RShell plugin directly uses the R interpreter. The RShell plugin consists of a Taverna processor for R scripts and an RShell Session Manager that communicates with the R server. We made the RShell processor highly configurable allowing the user to define multiple inputs and outputs. Also, various data types are supported, such as strings, numeric data and images. To limit data transport between multiple RShell processors, the RShell plugin also supports persistent sessions. Here, we will describe the architecture of RShell and the new features that are introduced in version 1.2, i.e.: i) Support for R up to and including R version 2.9; ii) Support for persistent sessions to limit data transfer; iii) Support for vector graphics output through PDF; iv) Syntax highlighting of the R code; v) Improved usability through fewer port types. Our new RShell processor is backwards compatible with workflows that use older versions of the RShell processor. We demonstrate the value of the RShell processor by a use-case workflow that maps oligonucleotide probes designed with DNA sequence information from Vega onto the Ensembl genome assembly.\ud \ud Conclusion: Our RShell plugin enables Taverna users to employ R scripts within their workflows in a highly configurable way

Inclusive design: bridging theory and practice

Abstract. Large groups in society lack the necessary skills to be sufficiently self-reliant and are in need of personal assistance. These groups could be supported by information and information technology (ICT), but only if this technology is designed to fit their (cognitive) abilities. Inclusive design theory and methods have already been developed in research contexts, but there is still a gap between theory and practice. There is a need for a practical aid, that helps to create awareness of inclusive design among ICT developers, and offers easy-to-use information and tools to actually apply the methods for diverse target groups. This paper describes the first steps taken towards an inclusive design toolbox for developing ICT applications that offer cognitive support for selfreliance. Dutch ICT companies were interviewed and participated in a co-design workshop, leading to a number of initial needs, user requirements, and an on-line community, that form input for further development of the toolbox